Repository: google-research/proteinfer Branch: master Commit: 0acc7b16eb93 Files: 49 Total size: 38.3 MB Directory structure: gitextract_3w94dd4w/ ├── .github/ │ └── workflows/ │ └── test.yml ├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── baseline_utils.py ├── baseline_utils_test.py ├── colab_evaluation.py ├── colab_evaluation_test.py ├── colabs/ │ ├── Class_Activation_Mapping.ipynb │ ├── Embeddings.ipynb │ ├── Price.ipynb │ ├── README.md │ ├── Random_EC.ipynb │ └── Random_GO.ipynb ├── evaluation.py ├── evaluation_test.py ├── hparams_sets.py ├── inference.py ├── inference_test.py ├── install_models.py ├── misc/ │ └── price_et_al_table_s12.csv ├── parenthood_bin.py ├── parenthood_lib.py ├── parenthood_lib_test.py ├── protein_dataset.py ├── protein_dataset_test.py ├── protein_model.py ├── protein_model_test.py ├── proteinfer.py ├── proteinfer_test.py ├── requirements.txt ├── test_util.py ├── test_util_test.py ├── testdata/ │ ├── blast.tsv │ ├── dev-00000-of-00001.tfrecord │ ├── enzclass.txt │ ├── go.obo │ ├── label_vocab.tsv │ ├── saved_model/ │ │ ├── saved_model.pb │ │ └── variables/ │ │ ├── variables.data-00000-of-00001 │ │ └── variables.index │ ├── test-00000-of-00001.tfrecord │ ├── test_hemoglobin.fasta │ └── train-00000-of-00001.tfrecord ├── train.py ├── train_test.py ├── utils.py └── utils_test.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/test.yml ================================================ name: Testing on: push: branches: [ master ] pull_request: branches: [ master ] jobs: test: runs-on: ubuntu-latest strategy: matrix: python-version: [ 3.7] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip sed 's/tensorflow-gpu/tensorflow/' requirements.txt > requirements.txt.modified pip install -r requirements.txt.modified - name: Test run: | bash -c 'for f in *_test.py; do python3 $f || exit 1; done' ================================================ FILE: .gitignore ================================================ cached_models/** __pycache__/** ================================================ FILE: CONTRIBUTING.md ================================================ This repository exists to provide a record of published work, and so we do not expect to receive pull-requests and do not currently have infrastructure to support these. ## Contributor License Agreement Contributions to this project must be accompanied by a Contributor License Agreement. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ [![GitHub license](https://img.shields.io/badge/license-Apache2-blue.svg)](https://github.com/google-research/nisaba/blob/main/LICENSE) [![Paper](https://img.shields.io/badge/paper-eLife-blue.svg)](https://elifesciences.org/articles/80942) # ProteInfer ProteInfer is an approach for predicting the functional properties of protein sequences using deep neural networks. #### 📝 Read about the method in [our interactive paper](https://google-research.github.io/proteinfer/) (or [in the static version](https://elifesciences.org/articles/80942), published in eLife). ## Usage instructions Go to https://google-research.github.io/proteinfer/ to use an interactive demo in your browser, and read the related paper. Or if you're interested in the command line interface instead, see below. You do not need a Google Cloud machine - these instructions were made as such just to demonstrate what it would take if you had to install everything from scratch. ### Install gcloud on your local machine if you don't have it installed ``` sudo apt install -y google-cloud-sdk gcloud auth login ``` ### Create GCP instance with a GPU ``` gcloud compute instances create proteinfer-gpu --machine-type n1-standard-8 --zone us-west1-b --accelerator type=nvidia-tesla-v100,count=1 --image-family ubuntu-2004-lts --image-project ubuntu-os-cloud --maintenance-policy TERMINATE --boot-disk-size 250 ``` ### ssh into the machine ``` # You may need to wait ~30 seconds for the machine to boot up first. gcloud compute ssh proteinfer-gpu ``` ### Install cuda dependencies for GPU support ``` sudo apt update sudo add-apt-repository ppa:graphics-drivers -y wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb -O /tmp/cuda-keyring_1.0-1_all.deb sudo dpkg -i /tmp/cuda-keyring_1.0-1_all.deb sudo bash -c 'echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda_learn.list' sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub sudo apt update sudo apt install -y cuda-10-0 libcudnn7 ``` ### Install local python virtual environment ``` sudo apt update sudo add-apt-repository ppa:deadsnakes/ppa -y sudo apt install -y python3-venv python3.7 python3-pip python3.7-venv mkdir ~/python_venv cd ~/python_venv python3.7 -m venv proteinfer source ~/python_venv/proteinfer/bin/activate cd ~ ``` ### Get our code from github and install python dependencies (e.g. numpy) ``` git clone https://github.com/google-research/proteinfer cd ~/proteinfer pip3 install -r requirements.txt ``` ### Run our code on test sequences ``` cd ~/proteinfer python3 install_models.py python3 proteinfer.py -i testdata/test_hemoglobin.fasta -o ~/hemoglobin_predictions.tsv # View your predictions. cat ~/hemoglobin_predictions.tsv ``` ### Copy your sequences as a fasta file to the GCP instance ``` # exit the ssh session by typing ctrl D gcloud compute scp proteinfer-gpu:~/ # Then ssh back in again gcloud compute ssh proteinfer-gpu # Then run your inference python3 proteinfer.py -i ~/ -o ~/predictions.tsv ``` ### Delete the instance when you're done to save money ``` gcloud compute instances delete 'proteinfer-gpu' ``` ## Running unit tests ``` bash -c 'for f in *_test.py; do python3 $f || exit 1; done' ``` ================================================ FILE: baseline_utils.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python2, python3 """Utilities for evaluating baseline implementations of function annotation.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import re import typing from typing import Any, Dict, FrozenSet, List, Set, Text import numpy as np import pandas as pd import six from six.moves import zip import tensorflow.compat.v1 as tf import tqdm _BLAST_OUTPUT_COLUMNS = [ 'query_id', 'subject_id', 'percent_seq_identity', 'alignment_length', 'n_mismatches', 'n_gap_opens', 'query_alignment_start', 'query_alignment_end', 'subject_alignment_start', 'subject_alignment_end', 'expected_value', 'bit_score', ] _MERGE_COL_NAME = 'merge_col' # e.g. >accession="A0A0PX123" labels="PF00001,PF00002" # Notice optional fasta header character '>' at the beginning. _SEQUENCE_HEADER_RE = re.compile(r'^>?accession="(\w+)"\tlabels="(.*)"') # Format of query sequence field for interproscan. _INTERPROSCAN_ACCESSION_REGEX = re.compile(r'accession="(\w+)"') def _get_sequence_name_from_sequence_header(s): """Returns sequence name from fasta header. Args: s: Fasta header input string. Format of sequence headers is >accession_"label1,label2". The initial '>' character is optional. """ return _SEQUENCE_HEADER_RE.findall(s)[0][0] # 0th match, 0th capture group. def _get_labels_from_sequence_header(s): r"""Returns labels from fasta header. Args: s: Fasta header input string. Format of sequence headers is >accession_"label1,label2". The initial '>' character is optional. """ # 0th match, 1th capture group. regex_match = _SEQUENCE_HEADER_RE.findall(s)[0][1] if regex_match == '': # pylint: disable=g-explicit-bool-comparison # If the regex match is the empty string, we just want an empty set, as # there are no labels. return set() return set(six.ensure_str(regex_match).split(',')) class BlastResult( typing.NamedTuple('BlastResult', ( ('sequence_name', Text), ('closest_sequence', Text), ('predicted_label', Set[Text]), ('percent_seq_identity', float), ('e_value', float), ('bit_score', float), ))): """Result of parsing BLAST output.""" @staticmethod def from_string(s, ground_truth_lookup): """Constructs a BlastResult from a line in blast tsv output.""" tab_split = six.ensure_str(s).split('\t') query_sequence = six.ensure_str(tab_split[0]).split('"')[1] closest_sequence = six.ensure_str(tab_split[1]).split('"')[1] percent_seq_identity = float(tab_split[2]) e_value = float(tab_split[10]) bit_score = float(tab_split[11]) predicted_label = ground_truth_lookup[closest_sequence] return BlastResult( sequence_name=query_sequence, closest_sequence=closest_sequence, predicted_label=predicted_label, percent_seq_identity=percent_seq_identity, e_value=e_value, bit_score=bit_score) class InterproScanResult( typing.NamedTuple('InterproScanResult', (('sequence_name', Text), ('predicted_label', Set[Text])))): """Result of parsing InterproScan output.""" @staticmethod def from_string(s): tab_split = six.ensure_str(s).rstrip('\n').split('\t') if len(tab_split) < 14 or tab_split[13] == '': # pylint: disable=g-explicit-bool-comparison go_labels = set() else: go_labels = set(six.ensure_str(tab_split[13]).split('|')) return InterproScanResult( sequence_name=_INTERPROSCAN_ACCESSION_REGEX.findall(tab_split[0])[0], predicted_label=go_labels) def load_ground_truth(filename): """Returns dataframe of ground truth from FASTA file. Documentation on how to produce this FASTA file is available at https://docs.google.com/document/d/1uB8J_a1cURIeVNqa5SOJRBBtimI1nOHbU1NbN43Xr0s Args: filename: str. Path to FASTA file. Fasta header input string. Format of sequence headers is like >accession="A0A0PX123" labels="PF00001,PF00002" Returns: pd.DataFrame with columns sequence_name (str), sequence (str), true_label(Set[str]). """ dict_for_making_df = {'sequence_name': [], 'true_label': [], 'sequence': []} with tf.io.gfile.GFile(filename) as f: for line in tqdm.tqdm(f, position=0): line = six.ensure_str(line) if line.startswith('>'): dict_for_making_df['sequence_name'].append( _get_sequence_name_from_sequence_header(line)) dict_for_making_df['true_label'].append( _get_labels_from_sequence_header(line)) else: dict_for_making_df['sequence'].append(line.rstrip()) all_test_seqs = pd.DataFrame(dict_for_making_df) return all_test_seqs def _set_predicted_labels_missing(row): """Adds empty set as predicted labels for a DataFrame row.""" if row[_MERGE_COL_NAME] == 'right_only': return set() else: return row['predicted_labels'] def _fillna(df, column_name, value): """Replacement for df.fillna that works with non-indexable values. https://github.com/pandas-dev/pandas/issues/21329 Args: df: pd.DataFrame with column `column_name`. column_name: str. value: value to set when column's value is NaN. """ df[column_name] = [ (x if isinstance(x, (set, frozenset)) else value) for x in df[column_name] ] def _blast_row_to_confidence_array( row, label_vocab, label_to_vocab_index, ): """Returns a confidence array of preds (predicted labels get bit_score).""" arr = np.zeros_like(label_vocab, np.float32) indexes_to_update = np.array( [label_to_vocab_index[l] for l in row.predicted_label], dtype=np.int32) arr[indexes_to_update] = row.bit_score return arr def load_blast_output(filename, label_vocab, training_data_ground_truth, test_data_ground_truth): """Load a file containing tsv-separated blast output. Args: filename: str. Path to output of blast. label_vocab: np.array of string (labels). Used to compute the output column `predictions`. training_data_ground_truth: pd.DataFrame. The output of `load_ground_truth`. Used to compute predicted labels, by using the labels on the training data for the best match, given by blast. test_data_ground_truth: pd.DataFrame. The output of `load_ground_truth`. Used to compute the true labels. Returns: pd.DataFrame with columns sequence_name (str); true_label (Set[str]); predicted_label (Set[str]); predictions (np.array of length len(label_vocab), filled with the bit score of the closest match). A value of 0 is used for sequences with no blast calls; percent_seq_identity (float, between 0 and 100); e_value (float, a measure of confidence. Lower is more confident.). A value of NaN is used for missing blast calls; bit_score (float, a measure of confidence. Higher is more confident.). A value of 0 is used for sequences with no blast calls. For more information on e-value and bit score, see http://www.metagenomics.wiki/tools/blast/evalue """ training_data_ground_truth_lookup = dict( list( zip(training_data_ground_truth.sequence_name, training_data_ground_truth.true_label))) blast_output_namedtuples = [] with tf.io.gfile.GFile(filename) as f: for line in tqdm.tqdm(f, position=0): blast_output_namedtuples.append( BlastResult.from_string( line, training_data_ground_truth_lookup)) blast_output = pd.DataFrame.from_records( blast_output_namedtuples, columns=BlastResult._fields) # Add in ground truth to get all sequences (including those missed by BLAST). blast_output = blast_output.merge( test_data_ground_truth, on='sequence_name', how='right', indicator=_MERGE_COL_NAME) label_to_vocab_index = {l: i for i, l in enumerate(label_vocab)} # For all sequences for which we don't have predictions, set the predictions # to the empty set. _fillna(blast_output, 'predicted_label', frozenset()) # A bit score of 0 is a "zero confidence" score. blast_output['bit_score'].fillna(0., inplace=True) blast_output['predictions'] = blast_output.apply( axis='columns', func=lambda row: _blast_row_to_confidence_array( # pylint: disable=g-long-lambda row, label_vocab, label_to_vocab_index)) # Clean up output columns. blast_output.drop(inplace=True, columns=[_MERGE_COL_NAME, 'sequence']) return blast_output def _pad_ec_label_with_hyphens(label): """E.g. Given 'EC:1', returns 'EC:1.-.-.-'.""" to_return = label while to_return.count('.') != 3: to_return += '.-' return to_return def limit_set_of_labels(df, acceptable_labels, column_to_limit): """Limits the set of things in df[column_to_limit to acceptable_labels.""" working_df = df.copy() working_df[column_to_limit] = working_df[column_to_limit].apply( acceptable_labels.intersection) return working_df def limit_labels_for_label_normalizer( label_normalizer, acceptable_labels): """Limits keys and values in label_normalizer to acceptable labels.""" limited_label_normalizer = {} for k, v in label_normalizer.items(): if k in acceptable_labels: limited_label_normalizer[k] = sorted( acceptable_labels.intersection(set(v))) return limited_label_normalizer def load_interproscan_output( interproscan_output_filename, test_data_ground_truth, ): """Loads tab-separated output from interproscan. Args: interproscan_output_filename: file path to tsv interproscan output. test_data_ground_truth: pd.DataFrame. The output of `load_ground_truth`. Used to compute the true labels. Returns: pd.DataFrame with output columns 'sequence_name' (Text), predicted_label (frozenset). """ interproscan_output_namedtuples = [] with tf.io.gfile.GFile(interproscan_output_filename) as f: for line in tqdm.tqdm(f, position=0): interproscan_output_namedtuples.append( InterproScanResult.from_string(line)) interproscan_output = pd.DataFrame.from_records( interproscan_output_namedtuples, columns=InterproScanResult._fields) collapsed_interproscan_output_dict = { 'sequence_name': [], 'predicted_label': [] } # There is more than one output per accession because interproscan supports # many analyses of each sequence, and so all the go labels need to be # collapsed. for grouping_key, group in tqdm.tqdm( interproscan_output.groupby('sequence_name'), position=0): collapsed_interproscan_output_dict['sequence_name'].append(grouping_key) collapsed_interproscan_output_dict['predicted_label'].append( frozenset().union(*group.predicted_label.values.tolist())) collapsed_interproscan_output = pd.DataFrame( collapsed_interproscan_output_dict) # Add in ground truth to get all sequences (including those missed by BLAST). interproscan_output = collapsed_interproscan_output.merge( test_data_ground_truth, on='sequence_name', how='right', indicator=_MERGE_COL_NAME) # For all sequences for which we don't have predictions, set the predictions # to the empty set. _fillna(interproscan_output, 'predicted_label', frozenset()) # Clean up output columns. interproscan_output.drop(inplace=True, columns=[_MERGE_COL_NAME, 'sequence']) return interproscan_output ================================================ FILE: baseline_utils_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python2, python3 # pylint: disable=line-too-long """Tests for module model_performance_analysis.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tempfile from absl.testing import absltest from absl.testing import parameterized import numpy as np import pandas as pd import baseline_utils import test_util import tensorflow.compat.v1 as tf def _write_to_file(contents): tmpfile_name = tempfile.mktemp() with tf.io.gfile.GFile(tmpfile_name, "w") as f: f.write(contents.encode("utf-8")) return tmpfile_name class BaselineUtilsTest(parameterized.TestCase): @parameterized.named_parameters( dict( testcase_name="has fasta character >", header='>accession="ACCESSION"\tlabels="label1,label2"', expected="ACCESSION", ), dict( testcase_name="does not have character >", header='accession="ACCESSION"\tlabels="label1,label2"', expected="ACCESSION", ), ) def test_get_sequence_name_from_sequence_header(self, header, expected): actual = baseline_utils._get_sequence_name_from_sequence_header(header) self.assertEqual(actual, expected) @parameterized.named_parameters( dict( testcase_name="two labels", header='>accession="ACCESSION"\tlabels="label1,label2"', expected={"label1", "label2"}, ), dict( testcase_name="zero labels", header='>accession="ACCESSION"\tlabels=""', expected=set(), ), ) def test_get_labels_from_sequence_header(self, header, expected): actual = baseline_utils._get_labels_from_sequence_header(header) self.assertEqual(actual, expected) def test_load_ground_truth(self): input_fasta = ('>accession="ACCESSION"\tlabels="GO:101010,EC:9.9.9.9"\n' "ADE\n" '>accession="ACCESSION2"\tlabels="EC:1.2.-.-"\n' "WWWW\n") tmpfile_name = _write_to_file(input_fasta) actual = baseline_utils.load_ground_truth(tmpfile_name) expected = pd.DataFrame({ "sequence_name": ["ACCESSION", "ACCESSION2"], "true_label": [{"GO:101010", "EC:9.9.9.9"}, {"EC:1.2.-.-"}], "sequence": ["ADE", "WWWW"] }) test_util.assert_dataframes_equal(self, actual, expected) @parameterized.named_parameters( dict( testcase_name="no inputs, one thing in vocab", input_row=pd.Series({ "predicted_label": frozenset([]), "bit_score": 99. }), input_label_vocab=np.array(["PF00001"]), expected=[0.], ), dict( testcase_name="one input, one thing in vocab", input_row=pd.Series({ "predicted_label": frozenset(["PF00001"]), "bit_score": 99. }), input_label_vocab=np.array(["PF00001"]), expected=[99.], ), dict( testcase_name="one input, two things in vocab", input_row=pd.Series({ "predicted_label": frozenset(["PF00001"]), "bit_score": 99. }), input_label_vocab=np.array(["PF00001", "PF99999"]), expected=[99., 0.], ), dict( testcase_name="two inputs, two things in vocab", input_row=pd.Series({ "predicted_label": frozenset(["PF00001", "PF99999"]), "bit_score": 99. }), input_label_vocab=np.array(["PF00001", "PF99999"]), expected=[99., 99.], ), ) def test_blast_row_to_confidence_array(self, input_row, input_label_vocab, expected): lookup = {k: i for i, k in enumerate(input_label_vocab)} actual = baseline_utils._blast_row_to_confidence_array( input_row, input_label_vocab, lookup) np.testing.assert_allclose(actual, expected) def test_load_blast_output(self): input_test_fasta = ( '>accession="ACCESSION"\tlabels="GO:101010,EC:9.9.9.9"\n' "ADE\n" '>accession="ACCESSION2"\tlabels="EC:1.2.-.-"\n' "WWWW\n") test_fasta_filename = _write_to_file(input_test_fasta) ground_truth_test = baseline_utils.load_ground_truth(test_fasta_filename) input_train_fasta = ( '>accession="MATCHACCESSION"\tlabels="GO:101010,EC:9.9.9.9,Pfam:PF12345"\n' "ADE\n") train_fasta_filename = _write_to_file(input_train_fasta) ground_truth_train = baseline_utils.load_ground_truth(train_fasta_filename) # Missing second sequence in ground truth. input_blast = ( 'accession="ACCESSION"\taccession="MATCHACCESSION"\t82.456\t57\t10\t0\t1\t57\t1\t57\t6.92e-21\t79.3\n' ) input_label_vocab = np.array( ["EC:1.2.-.-", "EC:9.9.9.9", "GO:101010", "Pfam:PF12345"]) blast_filename = _write_to_file(input_blast) actual = baseline_utils.load_blast_output( filename=blast_filename, label_vocab=input_label_vocab, test_data_ground_truth=ground_truth_test, training_data_ground_truth=ground_truth_train) expected = pd.DataFrame({ "sequence_name": ["ACCESSION", "ACCESSION2"], "closest_sequence": ["MATCHACCESSION", float("nan")], "true_label": [{"GO:101010", "EC:9.9.9.9"}, {"EC:1.2.-.-"}], "predicted_label": [{"GO:101010", "EC:9.9.9.9", "Pfam:PF12345"}, frozenset()], "percent_seq_identity": [82.456, float("nan")], "e_value": [6.92e-21, float("nan")], "bit_score": [79.3, 0.0], }) test_util.assert_dataframes_equal( self, # Assert dataframes equal except for predictions column. # Rely on unit testing for predictions column instead to increase # test clarity. See test_blast_row_to_confidence_array above. actual.drop(columns=["predictions"]), expected, nan_equals_nan=True) def test_limit_set_of_labels(self): # Set up input data. input_df = pd.DataFrame( {"labels": [frozenset(["a"]), frozenset(["a", "b"])]}) acceptable_labels = frozenset(["a"]) column_to_limit = "labels" # Assert input dataframe was not modified later on, so save a copy. input_df_copy = input_df.copy() # Compute actual. actual = baseline_utils.limit_set_of_labels(input_df, acceptable_labels, column_to_limit) expected = pd.DataFrame({"labels": [frozenset(["a"]), frozenset(["a"])]}) # Test assertions. test_util.assert_dataframes_equal(self, actual, expected) # Assert input dataframe was not modified. test_util.assert_dataframes_equal(self, input_df, input_df_copy) def test_limit_labels_for_label_normalizer(self): input_label_normalizer = { "a": ["a", "b", "c"], "DDDD": ["XXXX"], "b": ["YYYY", "b"] } input_acceptable_labels = frozenset(["a", "b"]) actual = baseline_utils.limit_labels_for_label_normalizer( input_label_normalizer, input_acceptable_labels) expected = {"a": ["a", "b"], "b": ["b"]} self.assertDictEqual(actual, expected) @parameterized.named_parameters( dict( testcase_name="one sequence, one label row, no extra nonlabel entries", interproscan_output="""accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 TIGRFAM TIGR00506 ribB: 3,4-dihydroxy-2-butanone-4-phosphate synthase 13 209 1.5E-86 T 21-10-2019 IPR000422 3,4-dihydroxy-2-butanone 4-phosphate synthase, RibB GO:0008686|GO:0009231""", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={ "B7UIV5": {"GO:0008686", "GO:0009231"} }, ), dict( testcase_name="one sequence, one label row, extra nonlabel entries", interproscan_output="""accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 TIGRFAM TIGR00506 ribB: 3,4-dihydroxy-2-butanone-4-phosphate synthase 13 209 1.5E-86 T 21-10-2019 IPR000422 3,4-dihydroxy-2-butanone 4-phosphate synthase, RibB GO:0008686|GO:0009231 accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 Gene3D G3DSA:3.90.870.10 1 217 5.1E-95T21-10-2019""", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={"B7UIV5": {"GO:0008686", "GO:0009231"}}, ), dict( testcase_name="one sequence, no labels", interproscan_output="""accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 Gene3D G3DSA:3.90.870.10 1 217 5.1E-95T21-10-2019""", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={"B7UIV5": set()}, ), dict( testcase_name="one sequence, multiple label rows, extra nonlabel entries", interproscan_output="""accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 PANTHER PTHR21327:SF38 1 217 8.2E-126 T21-10-2019 accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 TIGRFAM TIGR00506 ribB: 3,4-dihydroxy-2-butanone-4-phosphate synthase 13 209 1.5E-86 T 21-10-2019 IPR000422 3,4-dihydroxy-2-butanone 4-phosphate synthase, RibB GO:0008686|GO:0009231 accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 Hamap MF_00180 3,4-dihydroxy-2-butanone 4-phosphate synthase [ribB]. 11 213 43.238 T 21-10-2019 IPR000422 3,4-dihydroxy-2-butanone 4-phosphate synthase, RibB GO:0008686|GO:0009231 accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 Gene3D G3DSA:3.90.870.10 1 217 5.1E-95T21-10-2019 accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 SUPERFAMILY SSF55821 7 213 5.95E-86T 21-10-2019 IPR017945 DHBP synthase RibB-like alpha/beta domain superfamily accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 Pfam PF00926 3,4-dihydroxy-2-butanone 4-phosphate synthase 17 208 1.7E-82 T 21-10-2019 IPR000422 3,4-dihydroxy-2-butanone 4-phosphate synthase, RibB GO:0008686|GO:0009231 accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 PANTHER PTHR21327 1 217 8.2E-126 T21-10-2019""", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={"B7UIV5": {"GO:0008686", "GO:0009231"}}, ), dict( testcase_name="two sequences, one has labels", interproscan_output="""accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 PANTHER PTHR21327 1 217 8.2E-126 T21-10-2019 accession="Q5SMK6" e9a286a263b71156fcf0cfebc12caec6 360 CDD cd00143 PP2Cc 64 325 6.91138E-87 T 21-10-2019 IPR001932 PPM-type phosphatase domain GO:0003824""", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED >accession="Q5SMK6"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={ "B7UIV5": set(), "Q5SMK6": {"GO:0003824"}, }, ), dict( testcase_name="two sequences, both have labels", interproscan_output="""accession="B7UIV5" 74c763abf8567dfb6f4f83a4e0a31454 217 TIGRFAM TIGR00506 ribB: 3,4-dihydroxy-2-butanone-4-phosphate synthase 13 209 1.5E-86 T 21-10-2019 IPR000422 3,4-dihydroxy-2-butanone 4-phosphate synthase, RibB GO:0008686|GO:0009231 accession="Q5SMK6" e9a286a263b71156fcf0cfebc12caec6 360 CDD cd00143 PP2Cc 64 325 6.91138E-87 T 21-10-2019 IPR001932 PPM-type phosphatase domain GO:0003824""", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED >accession="Q5SMK6"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={ "B7UIV5": {"GO:0008686", "GO:0009231"}, "Q5SMK6": {"GO:0003824"}, }, ), dict( testcase_name="sequence in ground truth that is missing in interproscan output", interproscan_output="", input_ground_truth_test_fasta=""">accession="B7UIV5"\tlabels="GO:101010" NOT_USED""", expected_predicted_labels_per_seq={ "B7UIV5": set(), }, ), ) def test_load_interproscan_output(self, interproscan_output, input_ground_truth_test_fasta, expected_predicted_labels_per_seq): # Set up inputs. input_file = _write_to_file(interproscan_output) input_test_fasta_filename = _write_to_file(input_ground_truth_test_fasta) input_ground_truth_test = baseline_utils.load_ground_truth( input_test_fasta_filename) # Compute actual results. actual_interproscan_output = baseline_utils.load_interproscan_output( test_data_ground_truth=input_ground_truth_test, interproscan_output_filename=input_file) # Assertions. expected_df_length = len( set( list(expected_predicted_labels_per_seq.keys()) + input_ground_truth_test.sequence_name.values)) self.assertLen(actual_interproscan_output, expected_df_length) for row in actual_interproscan_output.itertuples(): self.assertIn(row.sequence_name, expected_predicted_labels_per_seq) self.assertSetEqual(row.predicted_label, expected_predicted_labels_per_seq[row.sequence_name]) if __name__ == "__main__": absltest.main() ================================================ FILE: colab_evaluation.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Version of evaluation utilities intended for use in lower memory environments, such as colab. This version of the evaluation code leverages the fact that the vast majority of example-label predictions are essentially zero, and so only contribute to false negatives. It therefore represents the data in "tidy format" with one row per example-label pair and excludes example-label pairs below a defined threshold. """ import numpy as np import pandas as pd import sklearn import tqdm import inference import utils import evaluation def read_blast_table(filename): """Read a table of BLAST results.""" blast_out = pd.read_table(filename, names=[ 'up_id', 'target', 'pc_identity', 'pc_positives', 'alignment_length', 'mismatches', 'gap_opens', 'q. start', 'q. end', 's. start', 'evalue', 'bit_score' ]) def extract_accession(long_string): """Strip out accession decoration from a parameter""" return long_string.replace('accession="', '').replace('"', '') blast_out['up_id'] = blast_out['up_id'].map(extract_accession) blast_out['target'] = blast_out['target'].map(extract_accession) blast_out = blast_out[[ 'up_id', 'target', 'pc_identity', 'alignment_length', 'bit_score' ]] return blast_out def stats_by_group(df): """Calculate statistics from a groupby'ed dataframe with TPs,FPs and FNs.""" EPSILON = 1e-10 result = df[['tp', 'fp', 'fn']].sum().reset_index().assign( precision=lambda x: (x['tp'] + EPSILON) / (x['tp'] + x['fp'] + EPSILON), recall=lambda x: (x['tp'] + EPSILON) / (x['tp'] + x['fn'] + EPSILON)).assign( f1=lambda x: 2 * x['precision'] * x['recall'] / (x['precision'] + x['recall'] + EPSILON), count=lambda x: x['tp'] + x['fn']) result['proportion'] = result['count'] / np.sum(result['count']) result['proportion_text'] = (result['proportion'] * 100).round(2).astype(str) + "%" return result def get_stats(df): """Calculate statistics from a dataframe with TPs,FPs and FNs.""" df['dummy_group'] = 'all' data = stats_by_group(df.groupby('dummy_group')).drop( columns=['dummy_group', 'proportion', 'proportion_text']) return data def apply_threshold_and_return_stats(predictions_df, ground_truth_df, threshold=0.5, grouping=None): """Given predictions, ground truth and a threshold get statistics.""" calls = assign_tp_fp_fn(predictions_df, ground_truth_df, threshold) if grouping: calls['group'] = calls['label'].map(grouping) return stats_by_group( calls.groupby("group")).assign(threshold=threshold) else: return get_stats(calls).assign(threshold=threshold) def batch_inferences(iterator, batch_size): """Yield batches of seq_ids and predictions matrices from an iterator.""" counter = 0 predictions = [] seq_ids = [] while True: try: inference = next(iterator) except StopIteration: if len(seq_ids) > 0: yield seq_ids, np.vstack(predictions) return seq_ids.append(inference[0]) predictions.append(inference[1]) counter += 1 if counter == batch_size: yield seq_ids, np.vstack(predictions) predictions = [] seq_ids = [] counter = 0 def batched_inferences_from_files(shard_names, batch_size=100): """Iterate through TFRecord files of inferences and yield batches.""" for file_name in tqdm.tqdm(shard_names, position=0): inference_iterator = inference.parse_shard(file_name) batched_iterator = batch_inferences(inference_iterator, batch_size) while True: try: yield next(batched_iterator) except StopIteration: break def batched_inferences_from_dir(shard_dir_path, batch_size=100): """Iterate through directory of inference TFRecord files and yield batches.""" files_to_process = utils.absolute_paths_of_files_in_dir(shard_dir_path) return batched_inferences_from_files(files_to_process, batch_size) def _make_tidy_df_from_seq_names_and_prediction_array( sequence_names, predictions_array, vocab, min_decision_threshold=1e-20): """Given a list of sequences and a matrix of prediction values, yield a tidy dataframe of predictions.""" up_ids = [] labels = [] values = [] for i in range(len(sequence_names)): up_id = sequence_names[i] preds = predictions_array[i, :] for vocab_index in np.argwhere(preds > min_decision_threshold): vocab_index = vocab_index[0] up_ids.append(up_id) labels.append(vocab[vocab_index]) values.append(preds[vocab_index]) return pd.DataFrame({"up_id": up_ids, "label": labels, "value": values}) def get_normalized_inference_results(shard_dir_path, vocab, label_normalizer, min_decision_threshold=1e-20): """Take a directory of sharded inferences and output a tidy and normalized dataframe. Inferences are in the format defined in inference.py Args: shard_dir_path: directory of TFrecord inference shards vocab: a list of vocabulary items label_normalizer: a dictionary mapping vocabulary items to their parents min_decision_threshold: a threshold reflecting the minimum we will ever be able to use to call a positive in subsequent analysis. Higher values use less RAM at the expense of lower maximum sensitivity. Returns: A pandas dataframe with one row per example-label (provided value > min_decision_threshold) and the associated value from the neural network. """ batches = batched_inferences_from_dir(shard_dir_path) dfs = [] for seq_names, confidences in batches: normed_confidences = evaluation.normalize_confidences( confidences, vocab, label_normalizer) dfs.append( _make_tidy_df_from_seq_names_and_prediction_array( seq_names, normed_confidences, vocab, min_decision_threshold=min_decision_threshold)) return pd.concat(dfs) def make_tidy_df_from_ground_truth(ground_truth): """Create a tidy dataframe from ground truth data.""" up_ids = [] labels = [] for i in tqdm.tqdm(ground_truth.index, position=0): up_id = ground_truth['sequence_name'][i] for vocab_entry in ground_truth['true_label'][i]: up_ids.append(up_id) labels.append(vocab_entry) return pd.DataFrame({"up_id": up_ids, "label": labels, "gt": True}) def merge_predictions_and_ground_truth(predictions_df, ground_truth_df): """Perform an outer join of predictions and ground truth, then set all empty values to False.""" combined = predictions_df.merge(ground_truth_df, how="outer", suffixes=("_pred", "_gt"), left_on=["label", "up_id"], right_on=["label", "up_id"]) combined = combined.fillna(False) return combined def get_pr_curve_df(predictions_df, ground_truth_df, grouping=None, filtered=True): """Given predictions and ground truth in tidy format, yield a precision recall curve. Args: predictions_df: predictions in tidy format ground_truth_df: ground truth in tidy format grouping: optional dictionary mapping sequence names to categories filtered: whether to remove almost redundant points on PR curve """ combined = merge_predictions_and_ground_truth(predictions_df, ground_truth_df) if grouping == None: to_process = {'all': combined}.items() else: combined['group'] = combined['label'].map(grouping) to_process = combined.groupby('group') del combined output_dfs = [] for group_name, group in tqdm.tqdm(to_process, position=0): precisions, recalls, thresholds = sklearn.metrics.precision_recall_curve( group['gt'], group['value']) precisions = precisions[:-1] recalls = recalls[:-1] if filtered: precisions, recalls, thresholds = filter_pr_curve( precisions, recalls, thresholds) output_dfs.append( pd.DataFrame({ 'group': group_name, 'precision': precisions, 'recall': recalls, 'threshold': thresholds, 'f1': 2 * precisions * recalls / (precisions + recalls) })) return pd.concat(output_dfs) def filter_pr_curve(precisions, recalls, thresholds, resolution=1e-4): """Filters out imperceptible shifts in a PR curve.""" last_precision = None last_recall = None new_precisions = [] new_recalls = [] new_thresholds = [] for i in range(len(precisions)): if last_precision is None or abs(recalls[i] - last_recall) >= resolution: new_precisions.append(precisions[i]) last_precision = precisions[i] new_recalls.append(recalls[i]) last_recall = recalls[i] new_thresholds.append(thresholds[i]) return np.array(new_precisions), np.array(new_recalls), np.array( new_thresholds) def assign_tp_fp_fn(predictions_df, ground_truth_df, threshold): """Return a new predictions dataframe where each row is assigned as either a TP, FP or FN.""" combined = merge_predictions_and_ground_truth(predictions_df, ground_truth_df) combined['tp'] = (combined['gt'] == True) & (combined['value'] > threshold) combined['fp'] = (combined['gt'] == False) & (combined['value'] > threshold) combined['fn'] = (combined['gt'] == True) & (combined['value'] < threshold) return combined ================================================ FILE: colab_evaluation_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for module colab_evaluation.py.""" import gzip import os import time import math from absl import flags from absl.testing import absltest from absl.testing import parameterized import numpy as np import pandas as pd import colab_evaluation import inference import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS class ColabEvaluationTest(parameterized.TestCase): def _generate_random_inferences(self, n): serialized_inferences = [] accessions_list = [] activations_list = [] for _ in range(n): accession = f"ACCESSION_{time.time()}" activations = np.random.rand(100) accessions_list.append(accession) activations_list.append(activations) serialized_inferences.append( inference.serialize_inference_result(accession, activations)) return serialized_inferences, accessions_list, activations_list @parameterized.parameters([{'batch_size': 1}, {'batch_size': 9}]) def test_batched_inferences_from_dir(self, batch_size, num_examples=100): # Create input inference results. serialized_inferences, accessions_list, activations_list = self._generate_random_inferences( num_examples) shard_1_contents = b"\n".join(serialized_inferences[0:60]) shard_2_contents = b"\n".join(serialized_inferences[60:]) shard_dir = self.create_tempdir() shard_1_filename = shard_dir.create_file('shard_1').full_path shard_2_filename = shard_dir.create_file('shard_2').full_path # Write contents to a gzipped file. with tf.io.gfile.GFile(shard_1_filename, 'wb') as f: with gzip.GzipFile(fileobj=f, mode='wb') as f_gz: f_gz.write(shard_1_contents) with tf.io.gfile.GFile(shard_2_filename, 'wb') as f: with gzip.GzipFile(fileobj=f, mode='wb') as f_gz: f_gz.write(shard_2_contents) # Read these shards. iterator = colab_evaluation.batched_inferences_from_dir( shard_dir.full_path, batch_size=batch_size) actual = list(iterator) # Check output. self.assertEqual(len(actual), math.ceil(num_examples / batch_size)) self.assertEqual(actual[0][0][0], accessions_list[0]) if batch_size > 1: self.assertEqual(actual[1][0][1], accessions_list[batch_size + 1]) np.testing.assert_equal(actual[0][1][0], activations_list[0]) if batch_size > 1: np.testing.assert_equal(actual[1][1][1], activations_list[batch_size + 1]) def test_make_tidy_df_from_seq_names_and_prediction_array(self): vocab = ["ENTRY0", "ENTRY1", "ENTRY2"] sequence_names = ['SEQ0', 'SEQ1'] predictions_array = np.array([[0.1, 0.9, 0.5], [1, 1, 1]]) min_decision_threshold = 0.4 actual_df = colab_evaluation._make_tidy_df_from_seq_names_and_prediction_array( sequence_names, predictions_array, vocab, min_decision_threshold=min_decision_threshold) expected_df = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ0', 'SEQ1', 'SEQ1', 'SEQ1'], 'label': ['ENTRY1', 'ENTRY2', 'ENTRY0', 'ENTRY1', 'ENTRY2'], 'value': [0.9, 0.5, 1.0, 1.0, 1.0] }) pd.testing.assert_frame_equal(actual_df, expected_df) def test_make_tidy_df_from_ground_truth(self): input_df = pd.DataFrame({ 'sequence_name': ['SEQ0', 'SEQ1', 'SEQ2', 'SEQ3'], 'true_label': [['ENTRY1'], ['ENTRY1', 'ENTRY2'], [], ['ENTRY6']] }) actual_df = colab_evaluation.make_tidy_df_from_ground_truth(input_df) expected_df = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ1', 'SEQ1', 'SEQ3'], 'label': ['ENTRY1', 'ENTRY1', 'ENTRY2', 'ENTRY6'], 'gt': [True, True, True, True] }) pd.testing.assert_frame_equal(actual_df, expected_df) def test_merge_predictions_and_ground_truth(self): pred = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ0', 'SEQ1', 'SEQ1', 'SEQ1'], 'label': ['ENTRY1', 'ENTRY2', 'ENTRY0', 'ENTRY1', 'ENTRY2'], 'value': [0.9, 0.5, 1.0, 1.0, 1.0] }) gt = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ1', 'SEQ1', 'SEQ3'], 'label': ['ENTRY1', 'ENTRY1', 'ENTRY2', 'ENTRY6'], 'gt': [True, True, True, True] }) actual_df = colab_evaluation.merge_predictions_and_ground_truth( pred, gt) expected_df = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ0', 'SEQ1', 'SEQ1', 'SEQ1', 'SEQ3'], 'label': ['ENTRY1', 'ENTRY2', 'ENTRY0', 'ENTRY1', 'ENTRY2', 'ENTRY6'], 'value': [0.9, 0.5, 1.0, 1.0, 1.0, False], 'gt': [True, False, False, True, True, True] }) pd.testing.assert_frame_equal(actual_df, expected_df) def test_get_pr_curve_df(self): pred = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ0', 'SEQ1', 'SEQ1', 'SEQ1'], 'label': ['ENTRY1', 'ENTRY2', 'ENTRY0', 'ENTRY1', 'ENTRY2'], 'value': [0.9, 0.5, 1.0, 1.0, 1.0] }) gt = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ1', 'SEQ1', 'SEQ3'], 'label': ['ENTRY1', 'ENTRY1', 'ENTRY2', 'ENTRY6'], 'gt': [True, True, True, True] }) pr_curve = colab_evaluation.get_pr_curve_df(pred, gt, filtered=False) np.testing.assert_almost_equal(pr_curve['recall'], np.array([1, 0.75, 0.75, .5])) np.testing.assert_almost_equal( pr_curve['precision'], np.array([0.6666667, 0.6, 0.75, 0.6666667])) np.testing.assert_almost_equal( pr_curve['f1'], np.array([0.8, 0.6666667, 0.75, 0.5714286])) def test_assign_tp_fp_fn(self): pred = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ0', 'SEQ1', 'SEQ1', 'SEQ1'], 'label': ['ENTRY1', 'ENTRY2', 'ENTRY0', 'ENTRY1', 'ENTRY2'], 'value': [0.9, 0.5, 1.0, 1.0, 1.0] }) gt = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ1', 'SEQ1', 'SEQ3'], 'label': ['ENTRY1', 'ENTRY1', 'ENTRY2', 'ENTRY6'], 'gt': [True, True, True, True] }) tp_fp_fn = colab_evaluation.assign_tp_fp_fn(pred, gt, threshold=0.5) expected = pd.DataFrame({ 'tp': [True, False, False, True, True, False], 'fp': [False, False, True, False, False, False], 'fn': [False, False, False, False, False, True] }) actual = tp_fp_fn.loc[:, ["tp", "fp", "fn"]] pd.testing.assert_frame_equal(expected, actual) def test_apply_threshold_and_return_stats(self): pred = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ0', 'SEQ1', 'SEQ1', 'SEQ1'], 'label': ['ENTRY1', 'ENTRY2', 'ENTRY0', 'ENTRY1', 'ENTRY2'], 'value': [0.9, 0.5, 1.0, 1.0, 1.0] }) gt = pd.DataFrame({ 'up_id': ['SEQ0', 'SEQ1', 'SEQ1', 'SEQ3'], 'label': ['ENTRY1', 'ENTRY1', 'ENTRY2', 'ENTRY6'], 'gt': [True, True, True, True] }) actual = colab_evaluation.apply_threshold_and_return_stats(pred,gt,grouping = {"ENTRY0":'A',"ENTRY1":'A',"ENTRY2":'A',"ENTRY6":'A'}) expected = pd.DataFrame({ 'group': ['A'], 'tp': [3.0], 'fp': [1.0], 'fn': [1.0], 'precision': [0.75], 'recall': [0.75], 'f1': [0.75], 'count': [4.0], 'proportion': [1.0], 'proportion_text': ['100.0%'], 'threshold': [0.5] }) pd.testing.assert_frame_equal(actual,expected, check_dtype=False) def test_read_blast_table(self): actual = colab_evaluation.read_blast_table("testdata/blast.tsv") expected = pd.DataFrame({'up_id': ['ABC'], 'target': ['DEF'], 'pc_identity': [50], 'alignment_length': [100], 'bit_score': [500]}) pd.testing.assert_frame_equal(actual, expected) if __name__ == '__main__': absltest.main() ================================================ FILE: colabs/Class_Activation_Mapping.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 2, "metadata": { "accelerator": "GPU", "colab": { "name": "Copy of Proteinfer CAM v3", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "code", "execution_count": null, "source": [ "# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n", "# you may not use this file except in compliance with the License.\r\n", "# You may obtain a copy of the License at\r\n", "#\r\n", "# https://www.apache.org/licenses/LICENSE-2.0\r\n", "#\r\n", "# Unless required by applicable law or agreed to in writing, software\r\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n", "# See the License for the specific language governing permissions and\r\n", "# limitations under the License." ], "outputs": [], "metadata": { "id": "WkkXLGBrycfx" } }, { "cell_type": "markdown", "source": [ "# ProteInfer Class Activation Mapping (CAM)\n", "\n" ], "metadata": { "id": "XdR-yrNv9Fgk" } }, { "cell_type": "markdown", "source": [ "\n", "## Initial setup (code/data download)" ], "metadata": { "id": "Rqbcum1HM8aL" } }, { "cell_type": "code", "execution_count": null, "source": [ "!git clone https://github.com/google-research/proteinfer \r\n", "\r\n", "%cd proteinfer\r\n", "!pip3 install -qr requirements.txt\r\n", "\r\n", "import pandas as pd\r\n", "import tensorflow\r\n", "import inference\r\n", "import parenthood_lib\r\n", "import baseline_utils,subprocess\r\n", "import shlex\r\n", "import tqdm \r\n", "import sklearn\r\n", "import numpy as np\r\n", "import utils\r\n", "import colab_evaluation\r\n", "import plotly.express as px\r\n", "import seaborn as sns\r\n", "\r\n", "from plotnine import ggplot, geom_point, geom_point, geom_line, aes, stat_smooth, facet_wrap, xlim,coord_cartesian,theme_bw,labs,ggsave\r\n" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'proteinfer'...\n", "remote: Enumerating objects: 491, done.\u001b[K\n", "remote: Counting objects: 100% (214/214), done.\u001b[K\n", "remote: Compressing objects: 100% (177/177), done.\u001b[K\n", "remote: Total 491 (delta 96), reused 49 (delta 20), pack-reused 277\u001b[K\n", "Receiving objects: 100% (491/491), 11.55 MiB | 8.51 MiB/s, done.\n", "Resolving deltas: 100% (235/235), done.\n", "/content/proteinfer\n", "\u001b[K |████████████████████████████████| 99 kB 3.4 MB/s \n", "\u001b[K |████████████████████████████████| 2.3 MB 30.7 MB/s \n", "\u001b[K |████████████████████████████████| 10.8 MB 26.8 MB/s \n", "\u001b[K |████████████████████████████████| 2.8 MB 29.6 MB/s \n", "\u001b[K |████████████████████████████████| 50 kB 6.4 MB/s \n", "\u001b[K |████████████████████████████████| 59 kB 6.2 MB/s \n", "\u001b[K |████████████████████████████████| 89 kB 7.5 MB/s \n", "\u001b[K |████████████████████████████████| 56 kB 4.8 MB/s \n", "\u001b[K |████████████████████████████████| 17.3 MB 48 kB/s \n", "\u001b[K |████████████████████████████████| 10.5 MB 34.2 MB/s \n", "\u001b[K |████████████████████████████████| 107 kB 50.4 MB/s \n", "\u001b[K |████████████████████████████████| 13.1 MB 35.7 MB/s \n", "\u001b[K |████████████████████████████████| 4.4 MB 33.5 MB/s \n", "\u001b[K |████████████████████████████████| 226 kB 48.2 MB/s \n", "\u001b[K |████████████████████████████████| 6.8 MB 35.5 MB/s \n", "\u001b[K |████████████████████████████████| 110.5 MB 50 kB/s \n", "\u001b[K |████████████████████████████████| 411.0 MB 22 kB/s \n", "\u001b[K |████████████████████████████████| 503 kB 47.9 MB/s \n", "\u001b[K |████████████████████████████████| 103 kB 54.1 MB/s \n", "\u001b[K |████████████████████████████████| 45 kB 3.1 MB/s \n", "\u001b[K |████████████████████████████████| 328 kB 42.4 MB/s \n", "\u001b[K |████████████████████████████████| 63 kB 1.6 MB/s \n", "\u001b[K |████████████████████████████████| 9.5 MB 38.7 MB/s \n", "\u001b[K |████████████████████████████████| 3.8 MB 38.5 MB/s \n", "\u001b[?25h Building wheel for absl-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "xarray 0.18.2 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "tensorflow-probability 0.13.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\n", "tensorflow-metadata 1.2.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.7.1 which is incompatible.\n", "spacy 2.2.4 requires tqdm<5.0.0,>=4.38.0, but you have tqdm 4.28.1 which is incompatible.\n", "pyerfa 2.0.0 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "pyarrow 3.0.0 requires numpy>=1.16.6, but you have numpy 1.16.2 which is incompatible.\n", "panel 0.12.1 requires tqdm>=4.48.0, but you have tqdm 4.28.1 which is incompatible.\n", "kapre 0.3.5 requires numpy>=1.18.5, but you have numpy 1.16.2 which is incompatible.\n", "kapre 0.3.5 requires tensorflow>=2.0.0, but you have tensorflow 1.15.4 which is incompatible.\n", "jaxlib 0.1.70+cuda110 requires numpy>=1.18, but you have numpy 1.16.2 which is incompatible.\n", "jax 0.2.19 requires numpy>=1.18, but you have numpy 1.16.2 which is incompatible.\n", "google-colab 1.0.0 requires astor~=0.8.1, but you have astor 0.7.1 which is incompatible.\n", "google-colab 1.0.0 requires six~=1.15.0, but you have six 1.12.0 which is incompatible.\n", "google-api-python-client 1.12.8 requires six<2dev,>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "google-api-core 1.26.3 requires six>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "fbprophet 0.7.1 requires tqdm>=4.36.1, but you have tqdm 4.28.1 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", "cupy-cuda101 9.1.0 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "astropy 4.3.1 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n" ] } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UbK8U6SEKqUU", "outputId": "b41adc0f-c74e-4a26-fd34-93ad42ee4989" } }, { "cell_type": "code", "execution_count": null, "source": [ "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\r\n", "!tar xzf noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\r\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz\r\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_EC/eval_test.fasta\r\n", "\r\n" ], "outputs": [], "metadata": { "id": "T7OLyg9DF3uw" } }, { "cell_type": "code", "execution_count": null, "source": [ "def get_ec_num_mapping():\r\n", " tree = ET.parse('enzyme-data.xml')\r\n", " root = tree.getroot()\r\n", " rows = root[0][3].findall('row')\r\n", " rows = root.findall(\".//field[@name='accepted_name']..\")\r\n", " ec_nums = {}\r\n", " for row in rows:\r\n", " ec_num = row.find(\".//*[@name='ec_num']\").text\r\n", " name = row.find(\".//*[@name='accepted_name']\").text\r\n", " try:\r\n", " ec_nums[ec_num]=name\r\n", " except TypeError:\r\n", " continue\r\n", " return ec_nums\r\n", "\r\n", "def download_dataset():\r\n", " total = 13\r\n", " file_shard_names = ['https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-{:05d}-of-{:05d}.tfrecord'.format(i,total) for i in range(total)]\r\n", "\r\n", " for shard_name in tqdm.tqdm(file_shard_names, position=0,desc=\"Downloading\"):\r\n", " subprocess.check_output(shlex.split(f'wget {shard_name}'))\r\n", " return " ], "outputs": [], "metadata": { "id": "ERnBaCG_O00k" } }, { "cell_type": "code", "execution_count": null, "source": [ "\r\n", "import matplotlib as mpl\r\n", "mpl.rcParams['figure.dpi'] = 100\r\n", "!wget -qN https://www.enzyme-database.org/downloads/enzyme-data.xml.gz\r\n", "!gunzip -f enzyme-data.xml.gz\r\n", "\r\n", "\r\n", "import xml.etree.ElementTree as ET\r\n", "\r\n", "\r\n", "ec_nums = get_ec_num_mapping()\r\n", "download_dataset()" ], "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Downloading: 100%|██████████| 13/13 [00:04<00:00, 2.02it/s]\n" ] } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FrEsBu1CA7Ut", "outputId": "c4117fd4-ff22-4e67-cde9-55e7ddc20721" } }, { "cell_type": "markdown", "source": [ "##Read in the test dataset" ], "metadata": { "id": "KWu0UovMMzEC" } }, { "cell_type": "code", "execution_count": null, "source": [ "import protein_dataset\r\n", "import tqdm\r\n", "import numpy as np\r\n", "sequence_iterator = protein_dataset.yield_examples(\"./test*.tfrecord\")\r\n", "sequences = []\r\n", "labels = []\r\n", "ids = []\r\n", "for example in tqdm.tqdm(sequence_iterator):\r\n", " ids.append(example[protein_dataset.SEQUENCE_ID_KEY])\r\n", " sequences.append(example[protein_dataset.SEQUENCE_KEY])\r\n", " labels.append(example[protein_dataset.LABEL_KEY])\r\n", "\r\n", "# If we want to optimise for inference speed we should sort the dataset by\r\n", "# sequence length:\r\n", "seq_lengths = [len(x) for x in sequences]\r\n", "indices = np.argsort(-np.array(seq_lengths)).tolist()\r\n", "\r\n", "ids = [ids[indices[x]] for x in range(len(indices))]\r\n", "sequences = [sequences[indices[x]] for x in range(len(indices))]\r\n", "labels = [set(labels[indices[x]]) for x in range(len(indices))]" ], "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\r0it [00:00, ?it/s]WARNING: Logging before flag parsing goes to stderr.\n", "W0909 12:36:54.501479 140666907211648 deprecation.py:323] From /content/proteinfer/protein_dataset.py:290: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.\n", "54285it [00:27, 1953.67it/s]\n" ] } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RlyjYvhL9W8t", "outputId": "4db10071-5e40-41c9-bea3-2b2a7167ceaa" } }, { "cell_type": "markdown", "source": [ "## Load the saved model" ], "metadata": { "id": "8pScRsRCwX2H" } }, { "cell_type": "code", "execution_count": null, "source": [ "inferrer = inference.Inferrer(\r\n", " 'noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140',use_tqdm= True, batch_size=32,activation_type=\"representation\"\r\n", ")\r\n", "\r\n", "label_vocab = list(inferrer.get_variable('label_vocab:0').astype(str))\r\n", "label_normalizer = parenthood_lib.get_applicable_label_dict(\r\n", " 'parenthood.json.gz')\r\n", "\r\n", "\r\n", "kernel = inferrer.get_variable(\"logits/kernel/read:0\")\r\n", "\r\n" ], "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "W0909 12:37:24.753336 140666907211648 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/ragged/ragged_tensor.py:1586: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] } ], "metadata": { "id": "zljPgF2BMBj-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7cc5050c-877f-4bf0-91a2-d6638a2f4997" } }, { "cell_type": "code", "execution_count": null, "source": [ "def get_multi_full_ec(labels, desired_number=3):\r\n", " subset = [x for x in labels if x.startswith(b\"EC:\")]\r\n", " subset = [x for x in subset if b'-' not in x]\r\n", " if len(subset)==desired_number:\r\n", " return subset\r\n", " else:\r\n", " return []" ], "outputs": [], "metadata": { "id": "EoIhtq-6f69y" } }, { "cell_type": "code", "execution_count": null, "source": [ "def moving_average(a, n=3) :\r\n", " new = np.zeros_like(a)\r\n", " length_dim = a.shape[0]\r\n", " for i in range(length_dim):\r\n", " new[i,:]=np.mean(a[np.maximum(i-n,0):np.minimum(i+n,length_dim),:],axis=0)\r\n", " return new\r\n" ], "outputs": [], "metadata": { "id": "W9h1rwNQk2yE" } }, { "cell_type": "code", "execution_count": null, "source": [ "from matplotlib import colors as clr\r\n", "from matplotlib import pyplot as plt\r\n", "palette = clr.LinearSegmentedColormap.from_list('custom blue', ['#FFFFFF','#EEEEEE','#00EE00'], N=256)" ], "outputs": [], "metadata": { "id": "yxJF7xQYG13_" } }, { "cell_type": "code", "execution_count": null, "source": [ "items_that_satisfy_criteria = []\r\n", "for i in range(len(sequences)):\r\n", " new_lab = get_multi_full_ec(labels[i],desired_number=2)\r\n", " new_seq = sequences[i]\r\n", " new_id = ids[i]\r\n", " if len(new_lab)>0:\r\n", " items_that_satisfy_criteria.append({'labels':new_lab, 'sequence':new_seq, 'id':new_id})\r\n" ], "outputs": [], "metadata": { "id": "pFaTvOjAR0Il" } }, { "cell_type": "markdown", "source": [ "## Perform inference" ], "metadata": { "id": "FhGd6pdzodrP" } }, { "cell_type": "code", "execution_count": null, "source": [ "counter = 0\r\n", "\r\n", "sns.set_style(\"whitegrid\")\r\n", "sns.set(rc={'figure.figsize': (15, 35)})\r\n", "\r\n", "one_by_one = False\r\n", "ids = ['Q4LB35', 'Q54QE4', 'P54889', 'Q9PLG1', 'O94632', 'P19835', 'Q3MEJ8']\r\n", "if True:\r\n", " sns.set(rc={'figure.figsize': (15, 2)})\r\n", "\r\n", "for item in items_that_satisfy_criteria:\r\n", " if item['id'].decode() in ids or one_by_one:\r\n", "\r\n", " the_labels = item['labels']\r\n", " representation = inferrer.get_activations(\r\n", " [item['sequence'].decode(\"utf-8\")])\r\n", " label_ids = [label_vocab.index(x.decode('utf-8')) for x in the_labels]\r\n", " contributions = np.matmul(representation.squeeze(),\r\n", " kernel)[:, label_ids]\r\n", "\r\n", " sum_contributions = contributions.sum(axis=1)\r\n", " contributions = np.maximum(contributions, 0)\r\n", " contributions = moving_average(contributions, 80)\r\n", " contributions = contributions / contributions.max(axis=0,\r\n", " keepdims=True)\r\n", "\r\n", " df = pd.DataFrame(contributions.T)\r\n", " try:\r\n", " df.index = [\r\n", " ec_nums[x.decode().replace(\"EC:\", \"\")].replace(\r\n", " \"\",\r\n", " \"\").replace(\"\",\r\n", " \"\").replace(\"\",\r\n", " \"\").replace(\"\", \"\")\r\n", " for x in the_labels\r\n", " ]\r\n", " except KeyError:\r\n", " continue\r\n", " print(item['id'].decode())\r\n", " ax = None\r\n", " if False:\r\n", " ax = axes[counter]\r\n", " try:\r\n", " g = sns.heatmap(df, cmap=palette, xticklabels=500, ax=ax, vmin=0)\r\n", " counter += 1\r\n", " g.set_title(item['id'].decode())\r\n", " for _, spine in g.spines.items():\r\n", " spine.set_visible(True)\r\n", " plt.yticks(rotation=0)\r\n", " plt.xticks(rotation=0)\r\n", " plt.subplots_adjust(hspace=0.7)\r\n", " except ValueError:\r\n", " continue\r\n", "\r\n", " if True:\r\n", " plt.show()\r\n", "\r\n", "plt.show()\r\n" ], "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Annotating batches of sequences: 100%|██████████| 1/1 [00:01<00:00, 1.06s/it]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Q54QE4\n" ] }, { "output_type": "display_data", "data": { "image/png": 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WTq9xQfL19VWpUqV0/vx5I+90fvHz85Orq6u++OILmx/cpP3BQX5c84I8DgAAAAAAcG9o3ry5li5dapNbOiIiQiaTSU2aNMmy78GDBzVu3Dh17do109/EzsiOHTskKdOFxpnJVVC6adOmql+/vkaOHKmgoCD5+fnp8uXLWrFihUwmk81qR4sTJ04oJSVFiYmJOn/+vLZv366vv/5affr0sUmUnZ/c3d3Vt29fm1QdWWncuLHWrl2r9evXy8fHR1u3btXZs2dt2lSqVEk9evTQO++8o5iYGAUEBCghIUF79+7VqFGjVLJkSeOnCIMGDVK3bt308MMP66+//tK3336r+vXrGw/Q8/X11e7du1W/fn0VKlRIlSpVkru7u6TbuZuLFSumw4cPa8GCBfl7YtJo3769kaaiUqVK2rp1a7qH7mWmdu3aKly4sGbNmqWhQ4fq0qVLCg0NzTQAnFtFixZVjRo1tHz5cnl7e8vJyUnLli2Tu7u7rl69arTz8fGRo6OjPvnkEzk5OcnR0VE1atTQs88+qw0bNqhv374aOHCgfHx8FBsbq+PHjyspKUnjx4+Xo6Ojxo8fr2nTpmn48OHq0qWLnJ2d9d133+nXX39VyZIltWHDBnXu3FmlS5e+o+Px8vJSjx49tHz5crm5ual69erasWOHzp07Z9OuSZMmWrNmjV577TW1bdtWhw8f1meffWbTZu/evfr444/Vpk0blSlTRn/99ZfWr1+vunXr5jog3adPH0VGRqYLBJ87d04RERE2ZSaTSe3atctwnMaNGys4OFhLliwxHiaYNm9yWjm9xgXJwcFBkydP1oQJExQfH6+WLVuqUKFC+uOPP7Rv3z6NHTs203zY2SlWrJh69uyppUuXytXVVdWqVVNERISRc92yKjq/rnlBHQcAAAAAAMh/WeWUzqsePXpo3bp1CgoK0rBhw3Tp0iWFhISoR48eNjG7fv366Y8//tCuXbsk3c4YERQUJB8fH3Xq1ElHjhwx2np7e6tChQqSpAkTJqhixYqqXr268aDD1atXq02bNgUblDaZTAoLC9OiRYu0atUqXb58We7u7mrUqJFCQ0Mz/BVxy2poNzc3PfTQQ6pVq5ZWrVpl81C4gtC3b1+tXr3a5qFhmQkKClJ0dLQWLVok6XawdurUqRo+fLhNu+nTp6tcuXL66KOPtGbNGnl5ealBgwZG+oeKFSvqo48+0ttvv61Zs2YpPj5exYsXV4MGDWxy/U6fPl1vvPGGhgwZooSEBK1du1aPP/64pNsrdQMDAxUREZHuoXj5bcSIEbpy5YqWLFkiBwcHde/eXX379s0wV3daDz/8sN555x2FhIRoxIgR8vHx0axZs7RixYp8m9/8+fM1ffp04+GDffr0UXx8vFauXGm08fb21vTp07VixQpt3bpVycnJ+u233+Ti4qK1a9cqNDRUS5cuVVRUlLy8vFS9enWb9CrdunVTsWLFtHz5co0bN84Iai9atEi1a9dW165dNXjwYL3//vs5esBiVsaPH6+UlBStWLFCZrNZbdu21fjx4/Xyyy8bbVq0aKEJEyZo/fr12rx5s+rWrauwsDC1b9/eaFOhQgWZTCa9/fbbunLlivFgxnHjxuV6TvHx8enyWkvSf//7X/33v/+1KXN0dNTx48czHKdHjx66cOGC1q9fr/DwcDVt2lTz58/PNvVJTq5xQevQoYM8PDy0dOlSY4Vy2bJl1axZswzPTW6MHz9eycnJWrZsmXHNhw4dquDgYCPXdX5d84I8DgAAAAAA8M/n6elpLHwLCgpSkSJF1KVLF40dO9amndlstnlu4I8//qjY2FjFxsaqZ8+eNm2tn+vn5+enbdu2aeXKlUpKSlLZsmU1fPjwTLNKZMUhNTU1NQ/HiAJiNpvVpk0btWrVStOmTbvb08F9LCEhQfXr11dISIiefPLJuz2dB8bEiRP1ww8/2DVNyZ04lXJKftdvp+QxySRHOcqk26u8neRkvLcuM8tsbJtlthnPejtZycZ7S/u07UxpXg5yMN6nrU87lqVtZtL2zWgM6zIHOaSry+3+FZK5YAAADS9JREFUM2qXtt5yXsx/vyxSlSoHORh/p52j9XjW/dLuM+38reeadn/Wx21dZn1OnP7++bblb+vxLfeLk9XPwJ3kZFOedk6WuVjqrPta6jM6b5kdm3U/6/1Z1zvJKd31zWzctOfV+p60busil0yPJW15ZmNbn4+080n7NWF9zm2uUQ5WX+R0hcbdaidJOf3nqqWddfuM3qcoRUlKUqISbb7mpNv3g6XMcs8nK1mJSpT5/9q719C6yzsO4N/TS9KtelKFrNQZaCJaOqe2G4oltW6IrGm9gCK+Kq06qS+mWLcX2mmNKNiVDcR2L9rMwV44i1VBxLYq3rLagoIVJ87NNakUi1EYO0lEe0myFy6H5tqml5NGP59DSP7P83ue8zu3f/758c/zT296MvCi34dzuDy2N705nMMDxh35vfeIW//8g9sGf34H7wsGx5Qf2//vsz+H3kG3wXHDfZaONNo+Zri4I+/ncA4PeJ76+0Z6PKfCka/h4OfhyNejJz1Dnv+x5jfS/ny0+MmZnGmZNiB2aqaW9w1VOeIi4ZmaaZlWHjM1U8v9UzIl38v3UpWqVKc60zO9PG91qjM13ywRNtznrb9t8PfB/SONO1rcyeo/0TFjnX+k/c2pOJvtVM071jmPFj9eJYPh8houl/64E8nzZLwOE6G0cqrexwxVKBQGvCc892PzXXm+RlraN8kpXZr1dDGmM6U5dQ4ePJiPPvooL730Uj777DPrwnLK/f3vf09dXV0WL1483ql8a7399tt59913c+GFF6a3tzdvvPFGXnjhhdx7773jnRoAAADAuFGUPk18/vnnuemmm3L22WfngQceOKYrYsKJuPTSS7Nt27bxTuNb7fvf/37eeOONtLS05MCBA/nhD3+Ye++9NytWrBjv1AAAAIBx9F05I3wkitKniXPPPTf//Oc/xzsN4CT68Y9/nM2bN493GgAAAACnlZEXOwMAAAAAgJNMURoAAAAAgIpRlAYAAAAAoGKsKQ0AAAAAUEHf9QsdOlMaAAAAAICKUZQGAAAAAKBiFKUBAAAAAKgYa0oDAAAAAFSQNaUBAAAAAKBCFKUBAAAAAKgYRWkAAAAAACrGmtIAAAAAABVkTWkAAAAAAKgQRWkAAAAAACpGURoAAAAAgIpRlAYAAAAAoGJc6BAAAAAAoIJc6BAAAAAAACpEURoAAAAAgIpRlAYAAAAAoGKsKQ0AAAAAUEHWlAYAAAAAYMLbs2dPbrnllsybNy+NjY1Zt25dDh48eNRxfX192bRpU372s5/l4osvzs0335z33ntvSFxHR0fuvPPOzJ8/P5dddll++9vfpru7e8x5KkoDAAAAAExwpVIpy5cvz6FDh7J+/fqsWrUqTz/9dNauXXvUsS0tLXn88cezYsWKbNy4MbW1tbn11luzb9++csyhQ4fyy1/+Mnv37s0f/vCHNDc3Z8eOHfn1r3895lwt3wEAAAAAMMFt3rw5X375ZTZs2JAZM2YkSXp6evLQQw9l5cqVmTlz5rDjDhw4kI0bN+bWW2/NihUrkiQ//elPs3jx4jzxxBNpbm5Okrz00kv5+OOPs3Xr1jQ0NCRJisVibrvttrz//vu5+OKLjzlXZ0oDAAAAAExwra2tWbBgQbkgnSRNTU3p7e3NW2+9NeK4d999N93d3Wlqaiq3VVVV5eqrr05ra+uA+efMmVMuSCdJY2NjZsyYkTfffHNMuSpKAwAAAABUUKFQGPHreLW1tQ0oGCffnMlcW1ubtra2UcclGTL2vPPOy/79+/P111+POH+hUEh9ff2o8w/H8h0AjKhuUl0+Ln5c3i5k4C/H4bb70ldu70vfiHMf2Td4nv6+I9sHxxyt/Wh9xxs7XNyJjB3JaM/d8c55MsYd6zyjvXb920cbe7zP6/HkdCLzHm3+0eYZy5zHm9fRfNuv+t3XN/xnqe//t1HHHtHf//Ox7teOZfto7cfaP1r8WMcOdjzvr+Hus/93w4nmcyI5DNc3Uq5jcbyfwUnDnB803P6v8P/bkT8P119IIZMyafg5TuPP+emcG984nV6j0XIZ7zzH+/45/XhPcCKuuuqqUftfffXVYds7OztTLBaHtNfU1KRUKo04X2dnZ6qqqlJdXT2gvVgspq+vL6VSKdOmTUtnZ2fOPPPMMc8/HEVpAEY0tTA1DZMbjh4IAAAAcIwUpQEAAAAAThMjnQl9NMViMV1dXUPaS6VSampqRh138ODBHDhwYMDZ0p2dnSkUCuWxxWIx3d3dw84/a9asMeVqTWkAAAAAgAmuoaFhyNrOXV1d+eKLL4asBT14XJK0t7cPaG9ra8s555yTadOmjTh/X19f2tvbR51/OIrSAAAAAAAT3KJFi7Jz5850dnaW27Zv355JkyalsbFxxHE/+clPcsYZZ2Tbtm3ltkOHDuXll1/OokWLBsz/0UcfZe/eveW2Xbt25b///W+uvPLKMeVa6Bvp6i8AAAAAAEwIpVIpS5cuTX19fVauXJmOjo6sXbs21157bdasWVOOW758efbv359XXnml3LZp06asX78+v/nNb3LBBRfkqaeeyo4dO/L888+nrq4uyTeF6htuuCFJcs899+Srr77KunXrMmfOnGzcuHFMuSpKAwAAAAB8C+zZsycPP/xwdu/enenTp+f666/PqlWrUlVVVY5ZtmxZPv3007z22mvltr6+vmzatCl//etf85///Cdz587Nfffdl/nz5w+Yv6OjI4888kh27NiRKVOm5Oqrr87q1atzxhlnjClPRWkAAAAAACrGmtIAAAAAAFSMojQAAAAAABWjKA0AAAAAQMUoSgMAAAAAUDGK0gAAAAAAVIyiNAAAAAAAFaMoDQAAAABAxShKAzDAnj17csstt2TevHlpbGzMunXrcvDgwfFOCwDghD333HOZM2fOkK/f//73A+K2bNmSX/ziF7noooty3XXX5fXXXx8yV1dXV1avXp3LLrss8+fPz1133ZXPP/+8Ug8FACa0KeOdAACnj1KplOXLl2f27NlZv359Ojo6snbt2nz99ddZs2bNeKcHAHBS/OlPf8qZZ55Z3p45c2b55xdffDEPPPBA7rjjjlx++eXZunVrfvWrX+XJJ5/MvHnzynF33313/v3vf6e5uTnV1dV57LHHcvvtt+fZZ5/NlCn+1AaA0fhNCUDZ5s2b8+WXX2bDhg2ZMWNGkqSnpycPPfRQVq5cOeAPNgCAierCCy/M2WefPWzf448/nqVLl+buu+9Oklx++eX517/+lT/+8Y9paWlJkuzevTs7duzIE088kYULFyZJ6uvrs2TJkrz88stZsmRJZR4IAExQlu8AoKy1tTULFiwoF6STpKmpKb29vXnrrbfGMTMAgFNv37592bt3b5qamga0L1myJLt27Sovadba2ppisZjGxsZyTENDQ+bOnZvW1taK5gwAE5GiNABlbW1taWhoGNBWLBZTW1ubtra2ccoKAODkuuaaazJ37txcddVV2bhxY3p6epKkfLxTX18/IP68887LoUOHsm/fvnJcfX19CoXCgLiGhgbHTABwDCzfAUBZZ2dnisXikPaampqUSqVxyAgA4OSpra3NnXfemUsuuSSFQiGvvfZaHnvssXR0dGTNmjXl453Bx0P92/39nZ2dA9ak7ldTU5MPPvjgFD8KAJj4FKUBAAD4TrjiiityxRVXlLcXLlyY6urq/OUvf8kdd9wxjpkBwHeL5TsAKCsWi+nq6hrSXiqVUlNTMw4ZAQCcWk1NTenp6ck//vGP8vHO4OOhzs7OJCn3F4vFdHd3D5nLMRMAHBtFaQDKhlsHsaurK1988cWQtaYBAL5t+o93Bh8PtbW1ZerUqamrqyvHtbe3p6+vb0Bce3u7YyYAOAaK0gCULVq0KDt37iyfDZQk27dvz6RJkwZcXR4A4Nti69atmTx5cn70ox+lrq4us2fPzvbt24fELFiwIFVVVUm+OWYqlUrZtWtXOaa9vT0ffvhhFi1aVNH8AWAimtzc3Nw83kkAcHo4//zzs2XLluzcuTM/+MEP8s477+R3v/tdbrzxxixdunS80wMAOCG33XZbOjo60t3dnU8++SR//vOf8+STT2bZsmVZvHhxkuSss87Khg0b0tvbmyRpaWnJ66+/nkcffTSzZs1KksyaNSvvvfdennnmmcycOTP79u3Lgw8+mNra2qxevTqTJjn/CwBGU+gb/P9GAHyn7dmzJw8//HB2796d6dOn5/rrr8+qVavKZwYBAExUjzzySP72t7/ls88+S29vb2bPnp2bbropy5YtS6FQKMdt2bIlLS0t2b9/f+rr63PPPffk5z//+YC5urq68uijj+aVV17J4cOHs3Dhwtx///2ZOXNmpR8WAEw4itIAAAAAAFSM/ykCAAAAAKBiFKUBAAAAAKgYRWkAAAAAACpGURoAAAAAgIpRlAYAAAAAoGIUpQEAAAAAqBhFaQAAAAAAKkZRGgAAAACAilGUBgAAAACgYhSlAQAAAACoGEVpAAAAAAAq5n8GVhMigIQALQAAAABJRU5ErkJggg==", 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", 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" ] }, "metadata": {} } ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "0G1Njx6VyF58", "outputId": "4d507d58-7cc7-43ba-8c54-41b21fc14e1c" } } ] } ================================================ FILE: colabs/Embeddings.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "Proteinfer Embedding Analysis V2", "provenance": [], "collapsed_sections": [ "Rqbcum1HM8aL", "KWu0UovMMzEC", "8pScRsRCwX2H", "IsyTzFspnTUu" ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "Rqbcum1HM8aL" }, "source": [ "\n", "## Initial setup (code/data download)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kv5wiyegq1ge", "outputId": "9569d668-75bc-4652-d5e6-54f8fef69a54" }, "source": [ "\n", "%tensorflow_version 1\n", "!wget https://github.com/lmcinnes/umap/archive/master.zip\n", "!unzip -q master.zip\n", "!rm master.zip\n", "!cd umap-master" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n", "You set: `1`. This will be interpreted as: `1.x`.\n", "\n", "\n", "TensorFlow 1.x selected.\n", "--2021-09-20 20:34:36-- https://github.com/lmcinnes/umap/archive/master.zip\n", "Resolving github.com (github.com)... 140.82.112.4\n", "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://codeload.github.com/lmcinnes/umap/zip/master [following]\n", "--2021-09-20 20:34:36-- https://codeload.github.com/lmcinnes/umap/zip/master\n", "Resolving codeload.github.com (codeload.github.com)... 140.82.113.9\n", "Connecting to codeload.github.com (codeload.github.com)|140.82.113.9|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [application/zip]\n", "Saving to: ‘master.zip’\n", "\n", "master.zip [ <=> ] 39.12M 15.1MB/s in 2.6s \n", "\n", "2021-09-20 20:34:39 (15.1 MB/s) - ‘master.zip’ saved [41020921]\n", "\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "arKZBwHZseQU", "outputId": "12e0b673-9e70-4c2c-fdcb-77f0376cf7e0" }, "source": [ "%cd umap-master/\n", "!ls" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/umap-master\n", "appveyor.yml\t doc\t\t Makefile\trequirements.txt\n", "azure-pipelines.yml docs_requirements.txt notebooks\tsetup.py\n", "ci_scripts\t examples\t\t paper.bib\tumap\n", "CODE_OF_CONDUCT.md images\t\t paper.md\n", "CONTRIBUTING.md LICENSE.txt\t README.rst\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BiuQKLG1sZ_U", "outputId": "7dc562fc-b72a-4999-cf91-4c2b0e46be2d" }, "source": [ "!pip3 install -r requirements.txt" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.19.5)\n", "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (1.4.1)\n", "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (0.22.2.post1)\n", "Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (0.51.2)\n", "Collecting pynndescent>=0.5\n", " Downloading pynndescent-0.5.4.tar.gz (1.1 MB)\n", "\u001b[K |████████████████████████████████| 1.1 MB 10.5 MB/s \n", "\u001b[?25hCollecting tbb>=2019.0\n", " Downloading tbb-2021.3.0-py2.py3-none-manylinux1_x86_64.whl (4.1 MB)\n", "\u001b[K |████████████████████████████████| 4.1 MB 36.8 MB/s \n", "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (4.62.2)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->-r requirements.txt (line 3)) (1.0.1)\n", "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.51.2->-r requirements.txt (line 4)) (0.34.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.51.2->-r requirements.txt (line 4)) (57.4.0)\n", "Building wheels for collected packages: pynndescent\n", " Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for pynndescent: filename=pynndescent-0.5.4-py3-none-any.whl size=52373 sha256=dce98e806b5d2a2eb7665486af9b7c8e33ed79bda0ff60495dfe807860285ab0\n", " Stored in directory: /root/.cache/pip/wheels/d0/5b/62/3401692ddad12324249c774c4b15ccb046946021e2b581c043\n", "Successfully built pynndescent\n", "Installing collected packages: tbb, pynndescent\n", "Successfully installed pynndescent-0.5.4 tbb-2021.3.0\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "gqnLXvYT1V5-" }, "source": [ "import umap" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rbROrZPj1lrN", "outputId": "0756183b-b91f-408a-eca8-5f670d930b34" }, "source": [ "!git clone https://github.com/google-research/proteinfer \n", "\n", "%cd proteinfer\n", "\n", "!pip3 install -qr requirements.txt\n" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'proteinfer'...\n", "remote: Enumerating objects: 854, done.\u001b[K\n", "remote: Counting objects: 100% (577/577), done.\u001b[K\n", "remote: Compressing objects: 100% (419/419), done.\u001b[K\n", "remote: Total 854 (delta 250), reused 366 (delta 139), pack-reused 277\u001b[K\n", "Receiving objects: 100% (854/854), 102.40 MiB | 29.92 MiB/s, done.\n", "Resolving deltas: 100% (389/389), done.\n", "/content/umap-master/proteinfer\n", "\u001b[K |████████████████████████████████| 99 kB 6.0 MB/s \n", "\u001b[K |████████████████████████████████| 2.3 MB 60.2 MB/s \n", "\u001b[K |████████████████████████████████| 10.8 MB 15.5 MB/s \n", "\u001b[K |████████████████████████████████| 2.8 MB 54.6 MB/s \n", "\u001b[K |████████████████████████████████| 59 kB 9.0 MB/s \n", "\u001b[K |████████████████████████████████| 89 kB 12.1 MB/s \n", "\u001b[K |████████████████████████████████| 56 kB 6.7 MB/s \n", "\u001b[K |████████████████████████████████| 17.3 MB 54 kB/s \n", "\u001b[K |████████████████████████████████| 10.5 MB 28.2 MB/s \n", "\u001b[K |████████████████████████████████| 107 kB 75.0 MB/s \n", "\u001b[K |████████████████████████████████| 13.1 MB 14.2 MB/s \n", "\u001b[K |████████████████████████████████| 4.4 MB 59.6 MB/s \n", "\u001b[K |████████████████████████████████| 226 kB 78.3 MB/s \n", "\u001b[K |████████████████████████████████| 6.8 MB 32.5 MB/s \n", "\u001b[K |████████████████████████████████| 411.0 MB 23 kB/s \n", "\u001b[K |████████████████████████████████| 103 kB 72.9 MB/s \n", "\u001b[K |████████████████████████████████| 328 kB 74.9 MB/s \n", "\u001b[K |████████████████████████████████| 63 kB 2.3 MB/s \n", "\u001b[K |████████████████████████████████| 9.5 MB 3.4 MB/s \n", "\u001b[?25h Building wheel for absl-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lucid 0.3.10 requires umap-learn, which is not installed.\n", "xarray 0.18.2 requires numpy>=1.17, but you have numpy 1.16.5 which is incompatible.\n", "tensorflow-metadata 1.2.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.7.1 which is incompatible.\n", "pyerfa 2.0.0 requires numpy>=1.17, but you have numpy 1.16.5 which is incompatible.\n", "pyarrow 3.0.0 requires numpy>=1.16.6, but you have numpy 1.16.5 which is incompatible.\n", "kapre 0.3.5 requires numpy>=1.18.5, but you have numpy 1.16.5 which is incompatible.\n", "kapre 0.3.5 requires tensorflow>=2.0.0, but you have tensorflow 1.15.2 which is incompatible.\n", "jaxlib 0.1.70+cuda111 requires numpy>=1.18, but you have numpy 1.16.5 which is incompatible.\n", "jax 0.2.19 requires numpy>=1.18, but you have numpy 1.16.5 which is incompatible.\n", "google-colab 1.0.0 requires astor~=0.8.1, but you have astor 0.7.1 which is incompatible.\n", "google-colab 1.0.0 requires six~=1.15.0, but you have six 1.12.0 which is incompatible.\n", "google-api-python-client 1.12.8 requires six<2dev,>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "google-api-core 1.26.3 requires six>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", "cupy-cuda111 9.4.0 requires numpy<1.24,>=1.17, but you have numpy 1.16.5 which is incompatible.\n", "astropy 4.3.1 requires numpy>=1.17, but you have numpy 1.16.5 which is incompatible.\n", "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UbK8U6SEKqUU", "outputId": "6e6908e2-001b-487a-c590-edff1afa5a9b" }, "source": [ "\n", "import pandas as pd\n", "import tensorflow\n", "import inference\n", "import parenthood_lib\n", "import baseline_utils,subprocess\n", "import shlex\n", "import tqdm \n", "import sklearn\n", "import numpy as np\n", "import utils\n", "import colab_evaluation\n", "import plotly.express as px\n", "\n", "\n", "from plotnine import ggplot, geom_point, geom_point, geom_line, aes, stat_smooth, facet_wrap, xlim,coord_cartesian,theme_bw,labs,ggsave\n", "tensorflow.test.is_gpu_available()" ], "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "metadata": { "id": "YRyLcqoHzzpQ" }, "source": [ "" ], "execution_count": 6, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "bw-4B4_2y8rx", "outputId": "7a469f44-b850-4e6d-d81d-e0ba8d698d87" }, "source": [ "!wget https://github.com/lmcinnes/umap/archive/master.zip\n", "!unzip -q master.zip\n", "!rm master.zip\n", "%cd umap-master\n", "!ls\n", "!pip3 install -r requirements.txt\n", "import umap\n", "%cd .." ], "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2021-09-20 20:36:39-- https://github.com/lmcinnes/umap/archive/master.zip\n", "Resolving github.com (github.com)... 140.82.114.4\n", "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://codeload.github.com/lmcinnes/umap/zip/master [following]\n", "--2021-09-20 20:36:39-- https://codeload.github.com/lmcinnes/umap/zip/master\n", "Resolving codeload.github.com (codeload.github.com)... 140.82.114.10\n", "Connecting to codeload.github.com (codeload.github.com)|140.82.114.10|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [application/zip]\n", "Saving to: ‘master.zip’\n", "\n", "master.zip [ <=> ] 39.12M 16.0MB/s in 2.4s \n", "\n", "2021-09-20 20:36:42 (16.0 MB/s) - ‘master.zip’ saved [41020921]\n", "\n", "/content/umap-master/proteinfer/umap-master\n", "appveyor.yml\t doc\t\t Makefile\trequirements.txt\n", "azure-pipelines.yml docs_requirements.txt notebooks\tsetup.py\n", "ci_scripts\t examples\t\t paper.bib\tumap\n", "CODE_OF_CONDUCT.md images\t\t paper.md\n", "CONTRIBUTING.md LICENSE.txt\t README.rst\n", "Collecting numpy>=1.17\n", " Downloading numpy-1.21.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)\n", "\u001b[K |████████████████████████████████| 15.7 MB 329 kB/s \n", "\u001b[?25hRequirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (1.4.1)\n", "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (0.23.2)\n", "Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (0.51.2)\n", "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (0.5.4)\n", "Requirement already satisfied: tbb>=2019.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (2021.3.0)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (4.62.2)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->-r requirements.txt (line 3)) (1.0.1)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->-r requirements.txt (line 3)) (2.2.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.51.2->-r requirements.txt (line 4)) (57.4.0)\n", "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.51.2->-r requirements.txt (line 4)) (0.34.0)\n", "Installing collected packages: numpy\n", " Attempting uninstall: numpy\n", " Found existing installation: numpy 1.16.5\n", " Uninstalling numpy-1.16.5:\n", " Successfully uninstalled numpy-1.16.5\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lucid 0.3.10 requires umap-learn, which is not installed.\n", "lucid 0.3.10 requires numpy<=1.19, but you have numpy 1.21.2 which is incompatible.\n", "tensorflow-gpu 1.15.4 requires numpy<1.19.0,>=1.16.0, but you have numpy 1.21.2 which is incompatible.\n", "kapre 0.3.5 requires tensorflow>=2.0.0, but you have tensorflow 1.15.2 which is incompatible.\n", "google-colab 1.0.0 requires astor~=0.8.1, but you have astor 0.7.1 which is incompatible.\n", "google-colab 1.0.0 requires six~=1.15.0, but you have six 1.12.0 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", "Successfully installed numpy-1.21.2\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "numpy" ] } } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "/content/umap-master/proteinfer\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "T7OLyg9DF3uw" }, "source": [ "\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\n", "!tar xzf noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_EC/eval_test.fasta\n", "\n" ], "execution_count": 8, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "L8Y2qeJAJuPH", "outputId": "bed5cda9-9565-4058-f3bf-8e01516bdd83" }, "source": [ "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00001-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00002-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00003-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00004-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00005-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00006-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00007-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00008-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00009-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00010-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00011-of-00013.tfrecord\n", "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00012-of-00013.tfrecord\n" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2021-09-20 20:36:54-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00001-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c0b::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 17162409 (16M) [application/octet-stream]\n", "Saving to: ‘test-00001-of-00013.tfrecord’\n", "\n", "test-00001-of-00013 100%[===================>] 16.37M 75.1MB/s in 0.2s \n", "\n", "2021-09-20 20:36:55 (75.1 MB/s) - ‘test-00001-of-00013.tfrecord’ saved [17162409/17162409]\n", "\n", "--2021-09-20 20:36:55-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00002-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 74.125.137.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 5107497 (4.9M) [application/octet-stream]\n", "Saving to: ‘test-00002-of-00013.tfrecord’\n", "\n", "test-00002-of-00013 100%[===================>] 4.87M --.-KB/s in 0.02s \n", "\n", "2021-09-20 20:36:55 (208 MB/s) - ‘test-00002-of-00013.tfrecord’ saved [5107497/5107497]\n", "\n", "--2021-09-20 20:36:55-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00003-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c03::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 2876530 (2.7M) [application/octet-stream]\n", "Saving to: ‘test-00003-of-00013.tfrecord’\n", "\n", "test-00003-of-00013 100%[===================>] 2.74M --.-KB/s in 0.02s \n", "\n", "2021-09-20 20:36:56 (135 MB/s) - ‘test-00003-of-00013.tfrecord’ saved [2876530/2876530]\n", "\n", "--2021-09-20 20:36:56-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00004-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 74.125.137.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1952460 (1.9M) [application/octet-stream]\n", "Saving to: ‘test-00004-of-00013.tfrecord’\n", "\n", "test-00004-of-00013 100%[===================>] 1.86M --.-KB/s in 0.02s \n", "\n", "2021-09-20 20:36:56 (115 MB/s) - ‘test-00004-of-00013.tfrecord’ saved [1952460/1952460]\n", "\n", "--2021-09-20 20:36:56-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00005-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.101.128, 142.250.141.128, 142.251.2.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.101.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1835388 (1.8M) [application/octet-stream]\n", "Saving to: ‘test-00005-of-00013.tfrecord’\n", "\n", "test-00005-of-00013 100%[===================>] 1.75M --.-KB/s in 0.01s \n", "\n", "2021-09-20 20:36:56 (140 MB/s) - ‘test-00005-of-00013.tfrecord’ saved [1835388/1835388]\n", "\n", "--2021-09-20 20:36:56-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00006-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c06::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 691057 (675K) [application/octet-stream]\n", "Saving to: ‘test-00006-of-00013.tfrecord’\n", "\n", "test-00006-of-00013 100%[===================>] 674.86K --.-KB/s in 0.009s \n", "\n", "2021-09-20 20:36:56 (74.8 MB/s) - ‘test-00006-of-00013.tfrecord’ saved [691057/691057]\n", "\n", "--2021-09-20 20:36:57-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00007-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c0b::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1182345 (1.1M) [application/octet-stream]\n", "Saving to: ‘test-00007-of-00013.tfrecord’\n", "\n", "test-00007-of-00013 100%[===================>] 1.13M --.-KB/s in 0.008s \n", "\n", "2021-09-20 20:36:57 (150 MB/s) - ‘test-00007-of-00013.tfrecord’ saved [1182345/1182345]\n", "\n", "--2021-09-20 20:36:57-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00008-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 74.125.137.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 603474 (589K) [application/octet-stream]\n", "Saving to: ‘test-00008-of-00013.tfrecord’\n", "\n", "test-00008-of-00013 100%[===================>] 589.33K --.-KB/s in 0.004s \n", "\n", "2021-09-20 20:36:57 (137 MB/s) - ‘test-00008-of-00013.tfrecord’ saved [603474/603474]\n", "\n", "--2021-09-20 20:36:57-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00009-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.137.128, 142.250.141.128, 142.251.2.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.137.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 302710 (296K) [application/octet-stream]\n", "Saving to: ‘test-00009-of-00013.tfrecord’\n", "\n", "test-00009-of-00013 100%[===================>] 295.62K --.-KB/s in 0.003s \n", "\n", "2021-09-20 20:36:57 (107 MB/s) - ‘test-00009-of-00013.tfrecord’ saved [302710/302710]\n", "\n", "--2021-09-20 20:36:57-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00010-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c0b::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 14265593 (14M) [application/octet-stream]\n", "Saving to: ‘test-00010-of-00013.tfrecord’\n", "\n", "test-00010-of-00013 100%[===================>] 13.60M 71.2MB/s in 0.2s \n", "\n", "2021-09-20 20:36:58 (71.2 MB/s) - ‘test-00010-of-00013.tfrecord’ saved [14265593/14265593]\n", "\n", "--2021-09-20 20:36:58-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00011-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.137.128, 142.250.141.128, 142.251.2.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.137.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1525346 (1.5M) [application/octet-stream]\n", "Saving to: ‘test-00011-of-00013.tfrecord’\n", "\n", "test-00011-of-00013 100%[===================>] 1.45M --.-KB/s in 0.01s \n", "\n", "2021-09-20 20:36:58 (98.0 MB/s) - ‘test-00011-of-00013.tfrecord’ saved [1525346/1525346]\n", "\n", "--2021-09-20 20:36:58-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/datasets/swissprot/random/test-00012-of-00013.tfrecord\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 74.125.137.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 15807173 (15M) [application/octet-stream]\n", "Saving to: ‘test-00012-of-00013.tfrecord’\n", "\n", "test-00012-of-00013 100%[===================>] 15.07M 97.9MB/s in 0.2s \n", "\n", "2021-09-20 20:36:59 (97.9 MB/s) - ‘test-00012-of-00013.tfrecord’ saved [15807173/15807173]\n", "\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "KWu0UovMMzEC" }, "source": [ "##Read in the test dataset" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RlyjYvhL9W8t", "outputId": "f2ad0b2e-44b2-4f10-f5ae-ffa892dff68d" }, "source": [ "import protein_dataset\n", "import tqdm\n", "import numpy as np\n", "import pandas as pd\n", "sequence_iterator = protein_dataset.yield_examples(\"./test*.tfrecord\")\n", "sequences = []\n", "labels = []\n", "ids = []\n", "for example in tqdm.tqdm(sequence_iterator):\n", " ids.append(example[protein_dataset.SEQUENCE_ID_KEY])\n", " sequences.append(example[protein_dataset.SEQUENCE_KEY])\n", " labels.append(example[protein_dataset.LABEL_KEY])\n", "\n", "# If we want to optimise for inference speed we should sort the dataset by\n", "# sequence length:\n", "seq_lengths = [len(x) for x in sequences]\n", "indices = np.argsort(-np.array(seq_lengths)).tolist()\n", "\n", "ids = [ids[indices[x]] for x in range(len(indices))]\n", "sequences = [sequences[indices[x]] for x in range(len(indices))]\n", "labels = [set(labels[indices[x]]) for x in range(len(indices))]" ], "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\r0it [00:00, ?it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "WARNING:tensorflow:From /content/umap-master/proteinfer/protein_dataset.py:290: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "36341it [00:13, 2643.47it/s]\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "8pScRsRCwX2H" }, "source": [ "## Load the saved model" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zljPgF2BMBj-", "outputId": "6fe1ee09-e8d0-4885-8762-f5e64a2e162c" }, "source": [ "inferrer = inference.Inferrer(\n", " 'noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140',use_tqdm= True, batch_size=16,activation_type=\"pooled_representation\"\n", ")\n", "\n", "label_vocab = inferrer.get_variable('label_vocab:0').astype(str)\n", "label_normalizer = parenthood_lib.get_applicable_label_dict(\n", " 'parenthood.json.gz')\n" ], "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/ragged/ragged_tensor.py:1586: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "G4C8mjwOxvhU" }, "source": [ "\n", "kernel = inferrer.get_variable(\"logits/kernel/read:0\")\n" ], "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IsyTzFspnTUu" }, "source": [ "## Perform inference" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YTDYJAsJkwng", "outputId": "02634056-3a67-4d6c-d283-49efcfa7f15d" }, "source": [ "a=inferrer.get_activations(sequences)\n", "df = pd.DataFrame(a)" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Annotating batches of sequences: 100%|██████████| 2272/2272 [12:17<00:00, 3.08it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "C2OWl0GGe_JA", "outputId": "42cd9dbe-d56c-4614-fd60-a55613cef43d" }, "source": [ "df" ], "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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36341 rows × 1100 columns

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" ], "text/plain": [ " 0 1 2 ... 1097 1098 1099\n", "0 -0.502606 -0.656812 0.531399 ... -0.775725 -0.995626 -0.892861\n", "1 -0.518393 -0.638065 0.481470 ... -0.714520 -1.037862 -0.853183\n", "2 -0.518393 -0.638065 0.481470 ... -0.714520 -1.037862 -0.853183\n", "3 -0.497833 -0.616177 0.486445 ... -0.699195 -0.990511 -0.869959\n", "4 -0.405598 -1.313194 0.205521 ... -0.231710 -1.008995 -0.664563\n", "... ... ... ... ... ... ... ...\n", "36336 -0.008643 3.119127 -0.779204 ... 1.713477 0.903561 1.304516\n", "36337 -1.987763 3.587883 1.016211 ... 3.500444 0.198183 1.093233\n", "36338 -1.521850 2.871524 1.756830 ... 2.122742 -0.413387 1.888609\n", "36339 -1.757260 3.463014 0.987380 ... 3.611753 0.347418 0.826603\n", "36340 -2.891652 3.212122 1.987798 ... 3.227597 0.019375 1.097588\n", "\n", "[36341 rows x 1100 columns]" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VFUIWAQlfKpg", "outputId": "985bad49-a8dc-4fe3-c137-efe862dc1c74" }, "source": [ "%cd umap-master\n", "import umap" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/umap-master/proteinfer/umap-master\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "IXKn4IxZnib8" }, "source": [ "## Embedding" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cVUR6K0khTC0", "outputId": "570e400a-3fb3-4742-d29c-f142e08e1141" }, "source": [ "def filter_to_just_ec(the_input):\n", " return [x for x in the_input if x.startswith(b\"EC:\") and b\"-\" not in x]\n", "ec_labels = [filter_to_just_ec(x) for x in labels]\n", "to_include = [len(x) ==1 for x in ec_labels]\n", "indices = [i for i,x in enumerate(to_include) if x]\n", "labs = [ec_labels[x] for x in indices]\n", "\n", "\n", "\n", "pcs = umap.UMAP(300, metric='cosine', min_dist=1).fit_transform(df)" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "B4Zrwhrdg26Y" }, "source": [ "split_ec=[x[0].decode().replace(\"EC:\",\"\").split(\".\") for x in labs]" ], "execution_count": 17, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "V2nlXXE8gosr" }, "source": [ "color_palette = [\n", " (0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n", " (1.0, 0.4980392156862745, 0.054901960784313725),\n", " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n", " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n", " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n", " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n", " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n", " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n", " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n", " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]\n", "colors = pd.Series(split_ec).apply(lambda x: color_palette[int(x[0])])" ], "execution_count": 18, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "oh43oeQnfhcT", "outputId": "b52703a9-1a00-4634-ddcc-170c82a9030b" }, "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter(pcs[indices, 0], pcs[indices, 1], color=colors, marker='.')\n", "plt.show()" ], "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "vEVxup_0ggnb" }, "source": [ "def filter_to_just_ec(the_input):\n", " return [x for x in the_input if x.startswith(b\"EC:\") and b\"-\" not in x]\n", "ec_labels = [filter_to_just_ec(x) for x in labels]" ], "execution_count": 20, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "zUtJN63RmC4b" }, "source": [ "nonenzymes = [len(x)==0 for x in ec_labels]\n", "non_enzyme_indices = [i for i,x in enumerate(nonenzymes) if x]" ], "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "DycCLzvgnrVC" }, "source": [ "## EC model with GO terms" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "oYB_cjc9mKgq", "outputId": "dadfd405-074d-499b-d3ee-0288de15d350" }, "source": [ "import matplotlib.pyplot as plt\n", "is_membrane = np.array([b\"GO:0031224\" in x for x in labels])\n", "plt.scatter(pcs[non_enzyme_indices, 0], pcs[non_enzyme_indices, 1], color=[\"red\" if x else \"gray\" for x in is_membrane[non_enzyme_indices]], marker='.')\n", "plt.show()" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "AQIHw5LbmsDf", "outputId": "3fa0b47d-b968-4a9d-a5a1-dc7d76f0fafd" }, "source": [ "ribosome = np.array([b\"GO:0003735\" in x for x in labels])\n", "plt.scatter(pcs[non_enzyme_indices, 0], pcs[non_enzyme_indices, 1], color=[\"red\" if x else \"gray\" for x in ribosome[non_enzyme_indices]], marker='.')\n", "plt.show()" ], "execution_count": 23, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" } } ] } ] } ================================================ FILE: colabs/Price.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Price comparison", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "metadata": { "id": "E9iwE1jezcGB", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "467889e8-37b0-405f-e9dc-7c8345fcdab8" }, "source": [ "!git clone https://github.com/google-research/proteinfer \n", "!pip install -q -r proteinfer/requirements.txt\n", "%cd proteinfer\n" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'proteinfer'...\n", "remote: Enumerating objects: 612, done.\u001b[K\n", "remote: Counting objects: 100% (335/335), done.\u001b[K\n", "remote: Compressing objects: 100% (286/286), done.\u001b[K\n", "remote: Total 612 (delta 113), reused 160 (delta 31), pack-reused 277\u001b[K\n", "Receiving objects: 100% (612/612), 88.74 MiB | 26.54 MiB/s, done.\n", "Resolving deltas: 100% (252/252), done.\n", "\u001b[K |████████████████████████████████| 99 kB 5.4 MB/s \n", "\u001b[K |████████████████████████████████| 2.3 MB 37.1 MB/s \n", "\u001b[K |████████████████████████████████| 10.8 MB 51.6 MB/s \n", "\u001b[K |████████████████████████████████| 2.8 MB 50.4 MB/s \n", "\u001b[K |████████████████████████████████| 50 kB 8.1 MB/s \n", "\u001b[K |████████████████████████████████| 59 kB 7.0 MB/s \n", "\u001b[K |████████████████████████████████| 89 kB 10.8 MB/s \n", "\u001b[K |████████████████████████████████| 56 kB 6.0 MB/s \n", "\u001b[K |████████████████████████████████| 17.3 MB 52 kB/s \n", "\u001b[K |████████████████████████████████| 10.5 MB 25.5 MB/s \n", "\u001b[K |████████████████████████████████| 107 kB 68.4 MB/s \n", "\u001b[K |████████████████████████████████| 13.1 MB 209 kB/s \n", "\u001b[K |████████████████████████████████| 4.4 MB 53.1 MB/s \n", "\u001b[K |████████████████████████████████| 226 kB 74.6 MB/s \n", "\u001b[K |████████████████████████████████| 6.8 MB 21.4 MB/s \n", "\u001b[K |████████████████████████████████| 110.5 MB 1.2 MB/s \n", "\u001b[K |████████████████████████████████| 411.0 MB 25 kB/s \n", "\u001b[K |████████████████████████████████| 503 kB 67.1 MB/s \n", "\u001b[K |████████████████████████████████| 103 kB 80.1 MB/s \n", "\u001b[K |████████████████████████████████| 45 kB 4.9 MB/s \n", "\u001b[K |████████████████████████████████| 328 kB 59.6 MB/s \n", "\u001b[K |████████████████████████████████| 9.5 MB 17.7 MB/s \n", "\u001b[K |████████████████████████████████| 63 kB 2.2 MB/s \n", "\u001b[K |████████████████████████████████| 3.8 MB 55.4 MB/s \n", "\u001b[?25h Building wheel for absl-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "xarray 0.18.2 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "tensorflow-probability 0.13.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\n", "tensorflow-metadata 1.2.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.7.1 which is incompatible.\n", "spacy 2.2.4 requires tqdm<5.0.0,>=4.38.0, but you have tqdm 4.28.1 which is incompatible.\n", "pyerfa 2.0.0 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "pyarrow 3.0.0 requires numpy>=1.16.6, but you have numpy 1.16.2 which is incompatible.\n", "panel 0.12.1 requires tqdm>=4.48.0, but you have tqdm 4.28.1 which is incompatible.\n", "kapre 0.3.5 requires numpy>=1.18.5, but you have numpy 1.16.2 which is incompatible.\n", "kapre 0.3.5 requires tensorflow>=2.0.0, but you have tensorflow 1.15.4 which is incompatible.\n", "jaxlib 0.1.70+cuda110 requires numpy>=1.18, but you have numpy 1.16.2 which is incompatible.\n", "jax 0.2.19 requires numpy>=1.18, but you have numpy 1.16.2 which is incompatible.\n", "google-colab 1.0.0 requires astor~=0.8.1, but you have astor 0.7.1 which is incompatible.\n", "google-colab 1.0.0 requires six~=1.15.0, but you have six 1.12.0 which is incompatible.\n", "google-api-python-client 1.12.8 requires six<2dev,>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "google-api-core 1.26.3 requires six>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "fbprophet 0.7.1 requires tqdm>=4.36.1, but you have tqdm 4.28.1 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", "cupy-cuda101 9.1.0 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "astropy 4.3.1 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", "/content/proteinfer\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "mw86VQUi-omr", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "317a149b-b27b-49ee-cdbc-082128be0352" }, "source": [ "!cd proteinfer\n", "!ls" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/bin/bash: line 0: cd: proteinfer: No such file or directory\n", "baseline_utils.py\t install_models.py\t protein_model_test.py\n", "baseline_utils_test.py\t LICENSE\t\t README.md\n", "colab_evaluation.py\t misc\t\t\t requirements.txt\n", "colab_evaluation_test.py parenthood_bin.py\t testdata\n", "colabs\t\t\t parenthood_lib.py\t test_util.py\n", "CONTRIBUTING.md\t\t parenthood_lib_test.py test_util_test.py\n", "evaluation.py\t\t protein_dataset.py\t train.py\n", "evaluation_test.py\t protein_dataset_test.py train_test.py\n", "hparams_sets.py\t\t proteinfer.py\t\t utils.py\n", "inference.py\t\t proteinfer_test.py\t utils_test.py\n", "inference_test.py\t protein_model.py\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "Zu10hrem8ltZ" }, "source": [ "import inference\n", "import pandas as pd\n", "import evaluation\n", "import parenthood_lib\n", "\n", "import re" ], "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lA8bjb_T8ffX" }, "source": [ "# Parse Price's ground truth labels" ] }, { "cell_type": "code", "metadata": { "id": "WJ_sUsOL0UIi", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c4425fc8-db33-489c-dce2-4df8be41182b" }, "source": [ "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/price_enzymes.tsv" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2021-09-13 02:01:43-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/price_enzymes.tsv\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c0d::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 400317 (391K) [application/octet-stream]\n", "Saving to: ‘price_enzymes.tsv’\n", "\n", "\rprice_enzymes.tsv 0%[ ] 0 --.-KB/s \rprice_enzymes.tsv 100%[===================>] 390.93K --.-KB/s in 0.004s \n", "\n", "2021-09-13 02:01:43 (93.7 MB/s) - ‘price_enzymes.tsv’ saved [400317/400317]\n", "\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "JuN0SmQfosHJ" }, "source": [ "with open('price_enzymes.tsv') as f:\n", " price_enzymes = pd.read_csv(f, sep='\\t')" ], "execution_count": 5, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bSpUUPHF50-z" }, "source": [ "price_enzymes_w_annotation = price_enzymes[price_enzymes.new_annotation.apply(lambda x: x.count('EC ') == 1)].copy()" ], "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "a9SwAfKu8sMi" }, "source": [ "# Predict for these sequences" ] }, { "cell_type": "code", "metadata": { "id": "407DGW2d0p5j", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "790d05d6-3a64-4647-890c-5182bc1ab272" }, "source": [ "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz" ], "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2021-09-13 02:01:43-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 2607:f8b0:4023:c0b::80, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 134391480 (128M) [application/octet-stream]\n", "Saving to: ‘noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz’\n", "\n", "noxpnd_cnn_swisspro 100%[===================>] 128.17M 111MB/s in 1.2s \n", "\n", "2021-09-13 02:01:45 (111 MB/s) - ‘noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz’ saved [134391480/134391480]\n", "\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "mc8RU2T_006q" }, "source": [ "!tar xzf noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz" ], "execution_count": 8, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "vPAMz95A10l4", "outputId": "4c4dad89-4897-4307-c090-d26228b64232", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "inferrer = inference.Inferrer('noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140', use_tqdm=True)" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", "W0913 02:01:47.257570 140413780555648 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/ragged/ragged_tensor.py:1586: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "kiXZNz-N24c8", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2c3a52d3-3834-4cfd-aa69-e048e3f3e637" }, "source": [ "predictions = inferrer.get_activations(list(price_enzymes_w_annotation.protein_sequence))" ], "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Annotating batches of sequences: 100%|██████████| 3/3 [01:08<00:00, 26.95s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "yjoH2ojYUnzr" }, "source": [ "# Turn prediction array into actual labels by using the vocab.\n", "cnn_label_vocab = inferrer.get_variable('label_vocab:0').astype(str)\n", "price_enzymes_w_annotation['predicted_label'] = evaluation.filter_predictions_to_above_threshold(predictions, 0.148222, cnn_label_vocab) # Threshold is that which maximizes f1 score." ], "execution_count": 11, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ZxySu4lY3RKv", "outputId": "0e77ecc4-628d-4f63-be13-88e534f297d3", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "!wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2021-09-13 02:02:56-- https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.141.128, 142.251.2.128, 74.125.137.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.141.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 992539 (969K) [application/octet-stream]\n", "Saving to: ‘parenthood.json.gz’\n", "\n", "parenthood.json.gz 100%[===================>] 969.28K --.-KB/s in 0.007s \n", "\n", "2021-09-13 02:02:57 (138 MB/s) - ‘parenthood.json.gz’ saved [992539/992539]\n", "\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "1HLvySfK2rFm" }, "source": [ "# Find the most specific label(s) given to each protein.\n", "label_normalizer = parenthood_lib.get_applicable_label_dict('parenthood.json.gz')\n", "price_enzymes_w_annotation = parenthood_lib.filter_labels_to_most_specific(price_enzymes_w_annotation, label_normalizer)" ], "execution_count": 13, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lLJaHyQs-8uF" }, "source": [ "to_examine = price_enzymes_w_annotation.copy()\n", "\n", "# Drop labels where we have > 1 prediction for ease of comparison.\n", "to_examine = to_examine[(to_examine.predicted_label.apply(len) == 1)].copy()\n", "\n", "# Format predicted and true label identically.\n", "to_examine['true_label'] = to_examine.new_annotation.apply(lambda x: re.findall(r'\\(EC (.{1,3}\\..{1,3}\\..{1,3}\\..{1,3})\\)', x)[0])\n", "to_examine['predicted_label'] = to_examine.predicted_label.apply(lambda x: list(x)[0].replace('EC:', ''))" ], "execution_count": 14, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "zGkb5EcX9K7H" }, "source": [ "# Compute agreement and plot" ] }, { "cell_type": "code", "metadata": { "id": "Vy_TURPTRjNS" }, "source": [ "performance_by_level = pd.DataFrame([\n", " evaluation.ec_agreement_for_level(to_examine, 1),\n", " evaluation.ec_agreement_for_level(to_examine, 2),\n", " evaluation.ec_agreement_for_level(to_examine, 3),\n", " evaluation.ec_agreement_for_level(to_examine, 4)\n", "], columns=['Correct', 'Incorrect', 'Not called'], index=range(1, 5))[['Correct','Not called',\"Incorrect\"]]" ], "execution_count": 15, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "7KM1HDf26S76", "outputId": "26ca4ecc-70c4-424b-f3e0-8282b91d87f7", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "print(performance_by_level)" ], "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Correct Not called Incorrect\n", "1 103 0 12\n", "2 91 14 10\n", "3 75 28 12\n", "4 13 79 18\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "bk9F-kntOH1K", "outputId": "712f33da-d724-445b-b096-a69d66235db1", "colab": { "base_uri": "https://localhost:8080/" } }, "source": [ "array = performance_by_level.to_numpy()\n", "\n", "array/array.sum(axis=1,keepdims=True)" ], "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[0.89565217, 0. , 0.10434783],\n", " [0.79130435, 0.12173913, 0.08695652],\n", " [0.65217391, 0.24347826, 0.10434783],\n", " [0.11818182, 0.71818182, 0.16363636]])" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "metadata": { "id": "UIc9Xd2BpT1U" }, "source": [ "performance_by_level['position']=performance_by_level.index\n", "melted=pd.melt(performance_by_level,'position')" ], "execution_count": 18, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VYFnYOHQNXlh" }, "source": [ "from plotnine import *\n", "import matplotlib as mpl\n", "mpl.rcParams['figure.dpi']= 800" ], "execution_count": 19, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "grsYMd29NJYJ", "outputId": "b82569c3-4d69-4790-d166-27789a16eafa", "colab": { "base_uri": "https://localhost:8080/", "height": 464 } }, "source": [ "\n", "melted['variable'] = melted['variable'].astype('category').cat.reorder_categories([\"Incorrect\",\"Not called\",\"Correct\"], ordered=True )\n", "\n", "(ggplot(melted,aes(x=\"position\",fill=\"variable\",y=\"value\"))+\n", "geom_col(color=\"black\", position=\"fill\")+\n", "scale_fill_manual([\"red\",\"lightgray\",\"green\"])+ theme_classic()\n", ")\n" ], "execution_count": 20, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "code", "metadata": { "id": "hilfWfbiNTDA" }, "source": [ "" ], "execution_count": 20, "outputs": [] } ] } ================================================ FILE: colabs/README.md ================================================ ================================================ FILE: colabs/Random_EC.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Random_EC.ipynb", "provenance": [], "collapsed_sections": [ "nHWvlZYR7UAl" ] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "nHWvlZYR7UAl" }, "source": [ "# Setup\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "55knmU-w4OPK" }, "source": [ "## Get files / dependencies" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r0hWX2veJHpI", "outputId": "8e884a4c-1153-4d76-e419-7fc751bd528d" }, "source": [ "%tensorflow_version 1" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n", "You set: `1`. This will be interpreted as: `1.x`.\n", "\n", "\n", "TensorFlow is already loaded. Please restart the runtime to change versions.\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W3Y3LpXhZSWh", "outputId": "4199546c-8528-4d24-abf8-3cd055b6e6af" }, "source": [ "!git clone https://github.com/google-research/proteinfer \n", "\n", "%cd proteinfer\n", "\n", "!pip3 install -qr requirements.txt\n", "\n", "import pandas as pd\n", "import tensorflow\n", "import inference\n", "import parenthood_lib\n", "import baseline_utils,subprocess\n", "import shlex\n", "import tqdm \n", "import sklearn\n", "import numpy as np\n", "import utils\n", "import colab_evaluation\n", "import plotly.express as px\n", "\n", "from plotnine import ggplot, geom_point, geom_ribbon, geom_line, aes, stat_smooth, facet_wrap, xlim,coord_cartesian,theme_bw,labs,ggsave\n" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'proteinfer'...\n", "remote: Enumerating objects: 480, done.\u001b[K\n", "remote: Counting objects: 100% (203/203), done.\u001b[K\n", "remote: Compressing objects: 100% (168/168), done.\u001b[K\n", "remote: Total 480 (delta 94), reused 49 (delta 20), pack-reused 277\u001b[K\n", "Receiving objects: 100% (480/480), 9.83 MiB | 14.32 MiB/s, done.\n", "Resolving deltas: 100% (233/233), done.\n", "/content/proteinfer/proteinfer\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "kapre 0.3.5 requires numpy>=1.18.5, but you have numpy 1.16.2 which is incompatible.\n", "kapre 0.3.5 requires tensorflow>=2.0.0, but you have tensorflow 1.15.4 which is incompatible.\u001b[0m\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "n_XWusDibLE-" }, "source": [ "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\n", "!tar xzf noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140.tar.gz\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_EC/eval_test.fasta" ], "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wgU7aSau4qzg" }, "source": [ "## Load vocabulary and parenthood information" ] }, { "cell_type": "code", "metadata": { "id": "gY6_MDLc4jw0" }, "source": [ "vocab = inference.Inferrer(\n", " 'noxpnd_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-13685140'\n", ").get_variable('label_vocab:0').astype(str)\n", "label_normalizer = parenthood_lib.get_applicable_label_dict(\n", " 'parenthood.json.gz')" ], "execution_count": 17, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "aaSAa1xr5KXD" }, "source": [ "## Define a helper function to download inference results" ] }, { "cell_type": "code", "metadata": { "id": "SGsml7EVbTO9" }, "source": [ "def download_inference_results(run_name):\n", " file_shard_names = [\n", " '-{:05d}-of-00064.predictions.gz'.format(i) for i in range(64)\n", " ]\n", " subprocess.check_output(\n", " shlex.split(f'mkdir -p ./inference_results/{run_name}/'))\n", "\n", " for shard_name in tqdm.tqdm(file_shard_names,\n", " position=0,\n", " desc=\"Downloading\"):\n", " subprocess.check_output(\n", " shlex.split(\n", " f'wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/swissprot_inference_results/{run_name}/{shard_name} -O ./inference_results/{run_name}/{shard_name}'\n", " ))\n", " return\n" ], "execution_count": 18, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "H-yqN7iZR4vK" }, "source": [ "## Downloading predictions and getting them ready for analysis" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RZtlXNu8gKWd", "outputId": "48ab85c9-eb68-4ba5-f669-17b34478cbda" }, "source": [ "min_decision_threshold = 1e-10\n", "download_inference_results(f\"ec_random_test\")\n", "predictions_df = colab_evaluation.get_normalized_inference_results(\n", " \"inference_results/ec_random_test\",\n", " vocab,\n", " label_normalizer,\n", " min_decision_threshold=min_decision_threshold)\n" ], "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Downloading: 100%|██████████| 64/64 [00:22<00:00, 2.74it/s]\n", "100%|██████████| 64/64 [01:41<00:00, 1.54s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bLDLn75zC3Eu", "outputId": "1b98d52e-3696-47a6-c7b6-d530a72f042d" }, "source": [ "test_ground_truth = baseline_utils.load_ground_truth('eval_test.fasta')\n", "ground_truth_df = colab_evaluation.make_tidy_df_from_ground_truth(\n", " test_ground_truth)\n", "del test_ground_truth" ], "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "108578it [00:01, 107862.80it/s]\n", "100%|██████████| 54289/54289 [00:00<00:00, 66162.01it/s]\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "UaR0teacxMH8" }, "source": [ "# Analysis\n", "\n", "Now we can get some statistics about our predictions. Let's start with a simple calculation of precision, recall and F1 for the whole dataset at a threshold of 0.5. " ] }, { "cell_type": "markdown", "metadata": { "id": "FTi3TivFxMIA" }, "source": [ "What happens in different EC classes - is there differential performance?" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 269 }, "id": "eotFz2xxxMIA", "outputId": "9c073254-0bb1-4f71-ffeb-3b28733e080c" }, "source": [ "def get_first_level_of_ec_hierarchy(ec):\n", " ec_group_names = {\n", " \"EC:1\": \"Oxidoreductases\",\n", " \"EC:2\": \"Transferases\",\n", " \"EC:3\": \"Hydrolases\",\n", " \"EC:4\": \"Lyases\",\n", " \"EC:5\": \"Isomerases\",\n", " \"EC:6\": \"Ligases\",\n", " \"EC:7\": \"Translocases\"\n", " }\n", " return ec_group_names[ec.split(\".\")[0]]\n", "\n", "\n", "top_level_ec_grouping = {x: get_first_level_of_ec_hierarchy(x) for x in vocab}\n", "\n", "colab_evaluation.apply_threshold_and_return_stats(\n", " predictions_df, ground_truth_df, grouping=top_level_ec_grouping)" ], "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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grouptpfpfnprecisionrecallf1countproportionproportion_textthreshold
0Hydrolases2154146010630.9790920.9529730.965856226040.21904821.9%0.5
1Isomerases5631491570.9913730.9728750.98203757880.0560905.61%0.5
2Ligases11198251390.9977720.9877390.992730113370.10986310.99%0.5
3Lyases9294803640.9914660.9623110.97667196580.0935939.36%0.5
4Oxidoreductases116412716220.9772500.9492780.963061122630.11883711.88%0.5
5Transferases371434949260.9868750.9756760.981243380690.36891436.89%0.5
6Translocases340226710.9924150.9795570.98594434730.0336563.37%0.5
\n", "
" ], "text/plain": [ " group tp fp ... proportion proportion_text threshold\n", "0 Hydrolases 21541 460 ... 0.219048 21.9% 0.5\n", "1 Isomerases 5631 49 ... 0.056090 5.61% 0.5\n", "2 Ligases 11198 25 ... 0.109863 10.99% 0.5\n", "3 Lyases 9294 80 ... 0.093593 9.36% 0.5\n", "4 Oxidoreductases 11641 271 ... 0.118837 11.88% 0.5\n", "5 Transferases 37143 494 ... 0.368914 36.89% 0.5\n", "6 Translocases 3402 26 ... 0.033656 3.37% 0.5\n", "\n", "[7 rows x 11 columns]" ] }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 560 }, "id": "HJGDt1wG1wiR", "outputId": "6675200c-f187-4e2e-a4db-a0b9b83331e6" }, "source": [ "ec_pr_curves = colab_evaluation.get_pr_curve_df(\n", " predictions_df, ground_truth_df, grouping=top_level_ec_grouping)\n", "\n", "fig = px.line(ec_pr_curves, \n", " x=\"recall\", y=\"precision\", color=\"group\")\n", "fig.update_layout(template=\"plotly_white\", title=\"Performance by EC class\")\n", "\n", "\n", "fig.update_yaxes(range=(0.92, 1))" ], "execution_count": 22, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 7/7 [00:00<00:00, 36.80it/s]\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "S354lNUzxMIB" }, "source": [ "And what about at different levels of the EC hierarchy?" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 464 }, "id": "HOTqUhqyxMIC", "outputId": "24cb0996-ffd4-47c1-a35d-6616169ac7e5" }, "source": [ "def get_level_of_hierarchy(ec):\n", " num_of_dashes = ec.count(\"-\")\n", " level_of_hierarchy = 4 - num_of_dashes\n", " return level_of_hierarchy\n", "\n", "\n", "level_of_hierachy_grouping = {x: get_level_of_hierarchy(x) for x in vocab}\n", "\n", "level_data = colab_evaluation.apply_threshold_and_return_stats(\n", " predictions_df, ground_truth_df, grouping=level_of_hierachy_grouping)\n", "ggplot(level_data, aes(x=\"group\", y=\"f1\")) + geom_point() + geom_point(\n", ") + geom_line() + theme_bw() + labs(\n", " x=\"Level of hierarchy\", y=\"F1 score\") + coord_cartesian(ylim=[0.95, 0.99])" ], "execution_count": 23, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 23 } ] }, { "cell_type": "markdown", "metadata": { "id": "69I2wI1oxMIC" }, "source": [ "Now let's try varying the threshold to generate a precision-recall curve." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z9YIbO0dxMIC", "outputId": "d8c1c74b-a79b-453b-c07a-d0c3d9ba9217" }, "source": [ "cnn_pr_data = colab_evaluation.get_pr_curve_df(predictions_df, ground_truth_df)\n" ], "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1/1 [00:00<00:00, 6.97it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 464 }, "id": "2hmFNu1oxMIC", "outputId": "b71a7294-7ed9-47ac-c9a3-0f15c3dad803" }, "source": [ "ggplot(cnn_pr_data.drop(index=0),\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"f1\")) + geom_line() + geom_line() + coord_cartesian(\n", " xlim=(0.96, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"F1 Score\")\n" ], "execution_count": 25, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "markdown", "metadata": { "id": "40A0Yx1NxMID" }, "source": [ "What decision threshold maximises F1 score?" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 144 }, "id": "R_6fybn3xMID", "outputId": "9cc21ac7-2b7f-444f-f27a-be99542905e2" }, "source": [ "cnn_pr_data.sort_values('f1', ascending=False)[:3]\n" ], "execution_count": 26, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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groupprecisionrecallthresholdf1
177all0.9865680.9673230.6252050.976851
176all0.9863850.9674300.5900150.976815
178all0.9865770.9672160.6392630.976801
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" ], "text/plain": [ " group precision recall threshold f1\n", "177 all 0.986568 0.967323 0.625205 0.976851\n", "176 all 0.986385 0.967430 0.590015 0.976815\n", "178 all 0.986577 0.967216 0.639263 0.976801" ] }, "metadata": {}, "execution_count": 26 } ] }, { "cell_type": "markdown", "metadata": { "id": "9H9UiNZXxMIE" }, "source": [ "Now let's have a look at PR curves for each different top level group." ] }, { "cell_type": "markdown", "metadata": { "id": "G4aq7gtaAQmx" }, "source": [ "# Load CNN ensemble predictions" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EtefjIEV_Qut", "outputId": "0d858daa-89ed-4eff-a347-f6540602ed73" }, "source": [ "min_decision_threshold = 1e-10\n", "download_inference_results(f\"ec_random_test_ens\")\n", "ens_predictions_df = colab_evaluation.get_normalized_inference_results(\n", " \"inference_results/ec_random_test_ens\",\n", " vocab,\n", " label_normalizer,\n", " min_decision_threshold=min_decision_threshold)\n" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Downloading: 100%|██████████| 64/64 [00:34<00:00, 1.97it/s]\n", "100%|██████████| 64/64 [01:50<00:00, 1.69s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JLVF_tY8DVZx", "outputId": "9bfbebad-508d-4d94-e61b-71d7cdfa5dd7" }, "source": [ "ens_cnn_pr_data = colab_evaluation.get_pr_curve_df(ens_predictions_df,\n", " ground_truth_df)\n" ], "execution_count": 28, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1/1 [00:00<00:00, 1.50it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 144 }, "id": "c9-YYhgeKoPX", "outputId": "fb2c380f-383b-46d0-e93c-bb248ad7fe47" }, "source": [ "ens_cnn_pr_data.sort_values('f1', ascending=False)[0:3]\n" ], "execution_count": 29, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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groupprecisionrecallthresholdf1
170all0.9856780.9757350.2537790.980681
171all0.9857830.9756280.2573990.980679
176all0.9864210.9749980.2748640.980676
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" ], "text/plain": [ " group precision recall threshold f1\n", "170 all 0.985678 0.975735 0.253779 0.980681\n", "171 all 0.985783 0.975628 0.257399 0.980679\n", "176 all 0.986421 0.974998 0.274864 0.980676" ] }, "metadata": {}, "execution_count": 29 } ] }, { "cell_type": "code", "metadata": { "id": "6NvGPanNwkRy" }, "source": [ "cnn_pr_data['method'] = \"CNN\"\n", "ens_cnn_pr_data['method'] = \"CNN Ensemble\"" ], "execution_count": 30, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533 }, "id": "BKUHnc3qwsBp", "outputId": "3377e631-29ba-4189-8f00-56c04fd56883" }, "source": [ "method_comparison = pd.concat([cnn_pr_data, ens_cnn_pr_data],\n", " ignore_index=True)\n", "ggplot(method_comparison,\n", " aes(x=\"recall\", y=\"precision\", color=\"method\",\n", " linetype=\"method\")) + geom_line() + coord_cartesian(\n", " xlim=(0.91, 1), ylim=(0.91, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\")\n" ], "execution_count": 31, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning:\n", "\n", "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", "\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 31 } ] }, { "cell_type": "markdown", "metadata": { "id": "aSOdgXSpHiYx" }, "source": [ "# Blast comparison" ] }, { "cell_type": "markdown", "metadata": { "id": "vvFBBYlmToXe" }, "source": [ "Let's do the same sort of analysis for a BLAST baseline." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VXqzzmJOHjr8", "outputId": "e5a224a7-ae9e-4852-9f9d-5222fe6ff09c" }, "source": [ "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/blast_output/random/blast_out_test.tsv\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_EC/eval_test.fasta\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_EC/train.fasta\n", "train_ground_truth = colab_evaluation.make_tidy_df_from_ground_truth(baseline_utils.load_ground_truth('train.fasta')).rename(columns={\"up_id\":\"train_seq_id\"}).drop(columns=[\"gt\"])" ], "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "877044it [00:07, 123593.34it/s]\n", "100%|██████████| 438522/438522 [00:06<00:00, 66985.53it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "NwD3Ers7IIBe" }, "source": [ "blast_out = colab_evaluation.read_blast_table(\"blast_out_test.tsv\")\n", "blast_df = blast_out.merge(train_ground_truth,\n", " left_on=\"target\",\n", " right_on=\"train_seq_id\")\n", "blast_df.rename(columns={'bit_score': 'value', \"query\": \"up_id\"}, inplace=True)\n" ], "execution_count": 33, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kcqNR0hQJwHv", "outputId": "b02e334c-45ed-497a-8c31-c83ccb95d7cc" }, "source": [ "min_decision_threshold = 0\n", "blast_pr_data = colab_evaluation.get_pr_curve_df(blast_df, ground_truth_df)\n", "blast_pr_data['method'] = 'BLAST'" ], "execution_count": 34, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1/1 [00:00<00:00, 12.64it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533 }, "id": "2IIjwAbrOmTT", "outputId": "caac3df7-f211-4a06-efbe-7c3d643c4c40" }, "source": [ "cnn_pr_data['method'] = 'CNN'\n", "ens_cnn_pr_data['method'] = 'Ensembled CNN'\n", "method_comparison = pd.concat([\n", " cnn_pr_data.drop(index=0),\n", " ens_cnn_pr_data.drop(index=0),\n", " blast_pr_data.drop(index=0)\n", "],\n", " ignore_index=True)\n", "ggplot(method_comparison,\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"method\")) + geom_line() + coord_cartesian(\n", " xlim=(0.90, 1), ylim=(0.90, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\")\n" ], "execution_count": 35, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning:\n", "\n", "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", "\n" ] }, { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 174 }, "id": "DdPDKvc7axjD", "outputId": "b7a8d32d-e67b-4465-c140-c283cf3de0a4" }, "source": [ "method_comparison.groupby(\"method\")[['f1']].agg(max)" ], "execution_count": 36, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
f1
method
BLAST0.984229
CNN0.976851
Ensembled CNN0.980681
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" ], "text/plain": [ " f1\n", "method \n", "BLAST 0.984229\n", "CNN 0.976851\n", "Ensembled CNN 0.980681" ] }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 144 }, "id": "JoW4Rg7Ln2Bd", "outputId": "d66337a6-2aa3-4896-b2df-542e272201ef" }, "source": [ "method_comparison.sort_values('f1',\n", " ascending=False).drop_duplicates(['method'])\n" ], "execution_count": 37, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
groupprecisionrecallthresholdf1method
1169all0.9827170.98574560.5000000.984229BLAST
508all0.9856780.9757350.2537790.980681Ensembled CNN
176all0.9865680.9673230.6252050.976851CNN
\n", "
" ], "text/plain": [ " group precision recall threshold f1 method\n", "1169 all 0.982717 0.985745 60.500000 0.984229 BLAST\n", "508 all 0.985678 0.975735 0.253779 0.980681 Ensembled CNN\n", "176 all 0.986568 0.967323 0.625205 0.976851 CNN" ] }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "markdown", "metadata": { "id": "_VyNMIBZcAfk" }, "source": [ "Let's investigate what's going on at the left hand side of the graph where the CNN and ensemble achieve greater precision than BLAST." ] }, { "cell_type": "code", "metadata": { "id": "UQ0as8vXMNEW" }, "source": [ "def get_x_where_y_is_closest_to_z(df, x, y, z):\n", " return df.iloc[(df[y] - z).abs().argsort()[:1]][x]\n", "\n", "\n", "cnn_threshold = float(\n", " get_x_where_y_is_closest_to_z(cnn_pr_data,\n", " x=\"threshold\",\n", " y=\"recall\",\n", " z=0.96))\n", "blast_threshold = float(\n", " get_x_where_y_is_closest_to_z(blast_pr_data,\n", " x=\"threshold\",\n", " y=\"recall\",\n", " z=0.96))\n", "\n", "cnn_results = colab_evaluation.assign_tp_fp_fn(ens_predictions_df,\n", " ground_truth_df, cnn_threshold)\n", "\n", "blast_results = colab_evaluation.assign_tp_fp_fn(blast_df, ground_truth_df,\n", " blast_threshold)\n", "\n", "merged = cnn_results.merge(blast_results,\n", " how=\"outer\",\n", " suffixes=(\"_ens_cnn\", \"_blast\"),\n", " left_on=[\"label\", \"up_id\", \"gt\"],\n", " right_on=[\"label\", \"up_id\", \"gt\"])\n" ], "execution_count": 38, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "v4f6jjepehLW" }, "source": [ "blast_info = blast_out[['up_id', 'target', 'pc_identity']]\n" ], "execution_count": 39, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PF2TB_jNomft" }, "source": [ "Let's list some of the BLAST false-positives in case we want to investigate what's going on." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "fchf2u1wdihM", "outputId": "c8198c91-6576-4d87-f74b-85155a8bfd89" }, "source": [ "merged.query(\"fp_blast==True and fp_ens_cnn==False\").head()" ], "execution_count": 40, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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up_idlabelvalue_ens_cnngttp_ens_cnnfp_ens_cnnfn_ens_cnntargetpc_identityalignment_lengthvalue_blasttrain_seq_idtp_blastfp_blastfn_blast
1489Q9VXP4EC:3.-.-.-0.831271FalseFalseFalseFalseO3526448.416114229O35264FalseTrueFalse
1490Q9VXP4EC:3.1.-.-0.831271FalseFalseFalseFalseO3526448.416114229O35264FalseTrueFalse
1491Q9VXP4EC:3.1.1.-0.089787FalseFalseFalseFalseO3526448.416114229O35264FalseTrueFalse
1492Q9VXP4EC:3.1.1.470.089787FalseFalseFalseFalseO3526448.416114229O35264FalseTrueFalse
2004Q9SP62EC:1.14.19.60.0393828FalseFalseFalseFalseQ8GZC363.859131514Q8GZC3FalseTrueFalse
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" ], "text/plain": [ " up_id label value_ens_cnn ... tp_blast fp_blast fn_blast\n", "1489 Q9VXP4 EC:3.-.-.- 0.831271 ... False True False\n", "1490 Q9VXP4 EC:3.1.-.- 0.831271 ... False True False\n", "1491 Q9VXP4 EC:3.1.1.- 0.089787 ... False True False\n", "1492 Q9VXP4 EC:3.1.1.47 0.089787 ... False True False\n", "2004 Q9SP62 EC:1.14.19.6 0.0393828 ... False True False\n", "\n", "[5 rows x 15 columns]" ] }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "markdown", "metadata": { "id": "kXvgYdoIEC8x" }, "source": [ "# An ensemble of BLAST and ensembled-CNNs\n", "\n", "We've seen that the CNN-ensemble and BLAST have different strengths - at lower recalls the CNN appears to have greater precision than BLAST at lower recalls, but BLAST has better recall at lower precisions. Can we combine these approaches to get a predictor with the best of both worlds?" ] }, { "cell_type": "code", "metadata": { "id": "8c3FW0x6GgR5" }, "source": [ "blast_and_cnn_ensemble = ens_predictions_df.merge(blast_df,\n", " how=\"outer\",\n", " suffixes=(\"_ens_cnn\",\n", " \"_blast\"),\n", " left_on=[\"label\", \"up_id\"],\n", " right_on=[\"label\", \"up_id\"])\n" ], "execution_count": 41, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "WqkATRN3GqDE" }, "source": [ "blast_and_cnn_ensemble = blast_and_cnn_ensemble.fillna(False)\n" ], "execution_count": 42, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "QNO1n03TJp6z" }, "source": [ "We will create a simple ensemble where the value of the predictor is simply the multiple of the probability assigned by the ensemble of neural networks and the bit-score linking this sequence to to an example with this label by BLAST." ] }, { "cell_type": "code", "metadata": { "id": "8FbzU_6QGvZS" }, "source": [ "blast_and_cnn_ensemble['value'] = blast_and_cnn_ensemble[\n", " 'value_ens_cnn'] * blast_and_cnn_ensemble['value_blast']\n" ], "execution_count": 43, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ahX0RAesG6Nb", "outputId": "41d65006-4d07-4c76-8ea4-ba5253691c90" }, "source": [ "blast_and_cnn_ensemble_pr = colab_evaluation.get_pr_curve_df(\n", " blast_and_cnn_ensemble, ground_truth_df)" ], "execution_count": 44, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1/1 [00:00<00:00, 3.54it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "He1PKIk2wfes", "outputId": "c1a7f035-eccd-413e-bee6-f2f608b357b9" }, "source": [ "blast_and_cnn_ensemble_pr.f1.max()" ], "execution_count": 45, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.9863972357083497" ] }, "metadata": {}, "execution_count": 45 } ] }, { "cell_type": "code", "metadata": { "id": "JjVIWQabHI9z" }, "source": [ "blast_and_cnn_ensemble_pr['method'] = 'Ensemble of BLAST with Ensembled-CNN'\n" ], "execution_count": 46, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533 }, "id": "BXLI2DaoHIRd", "outputId": "e1ee8ca4-36c2-4da6-8dc7-54e50fc3e824" }, "source": [ "cnn_pr_data['method'] = 'CNN'\n", "ens_cnn_pr_data['method'] = 'Ensembled CNN'\n", "method_comparison = pd.concat([\n", " cnn_pr_data.drop(index=0),\n", " ens_cnn_pr_data.drop(index=0),\n", " blast_pr_data.drop(index=0),\n", " blast_and_cnn_ensemble_pr.drop(index=0)\n", "],\n", " ignore_index=True)\n", "ggplot(method_comparison,\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"method\")) + geom_line() + coord_cartesian(\n", " xlim=(0.93, 1), ylim=(0.93, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\") \n" ], "execution_count": 47, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning:\n", "\n", "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", "\n" ] }, { "output_type": "display_data", "data": { "image/png": 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f1
method
BLAST0.984229
CNN0.976851
Ensemble of BLAST with Ensembled-CNN0.986397
Ensembled CNN0.980681
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" ], "text/plain": [ " f1\n", "method \n", "BLAST 0.984229\n", "CNN 0.976851\n", "Ensemble of BLAST with Ensembled-CNN 0.986397\n", "Ensembled CNN 0.980681" ] }, "metadata": {}, "execution_count": 49 } ] }, { "cell_type": "markdown", "metadata": { "id": "Nz4dbIOk0M9a" }, "source": [ "# Bootstrapping" ] }, { "cell_type": "markdown", "metadata": { "id": "gx8A9-eZMT-9" }, "source": [ "## Defining functions" ] }, { "cell_type": "code", "metadata": { "id": "6ZrJzUIVcjm6" }, "source": [ "import collections\n", "\n", "\n", "def get_bootstrapped_pr_curves(predictions_df,\n", " ground_truth_df,\n", " grouping=None,\n", " n=100,\n", " method_label=None,\n", " sample_with_replacement=True):\n", "\n", " joined = predictions_df[predictions_df.value > 1e-10].merge(\n", " ground_truth_df, on=['up_id', 'label'], how='outer')\n", " unique_up_ids = joined['up_id'].unique()\n", "\n", " pr_samples = []\n", " for _ in tqdm.tqdm(range(n)):\n", " sampled_up_ids = np.random.choice(unique_up_ids, len(unique_up_ids),\n", " sample_with_replacement)\n", "\n", " count_by_sample = collections.Counter(sampled_up_ids)\n", " count_by_sample_ordered = [count_by_sample[x] for x in joined.up_id]\n", " joined_sampled = pd.DataFrame(np.repeat(joined.values,\n", " count_by_sample_ordered,\n", " axis=0),\n", " columns=joined.columns)\n", " unique_suffixes_counter = collections.defaultdict(lambda: 0)\n", " unique_suffixes = []\n", " for row in joined_sampled.values:\n", " lookup_key = (row[0], row[1])\n", " unique_suffixes.append(unique_suffixes_counter[lookup_key])\n", " unique_suffixes_counter[lookup_key] += 1\n", "\n", " joined_sampled['up_id'] = [\n", " f'{x}-{y}' for x, y in zip(joined_sampled.up_id, unique_suffixes)\n", " ]\n", "\n", " pred = joined_sampled[joined_sampled['value'].notna()][[\n", " 'up_id', 'label', 'value'\n", " ]]\n", " gt = joined_sampled[joined_sampled['gt'].notna()][[\n", " 'up_id', 'label', 'gt'\n", " ]]\n", "\n", " pr_curves = colab_evaluation.get_pr_curve_df(pred,\n", " gt,\n", " grouping=grouping)\n", " pr_curves.loc[pr_curves['threshold'] == 0.0, 'precision'] = 0\n", " pr_curves.loc[pr_curves['threshold'] == 0.0, 'f1'] = 0\n", " pr_curves['type'] = method_label\n", " pr_samples.append(pr_curves)\n", " return pr_samples\n" ], "execution_count": 50, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "WaSoUBt6alY0" }, "source": [ "## Perform calculations" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oiUrFouj0qKF", "outputId": "c0a03cab-13a2-431a-9dd9-bbb438df6ede" }, "source": [ "n = 100\n", "non_ensembled_prs = get_bootstrapped_pr_curves(predictions_df,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"Single CNN\")\n", "\n", "ensembled_prs = get_bootstrapped_pr_curves(ens_predictions_df,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"Ensemble of CNNs\")\n", "\n", "blast_prs = get_bootstrapped_pr_curves(blast_df,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"BLAST\")\n", " \n", "blast_and_cnn_ensemble_prs = get_bootstrapped_pr_curves(\n", " blast_and_cnn_ensemble,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"Blast/CNN-Ensemble\")\n" ], "execution_count": 51, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 1/1 [00:00<00:00, 8.11it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.68it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.36it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.30it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.22it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.54it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.27it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.47it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.33it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.52it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.31it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.91it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 7.00it/s]\n", "100%|██████████| 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create_interpolated_df(single_curve):\n", " interp_recall_fn = interp1d(single_curve.recall,\n", " single_curve.precision,\n", " bounds_error=False)\n", " recall = np.linspace(0.0, 1, 5001)\n", " interpolated_precisions = interp_recall_fn(recall)\n", " return pd.DataFrame({\n", " \"type\": single_curve.type.to_list()[0],\n", " \"group\": single_curve.group.to_list()[0],\n", " \"precision\": interpolated_precisions,\n", " \"recall\": recall\n", " })\n" ], "execution_count": 52, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "pnHKZRColhaK" }, "source": [ "curves = [\n", " ensembled_prs, non_ensembled_prs, blast_and_cnn_ensemble_prs, blast_prs\n", "]\n", "dfs = []\n", "\n", "for curve_set in curves:\n", " for c2 in curve_set:\n", " for group_name, df_group in c2.groupby(\"group\"):\n", " dfs.append(create_interpolated_df(df_group.query(\"precision>0\")))\n", "all = pd.concat(dfs)\n" ], "execution_count": 53, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Lhwtx2mWlb65" }, "source": [ "" ], "execution_count": 53, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 174 }, "id": "OJNtsQOcnlQE", "outputId": "955c8bbc-b790-4d76-8445-4e4244f1e257" }, "source": [ "curves = [\n", " ensembled_prs, non_ensembled_prs, blast_and_cnn_ensemble_prs, blast_prs\n", "]\n", "dfs = []\n", "\n", "\n", "def create_f1(single_curve):\n", "\n", " return pd.DataFrame(\n", " {\n", " \"type\": single_curve.type.to_list()[0],\n", " \"group\": single_curve.group.to_list()[0],\n", " \"f1\": single_curve.f1.max()\n", " },\n", " index=[0])\n", "\n", "\n", "for curve_set in curves:\n", " for c2 in curve_set:\n", " for group_name, df_group in c2.groupby(\"group\"):\n", " dfs.append(create_f1(df_group))\n", "f1 = pd.concat(dfs)\n", "\n", "\n", "def lower_func(x):\n", " return x.quantile(0.025)\n", "\n", "\n", "def upper_func(x):\n", " return x.quantile(0.975)\n", "\n", "\n", "f1_data = f1.groupby(['type',\n", " 'group']).agg(lower=(\"f1\", lower_func),\n", " upper=(\"f1\", upper_func)).reset_index()\n", "f1_data" ], "execution_count": 54, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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typegrouplowerupper
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1Blast/CNN-Ensembleall0.9855180.987427
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typegroupf1
0Ensemble of CNNsall0.981121
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" ], "text/plain": [ " type group f1\n", "0 Ensemble of CNNs all 0.981121\n", "0 Ensemble of CNNs all 0.980834\n", "0 Ensemble of CNNs all 0.980514\n", "0 Ensemble of CNNs all 0.980314\n", "0 Ensemble of CNNs all 0.980272\n", ".. ... ... ...\n", "0 BLAST all 0.984065\n", "0 BLAST all 0.983924\n", "0 BLAST all 0.984716\n", "0 BLAST all 0.983924\n", "0 BLAST all 0.984429\n", "\n", "[400 rows x 3 columns]" ] }, "metadata": {}, "execution_count": 55 } ] }, { "cell_type": "code", "metadata": { "id": "0YIkZtjeoJkU" }, "source": [ "def lower_func(x):\n", " return x.quantile(0.025)\n", "\n", "\n", "def upper_func(x):\n", " return x.quantile(0.975)\n", "\n", "\n", "for_graph = all.groupby(['type', 'group', 'recall'\n", " ]).agg(lower=(\"precision\", lower_func),\n", " upper=(\"precision\", upper_func)).reset_index()\n" ], "execution_count": 56, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4ARrq09Kr8cM" }, "source": [ "a = get_bootstrapped_pr_curves(predictions_df,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"Single CNN\")[0]\n", "b = get_bootstrapped_pr_curves(ens_predictions_df,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"Ensemble of CNNs\")[0]\n", "c = get_bootstrapped_pr_curves(blast_df,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"BLAST\")[0]\n", "d = get_bootstrapped_pr_curves(blast_and_cnn_ensemble,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"Blast/CNN-Ensemble\")[0]\n", "all_single = pd.concat([a, b, c, d]).query(\"precision>0\")\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "T_d5s8JGYRnF" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Xs85vkjhbD-t" }, "source": [ "## Plot bootstrap curves" ] }, { "cell_type": "code", "metadata": { "id": "Ii8vVgD9omWs" }, "source": [ "import plotly.graph_objects as go\n", "\n", "fig = go.Figure()\n", "\n", "\n", "def get_color(index, transparent):\n", " colors = {\n", " 'Single CNN': [150, 0, 0],\n", " 'Ensemble of CNNs': [0, 125, 125],\n", " 'Blast/CNN-Ensemble': [0, 200, 0],\n", " 'BLAST': [125, 0, 255]\n", " }\n", " transparency = 0.2 if transparent else 1\n", " return f\"rgba({colors[index][0]}, {colors[index][1]}, {colors[index][2]}, {transparency})\"\n", "\n", "for the_type, new in for_graph.groupby('type'):\n", " fig.add_trace(\n", " go.Scatter(\n", " x=new['recall'],\n", " y=new['upper'],\n", " mode='lines',\n", " showlegend=False,\n", " line=dict(width=0.0, color=get_color(the_type, False)),\n", " name=\"\",\n", " hoverinfo='skip',\n", " ))\n", " fig.add_trace(\n", " go.Scatter(\n", " x=new['recall'],\n", " y=new['lower'],\n", " name=the_type,\n", " hoverinfo='skip',\n", " showlegend=False,\n", " line=dict(width=0.0, color=get_color(the_type, False)),\n", " fill='tonexty',\n", " fillcolor=get_color(the_type, True),\n", " ))\n", "\n", "for the_type, new in all_single.groupby('type'):\n", " fig.add_trace(\n", " go.Scatter(x=new['recall'],\n", " y=new['precision'],\n", " name=the_type,\n", " line=dict(width=1, color=get_color(the_type, False))))\n", "\n", "fig.update_xaxes(title=\"Recall\", range=[0.90, 1])\n", "fig.update_yaxes(title=\"Precision\", range=[0.90, 1])\n", "fig.update_layout(template=\"plotly_white\")\n", "fig.update_layout(legend_title_text='Method')\n", "\n", "fig.update_layout(title=\"Precision and recall by method\", )\n", "\n", "fig.show()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "3ZUU00ABF7gL" }, "source": [ "fig=ggplot(all_single,\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"type\")) + geom_line() + coord_cartesian(\n", " xlim=(0.9, 1), ylim=(0.9, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\",fill=\"Method\") +geom_ribbon(aes(ymin=\"lower\",ymax=\"upper\",y=\"lower\",fill=\"type\"),data=for_graph,color=None,alpha=0.25)\n", "fig" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "8VeuukDTnn2B" }, "source": [ "ggsave(fig,\"random_truncated.pdf\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "kn5RV7p2oK36" }, "source": [ "fig=ggplot(all_single,\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"type\")) + geom_line() + coord_cartesian(\n", " xlim=(0.0, 1), ylim=(0.0, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\",fill=\"Method\") +geom_ribbon(aes(ymin=\"lower\",ymax=\"upper\",y=\"lower\",fill=\"type\"),data=for_graph,color=None,alpha=0.25)\n", "fig\n", "\n", "ggsave(fig,\"random_untruncated.pdf\")" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 174 }, "id": "9MqxNw30LUWQ", "outputId": "d23c54dd-0822-4ea6-8696-0345d0633f45" }, "source": [ "curves = [\n", " ensembled_prs, non_ensembled_prs, blast_and_cnn_ensemble_prs, blast_prs\n", "]\n", "dfs = []\n", "\n", "\n", "def create_f1(single_curve):\n", "\n", " return pd.DataFrame(\n", " {\n", " \"type\": single_curve.type.to_list()[0],\n", " \"group\": single_curve.group.to_list()[0],\n", " \"f1\": single_curve.f1.max()\n", " },\n", " index=[0])\n", "\n", "\n", "for curve_set in curves:\n", " for c2 in curve_set:\n", " for group_name, df_group in c2.groupby(\"group\"):\n", " dfs.append(create_f1(df_group.query(\"precision>0\")))\n", "f1 = pd.concat(dfs)\n", "\n", "\n", "def lower_func(x):\n", " return x.quantile(0.025)\n", "\n", "\n", "def upper_func(x):\n", " return x.quantile(0.975)\n", "\n", "\n", "f1_data = f1.groupby(['type',\n", " 'group']).agg(lower=(\"f1\", lower_func),\n", " upper=(\"f1\", upper_func)).reset_index()\n", "f1_data" ], "execution_count": 62, "outputs": [ { "output_type": "execute_result", "data": { 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typegrouplowerupper
0BLASTall0.9832760.985455
1Blast/CNN-Ensembleall0.9855180.987427
2Ensemble of CNNsall0.9795170.981811
3Single CNNall0.9756840.977731
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" ], "text/plain": [ " type group lower upper\n", "0 BLAST all 0.983276 0.985455\n", "1 Blast/CNN-Ensemble all 0.985518 0.987427\n", "2 Ensemble of CNNs all 0.979517 0.981811\n", "3 Single CNN all 0.975684 0.977731" ] }, "metadata": {}, "execution_count": 62 } ] }, { "cell_type": "markdown", "metadata": { "id": "0q_aYzUWxMIL" }, "source": [ "## Examine effect of number of training examples on performance\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "2IwNqpUVxMIM" }, "source": [ "def resample_with_replacement(df):\n", " indices = np.random.randint(0, df.shape[0], df.shape[0])\n", " return df.iloc[indices, :]\n", "\n", "\n", "def bootstrap(df, n=100):\n", " resampled_results = []\n", " for x in tqdm.tqdm(range(n), position=0):\n", " resampled = resample_with_replacement(df)\n", " data = colab_evaluation.stats_by_group(resampled.groupby('count_cut'))\n", " resampled_results.append(data)\n", " return pd.concat(resampled_results)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "T5okaIZSxMIM" }, "source": [ "train_counts = train_ground_truth.groupby(\n", " \"label\", as_index=False).count().rename(columns={\"train_seq_id\": \"count\"})\n" ], "execution_count": 64, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t1FeqUo1xMIM", "outputId": "b8aaa974-ce04-4bb1-82e7-ddd9a9463985" }, "source": [ "both = colab_evaluation.assign_tp_fp_fn(predictions_df, ground_truth_df,\n", " 0.625205)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "bootstrapped_data = bootstrap(both, n=5)\n", "bootstrapped_data['count_cut_str'] = bootstrapped_data['count_cut'].astype(str)" ], "execution_count": 65, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 5/5 [00:00<00:00, 6.08it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 594 }, "id": "VD9BtlLoT4bq", "outputId": "384cd07e-850f-4263-a85c-20bf730fa17a" }, "source": [ "bootstrapped_data['type'] = \"CNN\"\n", "both = colab_evaluation.assign_tp_fp_fn(blast_df, ground_truth_df, 60.5)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "\n", "bootstrapped_data_blast = bootstrap(both, n=100)\n", "bootstrapped_data_blast['type'] = \"BLAST\"\n", "both = colab_evaluation.assign_tp_fp_fn(ens_predictions_df, ground_truth_df,\n", " 0.25)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "\n", "bootstrapped_data_ens = bootstrap(both, n=100)\n", "bootstrapped_data_ens['type'] = \"Ensembled CNNs\"\n", "both = colab_evaluation.assign_tp_fp_fn(blast_and_cnn_ensemble,\n", " ground_truth_df, 0.17)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "\n", "bootstrapped_data_combo = bootstrap(both, n=100)\n", "bootstrapped_data_combo['type'] = \"Ensembled CNNs with BLAST\"\n", "bootstrapped_merge = pd.concat([\n", " bootstrapped_data_blast, bootstrapped_data, bootstrapped_data_ens,\n", " bootstrapped_data_combo\n", "],\n", " ignore_index=True)\n", "\n", "bootstrapped_merge['count_cut_str'] = bootstrapped_merge['count_cut'].astype(\n", " str)\n", "fig = px.box(bootstrapped_merge,\n", " width=700,\n", " color=\"type\",\n", " x=\"count_cut_str\",\n", " y=\"f1\",\n", " labels={\n", " \"count_cut_str\": \"Number of training examples per label\",\n", " \"f1\": \"F1\"\n", " },\n", " template=\"simple_white\")\n", "fig.show()\n" ], "execution_count": 66, "outputs": [ { "metadata": { "tags": null }, "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:14<00:00, 6.92it/s]\n", "100%|██████████| 100/100 [00:28<00:00, 3.52it/s]\n", "100%|██████████| 100/100 [00:45<00:00, 2.19it/s]\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "" ] }, "metadata": {}, "output_type": "display_data" } ] } ] } ================================================ FILE: colabs/Random_GO.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Random_GO.ipynb", "provenance": [], "collapsed_sections": [ "55knmU-w4OPK", "wgU7aSau4qzg", "aaSAa1xr5KXD", "H-yqN7iZR4vK" ], "machine_shape": "hm" }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "nHWvlZYR7UAl" }, "source": [ "# Setup\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "55knmU-w4OPK" }, "source": [ "## Get files / dependencies" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r0hWX2veJHpI", "outputId": "16ef4af3-3675-4d80-895b-a01eafdfb12c" }, "source": [ "%tensorflow_version 1" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n", "You set: `1`. This will be interpreted as: `1.x`.\n", "\n", "\n", "TensorFlow 1.x selected.\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2rI1fxA1JuaM", "outputId": "0cbb609a-5f92-4aa6-d018-86fab83ae2b6" }, "source": [ "!python --version" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 3.7.11\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W3Y3LpXhZSWh", "outputId": "284ebc04-5152-4670-ac9f-b4aa2c59dc46" }, "source": [ "!git clone https://github.com/google-research/proteinfer \n", "\n", "%cd proteinfer\n", "\n", "!pip3 install -qr requirements.txt\n", "\n", "import pandas as pd\n", "import tensorflow\n", "import inference\n", "import parenthood_lib\n", "import baseline_utils,subprocess\n", "import shlex\n", "import tqdm \n", "import sklearn\n", "import numpy as np\n", "import utils\n", "import colab_evaluation\n", "import plotly.express as px\n", "\n", "from plotnine import ggplot, geom_point, geom_point, geom_line, aes, stat_smooth, facet_wrap, xlim,coord_cartesian,theme_bw,labs,ggsave\n" ], "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'proteinfer'...\n", "remote: Enumerating objects: 473, done.\u001b[K\n", "remote: Counting objects: 100% (196/196), done.\u001b[K\n", "remote: Compressing objects: 100% (161/161), done.\u001b[K\n", "remote: Total 473 (delta 91), reused 49 (delta 20), pack-reused 277\u001b[K\n", "Receiving objects: 100% (473/473), 9.11 MiB | 14.42 MiB/s, done.\n", "Resolving deltas: 100% (230/230), done.\n", "/content/proteinfer\n", "\u001b[K |████████████████████████████████| 99 kB 3.6 MB/s \n", "\u001b[K |████████████████████████████████| 2.3 MB 14.2 MB/s \n", "\u001b[K |████████████████████████████████| 10.8 MB 18.5 MB/s \n", "\u001b[K |████████████████████████████████| 2.8 MB 52.7 MB/s \n", "\u001b[K |████████████████████████████████| 59 kB 4.9 MB/s \n", "\u001b[K |████████████████████████████████| 89 kB 5.7 MB/s \n", "\u001b[K |████████████████████████████████| 56 kB 3.9 MB/s \n", "\u001b[K |████████████████████████████████| 17.3 MB 23.0 MB/s \n", "\u001b[K |████████████████████████████████| 10.5 MB 50.1 MB/s \n", "\u001b[K |████████████████████████████████| 107 kB 70.6 MB/s \n", "\u001b[K |████████████████████████████████| 13.1 MB 40.4 MB/s \n", "\u001b[K |████████████████████████████████| 4.4 MB 52.3 MB/s \n", "\u001b[K |████████████████████████████████| 226 kB 54.2 MB/s \n", "\u001b[K |████████████████████████████████| 6.8 MB 51.5 MB/s \n", "\u001b[K |████████████████████████████████| 110.5 MB 27 kB/s \n", "\u001b[K |████████████████████████████████| 411.0 MB 21 kB/s \n", "\u001b[K |████████████████████████████████| 103 kB 57.7 MB/s \n", "\u001b[K |████████████████████████████████| 45 kB 2.7 MB/s \n", "\u001b[K |████████████████████████████████| 328 kB 63.2 MB/s \n", "\u001b[K |████████████████████████████████| 63 kB 1.4 MB/s \n", "\u001b[K |████████████████████████████████| 9.5 MB 43.5 MB/s \n", "\u001b[?25h Building wheel for absl-py (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lucid 0.3.10 requires umap-learn, which is not installed.\n", "xarray 0.18.2 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "tensorflow-metadata 1.2.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.7.1 which is incompatible.\n", "spacy 2.2.4 requires tqdm<5.0.0,>=4.38.0, but you have tqdm 4.28.1 which is incompatible.\n", "pyerfa 2.0.0 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "pyarrow 3.0.0 requires numpy>=1.16.6, but you have numpy 1.16.2 which is incompatible.\n", "panel 0.12.1 requires tqdm>=4.48.0, but you have tqdm 4.28.1 which is incompatible.\n", "kapre 0.3.5 requires numpy>=1.18.5, but you have numpy 1.16.2 which is incompatible.\n", "kapre 0.3.5 requires tensorflow>=2.0.0, but you have tensorflow 1.15.4 which is incompatible.\n", "jaxlib 0.1.70+cuda110 requires numpy>=1.18, but you have numpy 1.16.2 which is incompatible.\n", "jax 0.2.19 requires numpy>=1.18, but you have numpy 1.16.2 which is incompatible.\n", "google-colab 1.0.0 requires astor~=0.8.1, but you have astor 0.7.1 which is incompatible.\n", "google-colab 1.0.0 requires six~=1.15.0, but you have six 1.12.0 which is incompatible.\n", "google-api-python-client 1.12.8 requires six<2dev,>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "google-api-core 1.26.3 requires six>=1.13.0, but you have six 1.12.0 which is incompatible.\n", "fbprophet 0.7.1 requires tqdm>=4.36.1, but you have tqdm 4.28.1 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", "astropy 4.3.1 requires numpy>=1.17, but you have numpy 1.16.2 which is incompatible.\n", "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "n_XWusDibLE-" }, "source": [ "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/noxpd2_cnn_swissprot_go_random_swiss-cnn_for_swissprot_go_random-13731645.tar.gz\n", "!tar xzf noxpd2_cnn_swissprot_go_random_swiss-cnn_for_swissprot_go_random-13731645.tar.gz\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_GO/eval_test.fasta" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wgU7aSau4qzg" }, "source": [ "## Load vocabulary and parenthood information" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gY6_MDLc4jw0", "outputId": "622a0c32-710c-4700-91f7-3fc56ca9ed4f" }, "source": [ "vocab = inference.Inferrer(\n", " 'noxpd2_cnn_swissprot_go_random_swiss-cnn_for_swissprot_go_random-13731645'\n", ").get_variable('label_vocab:0').astype(str)\n", "label_normalizer = parenthood_lib.get_applicable_label_dict(\n", " 'parenthood.json.gz')" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Logging before flag parsing goes to stderr.\n", "W0908 10:41:05.693955 139873039775616 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/ragged/ragged_tensor.py:1586: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "aaSAa1xr5KXD" }, "source": [ "## Define a helper function to download inference results" ] }, { "cell_type": "code", "metadata": { "id": "SGsml7EVbTO9" }, "source": [ "def download_inference_results(run_name):\n", " file_shard_names = [\n", " '-{:05d}-of-00064.predictions.gz'.format(i) for i in range(64)\n", " ]\n", " subprocess.check_output(\n", " shlex.split(f'mkdir -p ./inference_results/{run_name}/'))\n", "\n", " for shard_name in tqdm.tqdm(file_shard_names,\n", " position=0,\n", " desc=\"Downloading\"):\n", " subprocess.check_output(\n", " shlex.split(\n", " f'wget https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/swissprot_inference_results/{run_name}/{shard_name} -O ./inference_results/{run_name}/{shard_name}'\n", " ))\n", " return\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "H-yqN7iZR4vK" }, "source": [ "# Downloading the data and getting it ready for analysis" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RZtlXNu8gKWd", "outputId": "15d9dfbe-f24b-418b-c78a-b160e11967e8" }, "source": [ "min_decision_threshold = 1e-3\n", "\n", "download_inference_results(f\"go_random_test\")\n", "\n", "predictions_df = colab_evaluation.get_normalized_inference_results(\n", " \"inference_results/go_random_test\",\n", " vocab,\n", " label_normalizer,\n", " min_decision_threshold=min_decision_threshold)\n" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading: 100%|██████████| 64/64 [04:08<00:00, 3.91s/it]\n", "100%|██████████| 64/64 [15:08<00:00, 14.38s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bLDLn75zC3Eu", "outputId": "7a58aa21-770f-4d1c-b52e-5a02aaad45f7" }, "source": [ "test_ground_truth = baseline_utils.load_ground_truth('eval_test.fasta')\n", "ground_truth_df = colab_evaluation.make_tidy_df_from_ground_truth(\n", " test_ground_truth)\n", "del test_ground_truth" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "108578it [00:01, 63382.08it/s]\n", "100%|██████████| 54289/54289 [00:01<00:00, 37184.31it/s]\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "UaR0teacxMH8" }, "source": [ "# Analysis\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z9YIbO0dxMIC", "outputId": "c37305c0-de04-418c-a76d-cd699cbf232f" }, "source": [ "cnn_pr_data = colab_evaluation.get_pr_curve_df(predictions_df, ground_truth_df)\n" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:03<00:00, 3.63s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "mxmaF5PCySFF", "outputId": "2536e60e-a3ab-4e63-98cd-37d11925aaca" }, "source": [ "cnn_pr_data.drop(index=0)" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
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groupprecisionrecallthresholdf1
1all0.7104920.9179490.0010000.801006
2all0.7112660.9178450.0010140.801458
3all0.7120260.9177430.0010300.801901
4all0.7127160.9176420.0010450.802300
5all0.7135830.9175410.0010630.802810
..................
1914all0.9880570.6843121.0000000.808600
1915all0.9881780.6829541.0000000.807692
1916all0.9887110.6774921.0000000.804036
1917all0.9888370.6759641.0000000.803001
1918all0.9889920.6739391.0000000.801621
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1918 rows × 5 columns

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" ], "text/plain": [ " group precision recall threshold f1\n", "1 all 0.710492 0.917949 0.001000 0.801006\n", "2 all 0.711266 0.917845 0.001014 0.801458\n", "3 all 0.712026 0.917743 0.001030 0.801901\n", "4 all 0.712716 0.917642 0.001045 0.802300\n", "5 all 0.713583 0.917541 0.001063 0.802810\n", "... ... ... ... ... ...\n", "1914 all 0.988057 0.684312 1.000000 0.808600\n", "1915 all 0.988178 0.682954 1.000000 0.807692\n", "1916 all 0.988711 0.677492 1.000000 0.804036\n", "1917 all 0.988837 0.675964 1.000000 0.803001\n", "1918 all 0.988992 0.673939 1.000000 0.801621\n", "\n", "[1918 rows x 5 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 468 }, "id": "2hmFNu1oxMIC", "outputId": "148d555e-4d9a-4086-bcbc-e2cb192d8f4b" }, "source": [ "ggplot(cnn_pr_data.drop(index=0),\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"f1\")) + geom_line() + geom_line() + coord_cartesian(\n", " xlim=(0.5, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"F1 Score\")\n" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 58 } ] }, { "cell_type": "markdown", "metadata": { "id": "40A0Yx1NxMID" }, "source": [ "What decision threshold maximises F1 score?" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 144 }, "id": "R_6fybn3xMID", "outputId": "5a95f6cb-b738-44aa-fa21-f471c26f7fde" }, "source": [ "cnn_pr_data.sort_values('f1', ascending=False)[:3]\n" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
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groupprecisionrecallthresholdf1
633all0.9180180.8540180.7101020.884862
632all0.9178880.8541190.7078670.884856
635all0.9182380.8538150.7139990.884856
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" ], "text/plain": [ " group precision recall threshold f1\n", "633 all 0.918018 0.854018 0.710102 0.884862\n", "632 all 0.917888 0.854119 0.707867 0.884856\n", "635 all 0.918238 0.853815 0.713999 0.884856" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "9H9UiNZXxMIE" }, "source": [ "Now let's have a look at PR curves for each different top level group." ] }, { "cell_type": "markdown", "metadata": { "id": "G4aq7gtaAQmx" }, "source": [ "# Load CNN ensemble predictions" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EtefjIEV_Qut", "outputId": "22e3a3ba-53df-4e49-bc63-0db689bcf602" }, "source": [ "min_decision_threshold = 1e-3\n", "download_inference_results(f\"go_random_test_ens\")\n", "ens_predictions_df = colab_evaluation.get_normalized_inference_results(\n", " \"inference_results/go_random_test_ens\",\n", " vocab,\n", " label_normalizer,\n", " min_decision_threshold=min_decision_threshold)\n" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading: 100%|██████████| 64/64 [04:06<00:00, 3.99s/it]\n", "100%|██████████| 64/64 [15:39<00:00, 15.16s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JLVF_tY8DVZx", "outputId": "5db12f03-26e2-4d9e-9080-5c3cb27ef14a" }, "source": [ "ens_cnn_pr_data = colab_evaluation.get_pr_curve_df(ens_predictions_df,\n", " ground_truth_df)\n" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:13<00:00, 13.92s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 144 }, "id": "c9-YYhgeKoPX", "outputId": "f8c58f51-e36a-4e6a-878d-6efbeb125be4" }, "source": [ "ens_cnn_pr_data.sort_values('f1', ascending=False)[0:3]\n" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
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groupprecisionrecallthresholdf1
863all0.9194770.8803190.3768710.899472
876all0.9208960.8790120.3871330.899466
862all0.9193560.8804190.3759790.899466
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" ], "text/plain": [ " group precision recall threshold f1\n", "863 all 0.919477 0.880319 0.376871 0.899472\n", "876 all 0.920896 0.879012 0.387133 0.899466\n", "862 all 0.919356 0.880419 0.375979 0.899466" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "code", "metadata": { "id": "6NvGPanNwkRy" }, "source": [ "cnn_pr_data['method'] = \"CNN\"\n", "ens_cnn_pr_data['method'] = \"CNN Ensemble\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533 }, "id": "BKUHnc3qwsBp", "outputId": "d87f2ba6-4ad7-443c-a3bd-3ba00b27c4f0" }, "source": [ "method_comparison = pd.concat([cnn_pr_data, ens_cnn_pr_data],\n", " ignore_index=True)\n", "ggplot(method_comparison.query(\"recall<1\"),\n", " aes(x=\"recall\", y=\"precision\", color=\"method\",\n", " linetype=\"method\")) + geom_line() + coord_cartesian(\n", " xlim=(0.5, 1), ylim=(0.5, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\")\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning:\n", "\n", "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", "\n" ] }, { "output_type": "display_data", "data": { "image/png": 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Yk5Px9B+Eb/AwvP0HYno85Lid5LidjPtBrF9mp/FNbT3fBwKsrKpmfV09vbt4WFtdy3/KqkgLNjK4NkA3r1sXZoqIdHAquEVE9sIen0Dc6COIG33Eju0Iiwup+2IdNZ99QuEDdxNpbMDTpx++wUPxDh6GMyOz6bmd3S46u11MJB7YsZ0hgNs0cZoGH9QHeXFDPhHgjl65pDkdfFhVTZ7bRar2CxcR6VBUcIuI7AfDMHCkZ+JIzyR+/EQiwSB1X39JzSerqXx9OSWP/x/O7E74Dzsc37CRODt13m0nk10/d/e6yXM7KSgIkZSezqaGRlIddurDYR7KL6Y8GCLD6aC/z8P4pDgtQxER6QBUcIuItIBht+Ptt2NdN6edSUNBPtUffUDNhyspe+4p7Gnp+IeNxDdiFO7uPTGaWTbiMs2mgtplGPy9d1fy6xtZW1PHupo6yhtDADxTVEZpY5BBfi/9fB7i7baYvlYRETk4KrhFRCzgzMwm+djpJB87ncaSYmrWfETNqg+pWPYy9qRk/CNH4xs+EnePXnuNYRhG03rwSSkJTcezXA42Buq5b2sRteEww+N8XJqbRUM4vKNvrQEXEWnTVHCLiFjMkZpG4jFTSTxmKsGKCqo/WknNRyupWP4Ktrh4/KOPINyzN5GMjP2KNyYhjjEJcYQjETYE6qkO7Si036us5uFtxQz2exke52NYvA+/TbPfIiJtjQpuEZEosicmknj0ZBKPnkyoejvVH62k6p3/Uv/qv9ickop/xGHEHTkOV+fcn4xlGgbdPO6mx6MT/HhtJqurani0oIR784u4r08eXtOkIhjSzXhERNoIfRqLiMSIzR9HwoSjiRs3kfyvvsS3dTM1H6yg4tV/4erWnfjxRxE3+ghMl2u/4rlNk1HxfkbF+wlFImwKNOCz2dgaaOCSbzfR2+tmdIKfkXE+7XwiItKKVHCLiLQCMyGRhN59SDp6Mg3b8qn67xuUPv0EpUseI+7I8ST87Gic2Tn7Hc9mGOR5dhTq2S4HC7t3YkVlNa+UVPDIthKuzsuhn89DQzisNd8iIjGmgltEpJU5s7JJPfUMkk88hZqPVlL5+jI2LX8FT78BJE6ainfQ0GZ3OdkbwzDo7nHT3ePmjIwUNgYayHE5aQiH+d1XG+nhdXFUUgJD4rzYDN1+XkQk2lRwi4i0EabTSdyYscSNGUv9xg1UvPZvCu5chD0lhYSJxxB3xHhsfv8BxTQMg647Z77DEbiwcwZvVWxn0eYCvDaT83LSGRrni8bLERGRnQ6Jgtvv92O325vu9GaVSCTSFNfq2NGya5ztZbygPMeK8hw7+5NrZ5dc0s/5Ncknz2L7W69TsewVSp9eQtwR40icehyO9P3b4eSHDKCfz0M/n4ezMlNYUVlDltNBJBLhsYIS8jwuRsX5cZh7znq3x1zrPR0b0cqz3X5IlChyiDAi7em/6hbIz8/niy++YNy4ca09FBGRFouEw9R9uoaqf/+L+u+/xTviMBKmHotzP3Y3+SnhSIRHi8p5t6oam2EwNt7HMUnxpGmXE2llCxcuZO7cuWRnZ7f2UEQOyiHxabpmzRoGDhxIWlqapXHD4TClpaWkpKRgtpOLkCKRCKFQCJvNttttp9sy5Tk2lOfYaWmuHSMOI274KAJff0n5v15g23V/xDtoCInTTsDTq89BjemXOemckZXK+5XVLC+rIs/bSJbHTXljkMSdd7Zsb7nWezo2opXn4uJiy2KJtLZDouCurq4mGAxa/uFlGEZT3PbywbhLexqz8hwbynPsHEyuDcPA26cf3j79qN+4nvJ/vUD+jdfh7tmLpGnT8Q4a0uJceGw2fpacwISkeCJABPjj91tIctiZmpLAcK8bezvKtd7TsRGtPAeDQctiibS2Q6LgFhHpiFy5eWT+5iIaCrZR8cqLbPvbLTizckiadgL+kYdhtPCuk4ZhsKtsuqZbJ/5dWsGD+cU8bpqcm53O0HhdZCkiciDax3dsIiKyV87MLNLPnkvXm+7A238ARYvvZdMV86h88z9EQqGDip3hdHBWVhp39splSlI8qTvXda+srKZ25y3mRURk3zTDLSLSQdiTkkmdNZukY6dT+dqrlC59nMplr5By2mx8AwcfVGyvzcbU5HjsdjuBUJh/FJRQFw5zYloyxyQnNLuziYiI7KAZbhGRDsbmjyN5+snk/nURnn4D2Hbb/yP/1htpyN9qSXy3zeTWnrnMSE/m+eJyLv5mI5sD9ZbEFhHpiFRwi4h0UDZ/HGm/mEOXv9wEwKY/XkrxYw8Tqq4+6Nh202BKSiK398plUnICGU4HoUiEtdW1Bx1bRKSjUcEtItLBObNzyJ43n6yLLqN23WdsvPwiKt963ZKblHhsJsenJeE0Tb6tC7BwQz4L129lY51mvEVEdlHBLSJyiPANGkKX6/4fycefSMk/HiH/5utpLC2xLH5vr4ebe3bBbTOZ/91m7tlSSGO4Q99bTURkv6jgFhE5hBh2O4mTp9HlL/+PSDDIpqsupertNy27JXe2y8klXbK4Oi+HOLsNh2kQCIUJdeybGouI7JN2KREROQQ50jPJufxPVP7nVYofeZCaNatIO+uX2BMSLYnf1+ehr88DwJLCUtbV1HFuTjo9vW5L4ouItCea4RYROUQZpkniMVPpfO0NBMvL2PTHS6n5eLXl/ZyUnkw3j4trvt/CU4WlBDXbLSKHGBXcIiKHOGdOJzr98ToSJk5i2+03UbLksYO+Yc4PxdttnNcpg0u6ZLG8rIrlZZWWxRYRaQ+0pERERDBsNlJOPAVP774U3n0HgfXfkfXbi7HFx1vWx/B4H3/1dsZn2ghHInxQVcNh8T4MQzfNEZGOTTPcIiLSxNtvAJ2vu5FIQwObr72S+o3rLY2faLfjMA021zdw99ZCbt60jaqgdbPpIiJtkQpuERHZjT0pmZwrrsHTpx9bFl7D9vfftbyPXLeLG7t3pqwxxPxvN/NdXcDyPkRE2goV3CIisgfT6ST9V+eTcvIsCu+7i9KljxMJhy3tI8vl5LpunRgc5+WF4nJLY4uItCVawy0iIs0yDIPEST/H2akLBX9fRGDTBjLPvwC7P86yPhymwdzsNII7Ny75oLKaYfE+7FrXLSIdiGa4RURkn7z9BtDp6oWEKivYet2fqN+62dL4hmHgMA2qgiEe3FbM9Rvyta5bRDoUFdwiIvKTHGnpZM6/GmduLlv+/CeqV31oeR/xdhvXd+9MIBzmyu82s17rukWkg1DBLSIi+8V0uck4/0KSjp1OwV23UfmfZZb3keKwsyAvh34+Dw/kF1t2y3kRkdakNdwiIrLfDMMg+djpOFJSKXzgbkI11SQdd6Kle2k7TZPzc9IJhCMYhkFJQyOpTodl8UVEYk0z3CIicsDiDj+SrN9fQvmLz1HyxP9ZPhNtGAYem0lJQyMXf7OJf5dWWBpfRCSWVHCLiEiL+IYMI/sPV7L97TcpfuRBy7cNBEh1Ojg/J51HC0q0daCItFsquEVEpMU8vfuSfekfqf5gBcX/92BU1lyPSYxjXucslhaWsqy00vL4IiLRpoJbREQOirtbd7L/cCXVK9+j5LGHo1J0D4/38fvOmXT3uiyPLSISbSq4RUTkoLm7dSfrkiuoevctSp96Iip9jE7w093jZnOgng+qqqPSh4hINLSJXUqqq6u56667WL16NR6PhxNPPJETTjih2bZvvPEGS5cupbS0lG7duvG73/2OTp06xXjEIiLyY54evci68FK23XIjNq+PpGOb/xw/WF/VBlicX8y8LlkMj/dFpQ8RESu1iRnue++9l8bGRhYvXsyCBQt4+umnWbVq1R7t1q1bx/33388f/vAHnnjiCQYNGsTChQsJhXRHMhGRtsDbtz+Zv72I0mefZPuKd6LSx9HJCczMSGHR5gLW1dRFpQ8RESu1esEdCAR49913mT17Nl6vl65duzJp0iSWL1++R9uVK1cyZswYunfvjs1m49RTT6WwsJC1a9e2wshFRKQ5vqHDST39TIoevJfA999GpY8T0pKYlJzALZu2UaNJFxFp41q94N66dSuRSITc3NymY3l5eWzatGmPtj++EGfX4w0bNkR1jCIicmASjppM3JHj2bboJhqLCqPSxxmZKVyWm4XPZotKfBERq7T6Gu5AIIDX693tmM/no65uz68Jhw8fzo033sikSZPIy8vjySefJBQKUV9fv1u7kpISSkpKACguLqahoQGAsMV7xO6KZ3XcaIpEIoTDYcLhsKV3hosm5Tk2lOfYOVRynXL6mTSWFpN/+03kXHktpsdj+bh6ul1UNDSytKiMX2Sm4Db/N490qOS5tbXHPIvEWqsX3G63e4/iura2Fk8zH8yDBw/mrLPO4rbbbqOqqoqJEyfSuXNnUlNTd2v3zDPPcP/99zc9Hj9+PAAFBQVReAVQVFQUlbiyO+U5NpTn2Dkkcn3iTIJ3387mO2/FecbZGKb1X6zWhCN8WhXg1ppa5vidmD8qVA+JPLcByrPI3rV6wZ2TkwPApk2b6NKlCwDr169v+vnHpkyZwpQpU4Adu5u8+uqr9OzZc7c2M2bMaCqyi4uLeeWVVwDIzMy0dOzhcJiioiLS09Mxo/BLJBoikQjBYBC73d6uZk+U5+hTnmPnUMt147zL2fqXq3G++xYpp5wWlfFdkdLANRvy+Y/hZHZmCnDo5bm1RCvP0ZokE2kNrV5wu91ujjjiCB599FEuvvhiiouLWbZsGRdeeOEebRsbG9myZQu5ublUVlZyzz33cPjhh++xLWBqamrTrHd8fDxOpxMgah+4pmm2qw/zXeNtLx/muyjPsaE8x86hkmtXVg6Zv72Y/FtuwN01j7jDxlg+vhyPm0u6ZPGXDVsZEe+jv/9/SxUPlTy3tvaUZ5FYa/WCG+DXv/41d955J3PmzMHj8TBjxgyGDx8OwMyZM7nmmmvo378/jY2NLFq0iG3btuF0Ohk7dixz5sxp3cGLiMhP8vYbQMrJsyhafD+u3K44M7Mt76Ovz8P13TrTxe20PLaIyMFoEwW33+9n/vz5zZ5bunRp089er5fbb789VsMSERELJU45lsDXX1Jw1+10vvovGA6H5X3kelwEQmFeLavg58nxlscXEWkJffcjIiIxYRgG6b88n1BVJeUv/zNq/QTCYV4qKeeFkoqo9SEiciBUcIuISMzY/H7SzphD2YvP0ZC/NSp9JDrsnJuTznPF5WwO6qY4ItL6VHCLiEhM+UYehnfAYIoevo9IlPZuHhXvZ0yCn8erG2jQ/tAi0spUcIuISEwZhkHa7LOp37SRqrdej1o/Z2amMNipu1CKSOtTwS0iIjHnSEkl5eRZlC79B8GK8qj04bPZmOJ1YjcMyhqDUelDRGR/qOAWEZFWkTBxEo7MLEqXPh7Vfl4pq+S69Vu1tEREWo0KbhERaRWGaZL2i7PZvuId6r7+Mmr9/CwxnkA4zL+0a4mItBIV3CIi0mrc3XsSP3YCxY8+RCQUnR1FvDaT0zNSeL64nJKGxqj0ISKyLyq4RUSkVaWcfBrBslIqX18WtT6OTIyji9vFfyu2R60PEZG9UcEtIiKtyhYfT8qMWZQ9u5RgRXSWfZiGweVdszgxLSkq8UVE9kUFt4iItLr4CUfhSM+k9KnoXUDpt9moC0d4rqiMYCQStX5ERH5MBbeIiLQ6wzRJO/Mctr/3dlQvoAxFIrxUWsGy0sqo9SEi8mMquEVEpE3YcQHlz6J6AWWc3cap6Sk8U1TGdt32XURiRAW3iIi0GSmnzIr6BZRHJceT4LDxXHFZ1PoQEfkhFdwiItJm2OJ+cAFlZXQuoLQZBr/ITMVh6FegiMSGPm1ERKRNiZ9wFI6M6N6Bclicj9MyU6IWX0Tkh1Rwi4hIm2KYJmmzz476BZS1oTB/Wb+VQt0MR0SiTAW3iIi0ObvuQFnyj4eJhMNR6cNjGtSHI/yzuDwq8UVEdlHBLSIibVLySafSULCN6vffjUp8wzA4OT2ZN8qrKKhviEofIiKggltERNooe2IiiZN+TtkLzxAJBqPSx+A4L909bl7WvtwiEkUquEVEpM1KnDKNUFUV29/9b9T6ODUjmf4+T9Tii4io4BYRkTbL5vOTOGUaZS8+F7Wb4bfTSscAACAASURBVAzwezkswU9Et3sXkShRwS0iIm1awtFTCFVXU/3Biqj18V1tgMu/20xjWEW3iFhPBbeIiLRpNp+PhAlHUf7yP6M2C53jdlLaGOTdyu1RiS8ihzYV3CIi0uYlTv45Dflbqf3s46jEd5smxyQn8FJJhZaWHOIWLFiAYRjk5OQQbmZLyiOOOALDMJgzZ84Bx33vvff2OG4YBjfffHNLh/uTFi1ahGEYUYsv+0cFt4iItHn2pGTijxhH+b/+GbU+JicnUNDQwCfVtVHrQ9oHh8NBSUkJ//3v7hfrbty4kRUrVuD3+w845rXXXttswS2HBhXcIiLSLiROPY7A118S+PabqMRPctg5NzudTKcjKvGl/XA6nUydOpUnnnhit+NLliyhf//+dO/evZVGJu2VCm4REWkXnFnZ+IaNoPzl6M1yj0+KJ9PlJKxlJYe80047jaeffprGxsamY48//jinn376Hm2/+OILTjjhBBISEvD5fEybNo3vvvuu6fyuJR2XXnophmFgGAZvvvlm0/lwOMyCBQvIyMggNTWVs88+m5qamt36+Oyzz5g8eTI+n4+EhAROPvlkNm3atFubqqoqzjzzTOLi4khLS+Oyyy4jGKU97OXA2Ft7ALHg9/ux2+2Wr8uLRCJNcdvLmr9d42wv4wXlOVaU59hRrlsucerxbF14NfVbt+DMztln25bm+amiMrYHQ5yTnXawwz1gbSXPByJa72e7vXVLlOOOO45f/vKXLFu2jGnTprFu3To+/fRTnn/+eZ588smmdt9//z1jxoxhwIABPPzww5imycKFCznqqKP46quvcLlcrFixgsMPP5zf//73TQV7v379mmLceeedjB07lkceeYSvv/6aSy+9lIyMDG688UYANm/ezLhx4+jevTuPPfYYgUCAq666ivHjx/Ppp58SFxcHwDnnnMOrr77KjTfeSF5eHn//+995/PHHY5g12ZtDouAeOnQoSUlJUfkrLykpiXA43OyFFW1ZKEr72UaL8hwbynPsKNctY8/tiqtXH8pe/iepc879yfYtyXO23cYDJRXMTE3AbbbOF8GtnecDFY33c1JSkmWxWsLr9XLCCSewZMkSpk2bxhNPPMHhhx9OXl7ebu2uvfZakpOTWb58OW63G4AxY8bQrVs3HnzwQX7zm98wevRoALp06dL08w9lZWXxj3/8A4ApU6awevVqnn766aaC+7bbbqOxsZFly5aRnJwM7Kht+vXrx8MPP8zvf/971q1bx7PPPssDDzzAOeecA8DkyZPp2bNndBIkB+SQKLjXrFnDwIEDSUuzdrYiHA5TWlpKSkoKZit9KB+oSCRCKBTCZrO1m6uWlefYUJ5jR7k+OMnTjmfb7TeTetJM7Mkpe23X0jyPSoznkaIyPqwJ8LOkeCuGvN/aUp73V7Tez8XFxZbFaqnTTjuN008/nbq6OpYsWcIFF1ywR5tly5Yxa9Ys7HZ708ReUlISQ4cO5cMPP9yvfo455pjdHvfr148lS5Y0PX777beZOHFiU7EN0KdPHwYPHsw777zD73//ez788EMikQgnnnhiUxubzcb06dO57bbbDuh1i/UOiYK7urqaYDBo+YeXYRhNcdvLB+Mu7WnMynNsKM+xo1wfHO/AITizO1G5/BVSZ83ea7uW5tlpMxiXGM8b5VVMTE6wYsgHrC3keX9F6/3cFtYeT548GYfDwdVXX8369euZOXPmHm1KSkpYtGgRixYt2uOc0+ncr34SExP3eF59fX3T4/LycoYMGbLH8zIyMigrKwNg27ZtOByOPb4ZyMjI2K8xSHQdEgW3iIh0HIZhkDTteIoevp+k42dg83ot7+Po5HiyXA4ikUi7KXzFeg6HgxkzZnDrrbdy1FFHNVu8JicnM23aNH7zm9/scW7X2uqDlZycTFFR0R7HCwsL6dWrF7BjWUpjYyPl5eW7Fd2FhYWWjEEOjgpuERFpd/wjDqN06T/Y/vYbJE6eZnn8LJeTLNf+zU5Kx/arX/2KoqIizj23+WsGjj76aD7//HOGDh2KzWbbaxyHw0EgEGjRGI488kjuu+++3Yrpr776ik8//bRpvfbIkSMBeO6555qOhUIhnn/++Rb1KdZSwS0iIu2OYbcTP3ESFa+9SsIxUzGisBZ+Y6Cee7cWcV23Ttg1y33IGjVq1D6L1muvvZaRI0cyefJk5s6dS0ZGBgUFBbz11luMHTuW0047DYC+ffvywgsvMHbsWHw+H717997vGfCLL76YxYsXM2nSJK666ioCgQB//OMf6dKlS9MdL/v168eJJ57IRRddRCAQoGvXrvz973+noaHhoHMgB699XK0jIiLyIwkTjiJUUU7Nx6ujEj/N4WBzoIG1uvOk7EOPHj344IMPSElJ4Te/+Q2TJ09m/vz51NTUMGjQoKZ2d911F+FwmKlTpzJy5EhWrVq133107tyZt956i6SkJM444wzmzp3L4MGDefPNN3cr2h966CGOP/54LrvsMs4880x69+7NRRddZOnrlZYxIu1ps88WyM/P57777mPu3LlkZ2dbGjscDlNQUEBmZma72mkgGAxit9vbzbpE5Tk2lOfYUa6tU/jgPYQqK8med/ke56zI880bt+G3mZzXKTYXnrXVPO9LtN7P0fz9LRJr7eOTXkREpBnxYydQ+9nHBMvLohJ/dIKfD7fXEOzYc1MiEmUquEVEpN1y9+yNIz2D7e++HZX4w+N8HBbvpy7Uvm5QJCJtiwpuERFptwzDIH7sz6h6+42o3A7dYzOZm5NOnH3vu0+IiPwU7VIiIiLtWtyR4yh9bil1X6zF22+A5fFLG4MsLSzl3Ox07Gb7WFctB+5ALmI8EMOHD49KXGlfNMMtIiLtmj0xCd+QYWx/+82oxPeaJu9VVvNZjXYrEZGW0Qy3iIi0e3FHjKfwnr+RVleH6fFYGttjMxka5+X9ymqGxvksjS1tz647N1rh66+/tiyWtG+a4RYRkXbPN2gIhtNJ9eoPoxJ/dIKfj6pqCIa1W4mIHDgV3CIi0u4Zdjv+UaPZvuKdqMQf5veR6XJQ0hiMSnwR6dhUcIuISIcQd/iR1K39jGBFueWx3TaThd07k+lyWB5bRDo+FdwiItIhuHv0wpGWzvb3ojPLXRcK80ppBY1aViIWeeqppxg9ejR+v5/09HQmTJjAiy++yJtvvolhGMyePXu39rNmzWLBggVNjw3DoHv37jQ2NjYdu+eee5gwYUKMXoHsLxXcIiLSIRiGQdyRE6h6582o7MltGPBEQSmfVmu3Ejl4t99+O+effz7z5s1j27ZtbNu2jT/96U88//zzAHi9Xp599lnWrl27zzgVFRU88MADsRiyHAQV3CIi0mHEHTGWxm351G/43vLYbtNkSJyXD6qqLY8th5aqqiquuuoq7rrrLmbOnElcXBw2m42jjjqKBx98EIC4uDjOO+88rrrqqn3Gmj9/Pn/+85+pq6uLxdClhVRwi4hIh+FIScXdqw/V778XlfjD4nx8Ul1LOAoz6HLoeO+99wgEApx00kn7bHfllVfyxhtvsHLlyr22Oe6448jLy+OOO+6wephiIe3DLSIiHUrcYWMoe/E5kk45zfLYg/1eHswvpqihkUyX0/L40noS/nYTAIVWxgR4eMkex0tLS0lNTcXh2PdFuCkpKVxyySVcccUVvP7663ttd8MNNzB9+nR+/etfH+SIJVo0wy0iIh2Kf+RhhKoqCXzzleWxkxx2Huibp2JbDkpKSgolJSW7Xey4N/PmzePzzz9n+fLle20zbtw4Ro8ezU033WTlMMVCKrhFRKRDscXF4+0/kOqV0VlW4jAMNgXqoxJbDg1jxozB7Xbz3HPP/WRbv9/PVVddxZVXXrnPdtdffz133HEHhYVWztGLVbSkREREOhz/YWMoeeL/cB01xfLY39QGWLB+K/f3zcNvs1keX1pH5e8vBWJza/f4+HgWLlzI7373O0zTZMqUKXg8Ht555x0ee+wxzjjjjN3an3/++dx2222sX7+ePn36NBtzyJAhHHvssfztb39jwIABlr0GsYZmuEVEpMPxDRlGuLaW8KaNlsfu4XXjtZnaHlAOyoUXXsidd97JzTffTEZGBllZWSxYsIDp06fv0dbpdLJgwQJKS0v3GfPPf/4zlZWV0RqyHATNcIuISIdj8/lx9+pD8Mu1cPgYa2MbBoP8Xj7eXsuYhDhLY8uhZebMmcycObPZcwUFBbs9njNnDnPmzNnt2I/3m+/Ro8d+rQuX2NMMt4iIdEjewUMJfbHvm4a01FC/l0+2a3tAEdk/KrhFRKRD8g4aSqSkiMYi6y8iGxbvY16XTMvjikjH1CaWlFRXV3PXXXexevVqPB4PJ554IieccEKzbd955x2eeOIJSkpKSExMZMaMGUyaNCnGIxYRkbbOkZmFkZxC7SdrcGVmWRrbb7PR2+eJyi3kRaTjaRMz3Pfeey+NjY0sXryYBQsW8PTTT7Nq1ao92hUXF3Prrbdy1llnsWTJEi6++GIeeOABvvvuu1YYtYiItGWGYWDr04/aT9dEJf4b5VVcvyE/KrFFpGNp9YI7EAjw7rvvMnv2bLxeL127dmXSpEnNbvBeXFyMz+dj1KhRGIZBnz596NSpE5s2bWqFkYuISFtn692Puq++IFxXZ3nsDKeDdTV11IbClscWkY6l1ZeUbN26lUgkQm5ubtOxvLw8VqxYsUfb3r17k5OTw4oVKzjssMP48ssvKSwspH///rEcsoiItBNmXncMm53adZ/hHz7K0ti9PG4cpsG6mlpGxPstjS2tZ297Z4scjFYvuAOBAF6vd7djPp+PumZmI2w2GxMnTmTRokXU19djGAbnn38+6enpu7UrKSmhpKQE2DEr3tDQAEA4bO0sxK54VseNpkgkQjgcJhwOYxhGaw9nvyjPsaE8x45yHRvhcBjDbsczYCA1a1bjHTrC0vgm0M/r4ZPttQzze3+y/f5or3n+4f+2V8OHD2/tIUgH1uoFt9vt3qO4rq2txePx7NF2zZo1LF68mGuvvZZevXqxZcsWrrvuOpKSkhg5cmRTu2eeeYb777+/6fH48eOBPfe0tEpRUVFU4srulOfYUJ5jR7mOjWDX7tS++i+C+fkYprUrKYcSpLYhGLXfL+2J3s8ie3fQBXd+fj5btmwhEAjscW7cuHE/+fycnBwANm3aRJcuXQBYv359088/tGHDBvr27dt0W9MuXbowYsQIVq1atVvBPWPGjKYiu7i4mFdeeQWAzExrt3AKh8MUFRWRnp6OafGHeLREIhGCwSB2u71dzZ4oz9GnPMeOch0bu/KcccRYNj/7JEmBWtzdeljah9UbA7bnPFv9ftYfMdKRtLjg/v7775k9ezbvv/8+sOfdjgzDIBQK/WQct9vNEUccwaOPPsrFF19McXExy5Yt48ILL9yjbc+ePXnqqaf45ptv6NmzJ1u2bOGjjz7ilFNO2a1damoqqampAMTHx+N0OgGi9ovNNM129Utz13jby4f5LspzbCjPsaNcx4YjMQlXtx7UfbIGb49elsd/uaSCdKfdknXc7TnP7en93JzmdkezgpaqCBxEwX3uueeyZcsWHnroIfr169dU1LbEr3/9a+68807mzJmDx+NhxowZTW/QmTNncs0119C/f38GDBjA7NmzueWWWygvL8fn8zFhwgSOOeaYFvctIiIdn2/QEGo+XkXKjFMtj70pUM+3dQFdOCkie9XigvuDDz7gkUce4aSTTjroQfj9fubPn9/suaVLl+72eOrUqUydOvWg+xQRkUOHd+AQyp5/mlBVFbb4eEtj9/V5WFJYSiQSaXez0rKnXr2s+xZEO57ILi3+7icnJwebzWblWERERKLC1TUP0+endu2nlsfu6/NQHgxR2NBoeWwR6RhaXHAvXLiQG2+8kbKyMivHIyIiYjnDNPEOGEjtZ59YHjvNYaer20VxY9Dy2CLSMbR4ScnDDz/Mli1b6Nq1K0OGDCExMXG384Zh8MILLxz0AEVERKzgHTCY0qWPEwmHLd0e0DAMbuzR2bJ4Im3Fww8/zD333NO0QcaPzZkzh8zMTG688cYDjn3PPfewZMkS3nzzzYMcZfvQ4k+c6upqevTowfDhw7HZbGzfvn23f1VVVVaOU0RE5KB4BwwiVFVJw+aNlseuD4f5ZHut5XGl45owYQJutxu/39/0Tzua/E9jYyMLFy6kT58++Hw+OnfuzMyZM/nss88AWLBgAYZh8OCDD+72vMzMzKYi/s0338QwDGbPnr1bm1mzZrFgwYJYvIwmLZ7hfuONN6wch4iISFTZE5Nwds6l9rNPceXmWRq7tDHIDRvz+VuvXNKcDktjS8e1aNEizjvvvNYeRpt06qmn8t1337F48WJGjBhBQ0MDzzzzDC+99BIDBw4EIDk5meuuu45f/OIXuFyuZuN4vV6effZZ5s+fT//+/WP5EnbTfjfMFBEROUDegYOp+exjy+NmOR0k2G18UVP3041FfsKGDRswDINHH32UvLw8kpKSuPjii5vOf//990ycOJGEhARSUlJ2u9FgQUEBp556KhkZGXTu3JkFCxYQDoeBHUtERo8ezfz580lOTqZTp0689NJLvPrqq/Tu3ZvExET+8Ic/7DGeefPmkZiYSPfu3ffYPe6HXn31VUaMGEFiYiLDhg3j7bffbjq3adMmjjrqKOLi4jjiiCPYuHHv3zS9/vrrvPzyy/zzn//k8MMPx+Fw4PP5OPPMM7niiiua2h199NGkp6dz99137zVWXFwc5513HlddddVe28TCQRXca9as4ZRTTiErKwuXy0VWVhYzZ85kzZo1Vo1PRETEMt6Bgwl8+zXhOmsLY8Mw6Ov1sE4Ft1jotdde4/PPP2fVqlUsXryY119/HYCrrrqKXr16UVJSQkFBAQsXLgR23PXz+OOPp0ePHmzcuJGVK1fywgsv7LbsYtWqVeTm5lJUVMTll1/O2WefzeLFi/nggw9YvXo19913H++9915T+48++ojU1FSKi4u5++67Oeuss/jmm2/2GOsnn3zC6aefzqJFiygrK+O6665j+vTplJSUAHDaaafRu3dviouLueOOO/ZYCvJDy5YtY9SoUeTm5u4zP4ZhcP3113P99ddTXV2913ZXXnklb7zxBitXrtxnvGhq8ZKSt99+m2OOOYbMzExOO+00MjIyKCws5LnnnmPMmDEsX76cI4880sqxioiIHBRPz94Ydju1X6zFP2yEpbH7+Ty8XFphaUyJnZtcCTt+2FhoXVBXAkv2cXrevHm73YfkhBNO4JFHHml6fO211+Lz+ejWrRvjxo1j9erVTJw4EafTybZt29i4cSM9evRg7NixwI7iePPmzfzlL3/BMAyys7OZN28eixcv5txzzwV2bOt8/vnnA3DGGWdwwQUXcNFFF5GQkEBCQgKjR49m9erVjBkzBtixbGP+/PmYpsmkSZOYMmUKTz75JH/84x93ey333nsvv/rVr5pqv2OPPZYhQ4bw8ssvM2HCBFasWMHLL7+M2+1m+PDhnHHGGXudoC0tLSU7O3u/UnzMMccwYMAAbr31Vq6++upm26SkpHDJJZdwxRVXNP3REmstnuGeP38+EyZM4Ntvv+XWW2/l8ssv59Zbb+Xbb79l/Pjxe72RjYiISGsx7Ha8/QZQ+7n12wOOTvBzaW6W5XGl47r11lupqKho+vfDYht2XAC4i9frbZrFvemmm8jOzuZnP/sZ3bp1a9olZMOGDRQXF5OUlERiYiKJiYn89re/pbCwcK8x99UP7CjQzR/s6pObm8vWrVv3eC0bNmzgb3/7W1O/iYmJvP/+++Tn55Ofn99U0P8wzt6kpKSQn5+/1/M/dsMNN3DLLbdQWlq61zbz5s3j888/Z/ny5fsd10otLrjXrFnDBRdcgN2++yS5zWbjggsuYPXq1Qc9OBEREat5+g+ibu1nlseNt9vIcTmJRCKWxxb5oV3rljdv3syzzz7LTTfdxH/+8x+6dOlCp06ddiviq6qqWLt2bYv72rp1a9MacNixFjsnJ2ePdl26dOGyyy7bre+amhrmz59PdnY2lZWVu+1gt2nTpr32OWnSJD744IN9tvmhww47jIkTJ3LDDTfstY3f7+eqq67iyiuv3K+YVmvxkhKfz0dRUVGz5woLC/H5fC0elIiISLR4evel5LHFBCsqsP/oHhIH62+bC+jkcnJierKlcSX6Lq2vBNrHrd2XLl3K4YcfTufOnUlMTMRms2Gz2Rg5ciRpaWn8+c9/Zt68eXg8Hr777jvy8/MZP358i/oqKyvjr3/9K5dccglvvfUWr7zySrOF7dy5czn22GM5+uijGTNmDPX19bz//vv07NmTLl26MHr0aK688kpuueUW1q1bxz/+8Q/69u3bbJ8TJ05k6tSpTJ8+nbvvvpthw4bR2NjIc889x+bNm5tdRbFw4UJGjRq1zz94zz//fG677TbWr19Pnz59WpSPlmrxDPdxxx3H5Zdfzmuvvbbb8ddee40rrriC448//qAHJyIiYjVnTidMn4/A119aHjvObuPbuoDlcaVjuuiii3bbh/uHSzv2ZdWqVRx++OH4fD7GjRvHRRddxIQJE7DZbLz44ot888039OzZk6SkJGbOnMm2bdtaPMYRI0ZQVFREWloac+fO5aGHHqJ37957tBs2bBiPPPIIl156KSkpKeTm5nLLLbc0zY4//vjjrF27ltTUVH73u99xzjnn7LPfpUuXctJJJ3HmmWeSmJhIr169ePHFFznuuOOabd+vXz9OPvlkamv3vh++0+lkwYIF+1x6Ei1GpIXffZWXlzNlyhQ++ugj4uPjSU9Pp6ioiKqqKkaOHMkrr7xCUlKS1eM9YPn5+dx3333MnTt3vxfg769wOExBQQGZmZm7rW9qyyKRCMFgELvdjmEYrT2c/aI8x4byHDvKdWzsK8/5i27CkZpG2i/mWNrn2xXbebyghLv7tGyf746W54MRzd/fzVm1ahVg/Qy3bmYjcBBLSpKSklixYgUvvfQS77zzDuXl5SQnJ3PkkUcybdq0dvNLREREDj2e3n3Y/t47lsfNc7soD4aoaAyS6Gjxr1gR6WAO6tPANE2OP/54LR8REZF2xdOrD6VLHydUU4PNwmuOsl0OjkqKJ6gLJ0XkBw6o4C4rKyMxMRHTNCkrK/vJ9snJumhERETaHlduHobDSeCbr/ANGWZZXNMwODcn3bJ4ItIxHFDBnZaWxooVKxg1ahSpqak/ub4sFAod1OBERESiwbDbcffoSd3XX1hacAN8XVvHpkADRycn/HRjETkkHFDB/dBDD9G9e/emn9vLBR0iIiI/5unVh9rPP7U8bn59I88Xl6vgFpEmB1Rwn3XWWU0/z5kzx+qxiIiIxIy7R0/KX3qBSDCIYbfuAsc8j4uSxiBVwRDxdptlcSU2orV3thzaLL2E+t133+WLL75g7Nixze7RKCIi0la48noQCTZSv2kj7m7dLYub43LiMAw2BOoZ5PdaFleiS9v3STS1eO++008/nbPPPrvp8T333MPYsWOZO3cuQ4YM4T//+Y8lAxQREYkGm8+HM6cTgW+svQGO3TDo4nayrb7B0rgi0n61uOB+5513mDp1atPjG264gV/96ldUVVVx8sknc+2111oyQBERkWhx9+5L3VfW33Hy2rxOTE6x9rbxItJ+tbjgLi4uJisrC4C1a9eyefNmLrzwQvx+P2eddRafffaZZYMUERGJBk+vvtR98yUtvOnyXtkMKGlotDSmiLRfLS64U1JS2LhxIwD//ve/ycrKon///sCO7QDD4bA1IxQREYkSd89ehLdvp7Fgm6Vx1wfq+d3XG6nR9rgiwkFcNDl16lQuv/xyPvnkEx5++GFmz57ddO7zzz8nLy/PkgGKiIhEiyMlFXtyCoFvvsKZlW1Z3M4uFzYDNtTV018XTooc8lo8w33zzTczefJk/v3vf/Pzn/98tzXbzz33HFOmTLFkgCIiItHk7tmbum++sjSmwzTo7HKxPlBvaVwRaZ9aPMOdkJDAQw891Oy5d955p8UDEhERiSV3z95Uvvaq5XHzPC7W16ngFhGL9+Fuq/x+P3a73fKLYiKRSFNcq2NHy65xtpfxgvIcK8pz7CjXsbG/eXb37E3JY4sJVlZii4+3rP+JiXHUhcMHlLOOnOcDZbfwZkQire2A3s2DBg3i8ccfZ8CAAQwcOPAnb+3+6afW3zK3JYYOHUpSUhLBYNDy2ElJSYTD4XZ3kWionV3IozzHhvIcO8p1bOxPns3MLAy3m5qvv8Q7ZJhlfXd17vgV25LfPR0xzy2JKdJRHFDBPXz4cHw+X9PPP1VwtxVr1qxh4MCBpKWlWRo3HA5TWlpKSkoKptni5fAxFYlECIVC2Gy2dvP/n/IcG8pz7CjXsXEgeXZ17UbjxvXYR4yyrP9IJMLtWwo5LjWR7h73fj+nI+f5QBQXF1sWS6S1HVDBvXjx4qafH374YavHEjXV1dUEg0HLP7wMw2iK214+GHdpT2NWnmNDeY4d5To2DiTP7q7daNi43tLXZhgG+fWNfF1bTw+v54Cf2xHzfCCi8a20SGtpH1MrIiIiUeTq2o36DestXzvd1eNik3YqETnktbjgPuecczj11FObPTdr1izmzp3b4kGJiIjEkqtrHqHtVQTLSi2N28XtZFOgwdKYItL+tLjgXr58OSeddFKz52bMmMGrr1q/xZKIiEg0ONIzMD0e6jd8b2ncbm4XHpvZrnYdERHrtXjPneLi4r1ehJiSkkJhYWGLByUiIhJLhmk2LSvxD7fuwsn+fq/uNCkiLZ/hzsnJYeXKlc2eW7lyJVlZWS0elIiISKy5cvMsn+EG+K42QEG9lpWIHMpaXHCfdtppLFy4kKVLl+52/KmnnuL666/n9NNPP+jBiYiIxIqrazcCG763fPnHE4WlvFmx3dKYItK+tLjgvvrq3t+WKQAAIABJREFUq5kwYQKzZs0iLi6OXr16ERcXx6xZsxg/fjzXXHONleMUERGJKldeN8Lbt0fpwkntVCJyKGtxwe10OnnppZd49dVX+e3/b+/Oo5uq1v6Bf0+mJulMB0rpyFSEckVa5qlM/SEXGSwWZCz6Lob3ogx6oTJZQYYlClwvoyiVhaBUqagMV8biC3Yhk/cqoiLSlrlJW4TSps30+6OXaO1AW5JzkvT7WatrNSf77Dx5Vpo83dln77/9DQkJCZgxYwYOHjyIvXv3QqVS2TNOIiIih6q4cFJr92klEWoPrlRCAICcnBykpaXhxo0blY5nZWVBEAScOXPGYY89YsQIJCQkOKx/ql2DL5p8YNCgQRg0aJA9YiEiIpKMIAjwiIq2+4WTkWoP6I0m3Deb4SmX261fcj05OTl47bXXMHToUISGhkodDonokTe++de//oWlS5diypQpyMvLAwB89dVXVf57IyIicnYekdEou2LfEe5wDxWWtgiDhx23PSci19Lgv36dToeePXvir3/9K9577z2899570Ov1AICtW7di2bJldguSiIhIDB7RLWDIte+FkwqZgNZaNRQuslV7Y5eSkoLY2FgcPnwYf/nLX6DRaNC3b1/k5OSgsLAQycnJ8PHxQcuWLbFr165K5+7btw9du3aFRqNBUFAQpk+fjvv37wOomDbSr18/AEDnzp0hCAKEP70mioqKMHbsWHh7eyMyMhJvvPFGlfgyMzPRsWNHqNVqhIaGYs6cOTAYDJXaXLx4EX379oVarUbLli2xbds2e6aIGqDBBfesWbOg0+nw/fff45dffqn05jRw4EAcOXLELgESERGJxSPKMRdO7rpdgG03dXbtkxzn1q1beOmll7BgwQLs2LEDly9fxrhx4zB69Gh06NABu3fvRlxcHMaPH4/c3FwAwCeffIJhw4ahQ4cO+PTTT/HGG28gMzMTzz//PACgU6dOWL9+PQAgPT0d2dnZyM7OrvS406ZNQ5s2bfDpp5/iqaeewrx58/Cvf/3Ldv/nn3+OUaNGoV27dtizZw/mzp2LTZs2Yfz48bY2BoMBiYmJuH37NrZv346VK1di5cqVOH36tKPTRrVo8Bzuffv2YcuWLXjsscdgNpsr3RceHo5r1649cnBERERisl04eeVXKAMC7davh0zA98WGhzckp1BYWIjjx4+jffv2AIAbN27ghRdewLx587Bo0SIAFaPUmZmZ2LNnD1588UW8/PLLGD16NN59911bP82aNcOQIUOwaNEitG/fHu3atQMAxMbGIj4+vsrjJiUlIS0tDQAwYMAA7Nu3D5988gkGDx4MAEhLS0O3bt2wc+dOAMDgwYOh1WoxdepUfPfdd+jQoQPef/993LhxAz/++CNat24NAHjiiScQExNju03ia/AIt8lkgqenZ7X3FRUVcZUSIiJyOb9fOOmAlUrKymHhFu8uITQ01FZsA0CbNm0AVHyD/4Cfnx+Cg4Nx9epV/Pzzz8jNzUVycjJMJpPtp2/fvpDJZHVefSQxMdH2uyAIeOyxx2wDmMXFxfj2228xatSoSueMHj0aAHDixAkAFZsPxsbGViquW7Vqhccff7w+KSA7a3DB3bVrV2zdurXa+z766CP07NmzwUERERFJ5cEW7/YU4aFCmcWK/HKTXfslx/Dz86t0+8EgYnXHDQaD7Rq2kSNHQqlU2n60Wi3MZjOuXr3a4Md9MD/7zp07sFqtaNq0aaU2vr6+8PDwQGFhIQDg5s2bCA4OrtL3n88jcTV4Ssnrr7+Ofv36oU+fPhg1ahQEQcCePXuwYsUK7Nu3z/afFhERkSvxiIrG3RNZsFqtVS5qa6gApQK9/bxhAUe43VGTJk0AAOvWrUPXrl2r3G+PJQD9/PwgCALy8/MrHf/tt99QVlZmi6FZs2Y4d+5clfNv374NHx+fR46DGqbBI9zdu3fHsWPHIAgCXnrpJVitVixbtgw3b97EkSNH0KlTJ3vGSUREJArbhZMFerv1KQgC/hbWFKEenG7pjtq2bYuwsDD8+uuviI+Pr/LzoOB+MFL+51VF6sLLywsdO3bEJ598Uul4RkYGAKBXr14AgC5dutgWtHjgl19+wb///e8GPTeyjwaNcJeXl2Pv3r3o2LEjjh8/jtLSUhQVFcHPzw9ardbeMRIREYnm9x0nr0AZGGS3fq+UGnC9zIheft5265OcgyAIWL16NcaOHYv79+/jr3/9Kzw9PZGbm4t9+/Zh+fLlaNOmDdq0aQO5XI6tW7dCoVBAoVBUe/FkTdLS0jBixAiMHz8e48ePx08//YT58+cjKSkJHTp0AFCxrOHrr7+OoUOHYunSpQCAxYsXIyQkxCHPneqmQSPcKpUKY8eOtW10o9FoEBoaymKbiIhcnqMunLxcWoZP8gvt2ic5j2eeeQb79+/Hjz/+iGeffRbDhg3DW2+9haioKNv86cDAQKxfvx7Hjx9H79690blz53o9xrBhw/Dxxx/ju+++w/Dhw7Fy5UpMmTIFH3zwga2NRqPBwYMHERwcjPHjx2PevHmYO3duvR+L7KvBc7jbtm1rK7iJiIjciUdUCxjsXHBHqj1wu9wIg9kCtZy7Tjqr999/v8qxhISEajdDysnJqXR70KBBGDRoUK39T506FVOnTq1T/3v27KlyLCkpCUlJSbU+Rvv27fHVV19VOjZ58uRazyHHavBf/IoVK/D666/XeakbIiIiV+ERXbFSiT13nAz/7/ztq2XlduuTiFxDg0e4586di4KCAnTt2hUBAQFo2rRppau5BUHgBH0iInJJ6qhoWIorLpy01zxutVyGpiolrhrK0VqrtkufROQaGlxw12eSPxERkStRBDWFTOtp9wsnX28RBk9OJyFqdOpdcP/www/YtGkT9Ho9QkNDMWrUqIfOVyIiInIlgiDAM64zrCb7blTjpZCj3GKByk7re5P9nD171iH9xsXFOaRfci31KrhPnDiBgQMHwmg0IigoCAUFBXj33Xexfv16TJs2zVExEhERia7p8/b/XPv6zj3suF2A9TFRdu+biJxXvQruV199FW3btsUXX3yB8PBw3L17F5MnT8bChQtZcBMRET1EoEqBQqOpYpRbxqklzqhNmzZ26+vnn3+2W1/k2upVcH/33XfYtGkTwsPDAQA+Pj5466230KJFC1y9etV2vL6Ki4uxfv16nDt3DhqNBiNHjsTw4cOrtMvKysKGDRtst61WK8rKypCamooePXo06LGJiIjEEqJSwQpAV25CczV3nSRqLOpVcOv1eoSFhVU69qDI1uv1DS64N2/eDKPRiPT0dOTn52PRokUICwurMu8pISEBCQkJtttnz57FqlWrOD+KiIhcgrdcBo1MwK1yIwtuokak3t9nCXa+0MNgMODkyZOYMGECtFotoqKikJiYiEOHDj303EOHDqFXr17w8PCwa0xERESOIAgCkoKbIFDZ4EXCiMgF1bvg7tevH3x8fGw//v7+AIDevXtXOu7r61un/q5fvw6r1YrIyEjbsejo6IfuYnn37l188803GDhwYH2fAhERkWSGBvojUsOBIgI+/vhjdOvWDV5eXggODkZCQgK++OILZGVlQRAETJgwoVL7MWPGIC0tzXZbEAS0bNkSRqPRdmzTpk2VZgOQc6j3RZP2ZjAYoNVqKx3z9PREaWlprecdP34czZo1Q9u2bavcp9frodfrAQA6nQ7l5RW7elksFjtFjUr92btfR7JarbBYLLBYLHb/tsJRmGdxMM/iYa7F4ax5vni/FDmGcjwZUHVginluPP7xj39g6dKl2LBhA5588klotVpkZWVh586dtm/9MzMzkZqaivbt29fYz507d/Duu+9i+vTpIkZP9SV5wa1Wq6sU1yUlJdBoNLWed/jwYQwYMKDa+3bv3o0tW7bYbvft2xcAcOvWrUeMtnr5+fkO6ZcqY57FwTyLh7kWh7Pl+SeDCUcNRjxhrH1gydU4W56d2d27d7FgwQK89957SE5Oth0fMGAABgwYgKysLHh7e2PcuHFYsGAB9uzZU2NfqampWLp0KVJSUh5aO5F0JJ9E1rx5cwBAXl4eIiIiAABXrlyx/V6dy5cvIy8vD/369av2/qSkJFuRrdPpcODAAQBASEiIPUOHxWJBfn4+goODIXOR5Z2sVitMJhMUCoVLjZ4wz47HPIuHuRaHs+a5zf1S7M69iaCmTSH/Uy6Z5985apDMGXz99dcwGAx4+umna203f/58tGjRAqdOnULXrl2rbfPUU09hz549ePvttzFv3jxHhEt2IHnBrVar0bNnT2zfvh2zZ8+GTqfDwYMHMXPmzBrPOXLkCOLi4mzzx/8sMDAQgYGBACqWLlSpKq4Ed9Qbrkwmc6o389pYrVZbvK7yZv4A8ywO5lk8zLU4nC3PIR4qmAEUmi1oqlJWuo95lk58fLxD+rVarVWOFRQUIDAwEEqlspozfhcQEICXXnoJr7zyCo4ePVpjuxUrVmDEiBGYOnXqI8dLjuEUfxlTp06FXC5HSkoKFi9ejKSkJNtSf8nJybhw4YKtrdFoxPHjx3mxJBERuaQmSgU0MgGFRvtuG0+uIyAgAHq9vtLFjjWZM2cOvv/++1pXb+vTpw+6deuGVatW2TNMsiPJR7gBwMvLC6mpqdXel5GRUem2UqnEjh07xAiLiIjI7mSCgPceawGZi41gk/306NEDarUan376aaU53NXx8vLCggULMH/+fLRs2bLGdsuXL0fv3r3x8ssv2ztcsgOnKLiJiIgaE7MVKDab4aOQSx0K/deZM2cAiLO1u4+PD5YtW4YZM2ZAJpNh8ODB0Gg0OHHiBD744AOMGzeuUvvp06djzZo1uHLlSrWrswFAx44dMXToUPzzn/9EbGys3Z4D2YdTTCkhIiJqTD68rcc717mqR2M2c+ZMrFu3Dm+++SaaNm2KZs2aIS0tDSNGjKjSVqVSIS0tDQUFBbX2uXTpUvz222+OCpkeAUe4iYiIRBasUuK7YvdaFpDqLzk5ucYpJX9epSUlJQUpKSmVjv35gsxWrVrVaV44iY8j3ERERCLzkctRbDZLHQYRiYQFNxERkcg0chlKuTMjUaPBgpuIiEhk4R4qJAcHVLtGMxG5H87hJiIiElmgSokhgX5Sh0FEIuEINxERkchKzRa8eyMfd0zc/IaoMeAINxERkcjkAnC48C76+/vAT8GPYmdS09rZRI+Cf+VEREQiU8lkUAkC7pl44aSziIuLkzoEcmOcUkJERCQBLy4NSNRosOAmIiKSgL9SjjIuDUjUKHBKCRERkQSWtQyXOgT6g7NnzzqkX05VIYAj3ERERJIos1g4pYSokeAINxERkQS239SjxGLBi+EhUodCf9CmTRu79cUVT+gBjnATERFJwEvBiyaJGgsW3ERERBLwlstRzGUBiRoFFtxEREQS8JbLcI8j3OTE3n//fXTr1q3G+1NSUpCamtqgvjdt2oSEhIQGRuZ6WHATERFJoKVWjaGBflKHQRJJSEiAWq2Gl5eX7YcrmvzOaDRi2bJlaNu2LTw9PREeHo7k5GR89913AIC0tDQIgoD33nuv0nkhISHIysoCAGRlZUEQBEyYMKFSmzFjxiAtLU2Mp2HDgpuIiEgCzT1U+H8BLLgbs7Vr16K4uNj246ilCV3R6NGjkZGRgfT0dNy5cwc//vgjhg4dir1799raNGnSBEuWLEFZWVmN/Wi1WmRmZuLChQtihF0jFtxEREQSKDVbsPWGDneMJqlDISeTk5MDQRCwfft2REdHw9/fH7Nnz7bd/+uvv6J///7w9fVFQEAA+vTpY7vv1q1bGD16NJo2bYrw8HCkpaXB8t8Nlh5MEUlNTUWTJk0QFhaGvXv34ssvv0RMTAz8/Pzw8ssvV4lnzpw58PPzQ8uWLZGRkVFj3F9++SXi4+Ph5+eHTp064f/+7/9s9+Xl5WHAgAHw9vZGz549kZubW2M/R48exf79+/H555+je/fuUCqV8PT0xMSJE/HKK6/Y2g0cOBDBwcHYuHFjjX15e3tj2rRpWLBgQY1txMCCm4iISAJKQcChwt9wu9wodSjkpA4fPozvv/8eZ8+eRXp6Oo4ePQoAWLBgAdq0aQO9Xo9bt25h2bJlAACLxYJhw4ahVatWyM3NxalTp/DZZ59VmnZx9uxZREZGIj8/H/PmzcPkyZORnp6Ob775BufOncM777yDr7/+2tb+zJkzCAwMhE6nw8aNGzFp0iRcunSpSqz//ve/MXbsWKxduxaFhYVYsmQJRowYAb1eDwB49tlnERMTA51Oh7fffrvKVJA/OnjwILp06YLIyMha8yMIApYvX47ly5ejuLi4xnbz58/HsWPHcOrUqVr7cyQW3ERERBJQyAT4KOQoNHGEu7F6MHL84GfSpEmV7n/ttdfg6emJFi1aoE+fPjh37hwAQKVS4ebNm8jNzYVSqUTv3r0BVBTHV69exeuvvw61Wo3Q0FDMmTMHH374oa3P5s2bY/r06VAoFBg3bhz0ej1mzZoFX19ftGjRAt26dbM9DlAxbSM1NRVKpRKJiYkYPHgwdu3aVeW5bN68Gf/zP/+DXr16QSaTYejQoejYsSP279+PvLw8ZGdnY8WKFVCr1YiLi8O4ceNqzEtBQQFCQ0PrlMNBgwYhNjYWq1evrrFNQEAAXnrppUqj42LjxjdEREQSaaJQoNDIlUqcQXx8fJVjd+/ehY+PzyMdt1qtNT7m6tWrMW3atBrvDwn5fVMkrVZrG8VdtWoVXn31VfTr1w9KpRJTpkxBamoqcnJyoNPp4O/vbzvPYrEgPDy8xj5rexygokCXyX4fn42MjMT169erxJqTk4OsrCxs3rzZdsxoNGLQoEG4ceMGfH194evrW6mf8+fPV/u8AwIC8NNPP9WYlz9bsWIFEhMT8be//a3GNnPmzMG6detw6NChOvdrTyy4iYiIJBKoVKDUwrW4ncGZM2cAVN1p8u7du9W2r8txR+00+WDe8saNG/Htt99iwIAB6Ny5MyIiIhAWFoacnBy7Pdb169dhsVhsRXdeXh46depUpV1ERATmzp1b7eofeXl5+O233yr9Q5KXl1fjYyYmJmLt2rXIy8tDRETEQ2Ps2rUr+vfvjxUrVtTYxsvLCwsWLMD8+fPRsmXLh/Zpb5xSQkREJJE5ESEYFdxE6jDIxWRkZODq1asAAD8/P8jlcsjlcnTu3BlBQUFYunQp7t+/D4vFgkuXLuH48eMNfqzCwkK88cYbMBqNOHz4MA4cOIBnnnmmSrspU6bgnXfewYkTJ2CxWFBaWopjx47h2rVriIiIQLdu3TB//nyUlZXh/Pnz2LFjR42P2b9/fzz55JMYMWIETp06BaPRiJKSEuzYsQMrV66s9pxly5Zh06ZNuHfvXo39Tp8+HTqdDocPH65/Ih4RC24iIiKJmK3AHc7hbrRmzZpVaR3uP07tqM3Zs2fRvXt3eHp6ok+fPpg1axYSEhIgl8vxxRdf4NKlS2jdujX8/f2RnJyMmzdvNjjG+Ph45OfnIygoCFOmTMHWrVsRExNTpV2nTp2wbds2/P3vf0dAQAAiIyPx1ltv2VZI2blzJy5cuIDAwEDMmDEDzz33XK2Pm5GRgaeffhoTJ06En58f2rRpgy+++AJPPfVUte3btWuHUaNGoaSkpMY+VSoV0tLSUFBQUI8M2IdgrW1ykRu4ceMGdu7ciYkTJyIoKMiufVssFhQUFCAgIKDS/CZnZrVaYTabIZfLIQiC1OHUCfMsDuZZPMy1OFwhz1lFd/Gprgj/aFOxGgPz/DudTocNGzZgypQpdb6A7lE8WAP7z1NKHsXPP//MzWwIQCOZw/3EE0/A398fJgeMIvj7+8Nisdj+g3MVZhfbTph5FgfzLB7mWhzOnmdfmYAikwlGo7FSgc08o9KFf0SurlEU3OfPn0eHDh04wg2OnoiFeRaHK+YZYK7F4gp5DvLwgNEKGAQZvBVy5vkPdDqd3foiklqjKLiLi4thMpns/uYlCIKtX1d5Y3zAlWJmnsXBPIuHuRaHK+Q5QFXxMVxkNsNH+ftHsjPH/GeOyrMjvpUmkopz/stPRETUCGhkMkwICYC3XC51KETkQI1ihJuIiMgZCYKAvwZyrjKRu2PBTUREJKGDBb/BWyFDd19vqUMhOG6zGmrcWHATERFJ6OcSA1QygQW3xLh8HzkS53ATERFJqIlSjkIjLxAkcmcsuImIiCTURKlAIVfkIHJrLLiJiIgkFKBUwOTemz4TNXqcw01ERCShzj5e6OzjJXUYRORAHOEmIiKSkNVqhb7ciHIn3X6eiB4dC24iIiIJWQC88HMuLpeWSR0KETkIC24iIiIJyQUBfgo5irhSCZHbYsFNREQkMX+uVELk1lhwExERSayJQsG1uIncGFcpISIiktigJr5QyQSpwyAiB2HBTUREJLHHvbUAKlYsISL3wyklREREErtcasDWGzqpwyAiB2HBTUREJLESswVHi37jCDeRm2LBTUREJLEmSgVMVuCemZvfELkjFtxEREQSa6KouKSqgCuVELklFtxEREQS08hlCFIqUMrt3YncElcpISIicgL/jImC1WqFiRvgELkdFtxERERO4K7JDJPFAm8ux03kdjilhIiIyAl8cEuPjPxCqcMgIgdgwU1EROQE/Lm9O5HbYsFNRETkBAKUChRw/jaRW2LBTURE5AQCVQoUGE3c/IbIDTnFRZPFxcVYv349zp07B41Gg5EjR2L48OHVti0vL8e2bdvw1Vdfoby8HKGhoVi2bBm0Wq3IURMREdlPC7UHJoUEguU2kftxioJ78+bNMBqNSE9PR35+PhYtWoSwsDDExcVVabthwwYYDAa8/fbb8PX1RW5uLpRKpQRRExER2Y+fUoEEfx8uC0jkhiSfUmIwGHDy5ElMmDABWq0WUVFRSExMxKFDh6q0vXbtGrKzszFjxgz4+/tDJpMhOjqaBTcREbmF927o8HOJQeowiMjOJB/hvn79OqxWKyIjI23HoqOjkZ2dXaXtpUuXEBwcjF27duHYsWPw8fHBiBEjkJiYKGbIREREDvFTiQGhSjnaSR0IEdmV5AW3wWCoMv/a09MTpaWlVdrqdDrk5uaiS5cuSE9PR05ODhYvXozQ0FDExsba2un1euj1ets55eXlAACLnbfMfdCfvft1JKvVCovFAovFAkFwjd0VmGdxMM/iYa7F4Yp5DlDKoTeamGciNyN5wa1Wq6sU1yUlJdBoNFXaenh4QCaTYcyYMVAqlWjdujV69uyJ06dPVyq4d+/ejS1btthu9+3bFwBw69YthzyH/Px8h/RLlTHP4mCexcNci8OV8qw1GnGjvBy3b9+WOpR6c6U8E4lN8oK7efPmAIC8vDxEREQAAK5cuWL7/Y+ioqLq1GdSUpKtyNbpdDhw4AAAICQkxA4R/85isSA/Px/BwcGQySSfDl8nVqsVJpMJCoXCpUZPmGfHY57Fw1yLwxXz3LLgDn4pMaBp06aNPs+OGiQjkoLkBbdarUbPnj2xfft2zJ49GzqdDgcPHsTMmTOrtI2NjUVISAg+/vhjjB49Gjk5OTh58iQWLFhQqV1gYCACAwMBAD4+PlCpVADgsDdcmUzmMm/mVqvVFq+rvJk/wDyLg3kWD3MtDlfK85BAf5hMJuaZyM04xV/G1KlTIZfLkZKSgsWLFyMpKcm2JGBycjIuXLgAAJDL5Vi4cCH+85//YMyYMXjjjTfw/PPPV5pOQkRE5KpMFityDeUwc/MbIrci+Qg3AHh5eSE1NbXa+zIyMirdDgsLw8qVK8UIi4iISFRFJhMW5t7EujaRCFQ5xZgYEdkB/5qJiIichJ+iYhysyMjNb4jcCQtuIiIiJ6GUCfCWy1DI3SaJ3AoLbiIiIifip5CjyGiWOgwisiOnmMNNREREFUYH+iNE4yF1GERkRyy4iYiInMjjXhooFPx4JnInnFJCRETkRM7cK8GOW3qpwyAiO2LBTURE5ETumc04e69E6jCIyI5YcBMRETmRAKUChUYTrNz8hshtsOAmIiJyIv4KOcqsVtw3W6QOhYjshAU3ERGREwlQKNBMpcR9CwtuInfBy6CJiIiciFYuw+rWERAEQepQiMhOOMJNRETkZG6WlUNfbpQ6DCKyExbcRERETib9ph5Hiu5KHQYR2QkLbiIiIicToFSgwGiSOgwishMW3ERERE6GBTeRe2HBTURE5GRYcBO5F65SQkRE5GQ6eGrQRMmPaCJ3wb9mIiIiJxOoUiLIQyV1GERkJ5xSQkRE5GRMFiuW51zHdUO51KEQkR2w4CYiInIyCpmASyVluMW1uIncAgtuIiIiJ8QLJ4ncBwtuIiIiJ8SCm8h98KJJIiIiJ9RC4wGlIEgdBhHZAQtuIiIiJzS6aYDUIRCRnXBKCRERkRMqNpvx4/1SqcMgIjtgwU1EROSEfi0tw7KcG7BarVKHQkSPiAU3ERGREwpQKmC0WnHXbJY6FCJ6RCy4iYiInFDAf7d250olRK6vUVw06eXlBYVCYfev5axWq61fV/nK70GcrhIvwDyLhXkWD3MtDlfPs4cgwFMuQ0G5CdFq543fUXlWKBpFiUKNRKN4NT/xxBPw9/eHyWT/UQJ/f39YLBZYLBa79+1IZhf7ipJ5FgfzLB7mWhyunucZzQIRplI45PPLnhyRZ39/f7v1RSS1RlFwnz9/Hh06dEBQUJBd+7VYLCgoKEBAQABkMteYnWO1WmE2myGXyyG4yPquzLM4mGfxMNficIc8d/T1ljqkh3JUnnU6nd36IpJaoyi4i4uLYTKZ7P4hIQiCrV9X+QB6wJViZp7FwTyLh7kWhzvk+cuCOygymTHGidfkdlSenX1Un6g+XONffiIiokbovtnCtbiJ3AALbiIiIicVoFRwlRIiN8CCm4iIyEkFKBUoNJlgcZFVVoioeiy4iYiInFSwSolWGjUMFhbcRK6sUVw0SUQweMY8AAAQtElEQVRE5IqCVUq81iJM6jCI6BFxhJuIiMiJ/XS/FPpyo9RhENEjYMFNRETkxLbe1OH0vftSh0FEj4AFNxERkRPjSiVEro8FNxERkRNrwoKbyOWx4CYiInJiAQoFCllwE7k0rlJCRETkxHr5eaOjt1bqMIjoEbDgJiIicmJBKiWCoJQ6DCJ6BJxSQkRE5MTuGE145ZerKOK0EiKXxYKbiIjIiWnlMlwxlPHCSSIXxoKbiIjIialkMnjLZSy4iVwYC24iIiInF6BUsuAmcmEsuImIiJxcrJcGnnJ+ZBO5Kq5SQkRE5OTGhwRKHQIRPQL+u0xEROTk9OVG/Ke4ROowiKiBWHATERE5uYslBrxzPV/qMIiogVhwExERObkAZcX27harVepQiKgBWHATERE5uSYKBSwA7pjMUodCRA3AgpuIiMjJNVHKAQCFXBqQyCWx4CYiInJyKpkMr7VojjAPldShEFEDcFlAIiIiFxCj1UgdAhE1kFMU3MXFxVi/fj3OnTsHjUaDkSNHYvjw4dW2HTZsGDw8PCAIAgCgXbt2SEtLEzFaIiIi8W2/qYeXXIaRwU2kDoWI6skpCu7NmzfDaDQiPT0d+fn5WLRoEcLCwhAXF1dt+zVr1iAsLEzkKImIiKRTZrWgqIxzuIlckeRzuA0GA06ePIkJEyZAq9UiKioKiYmJOHTokNShEREROQ1/hQJFvGiSyCVJPsJ9/fp1WK1WREZG2o5FR0cjOzu7xnMWLlwIs9mM1q1bIyUlBREREWKESkREJBl/hQJFXBaQyCVJXnAbDAZotdpKxzw9PVFaWlpt++XLlyMmJgZGoxGZmZlYvHgxNmzYUKkPvV4PvV4PANDpdCgvLwcAWCwWu8b+oD979+tIVqsVFosFFovFNg/e2THP4mCexcNci8Pd8hyqUiBarXK65+OKeSYSm+QFt1qtrlJcl5SUQKOp/mrs2NhYAIBSqcT48eNx7NgxXLx4sdJ87927d2PLli2223379gUA3Lp1y97hAwDy87ndrhiYZ3Ewz+JhrsXhLnn2AfCMwnGfZY/KXfJM5AiSF9zNmzcHAOTl5dmmhly5cqXO00SqG2lJSkqyFdk6nQ4HDhwAAISEhNgjZBuLxYL8/HwEBwdDJpN8OnydWK1WmEwmKBQKlxqlYp4dj3kWD3MtDnfLs9lqxZl799FOq4G3Qi5RhFU5Ks/O+o8FUUNIXnCr1Wr07NkT27dvx+zZs6HT6XDw4EHMnDmzStu8vDwYjUZERUXBZDJh9+7dKC8vR0xMTKV2gYGBCAwMBAD4+PhAparYKMBRb7gymcyl3swfxOsqH5oPMM/iYJ7Fw1yLw23ybLVi3fV8vBIZiliVUpoAa+FKeSYSm+QFNwBMnToV69atQ0pKCjQaDZKSkmxTRJKTk/Hqq6+iffv2uHPnDjZu3Ai9Xg+VSoVWrVrhtddeg5eXl8TPgIiIyLFkgsALJ4lclFMU3F5eXkhNTa32voyMDNvvf/nLX7Bx40axwiIiInIq/go5lwYkckH87oeIiMhF+CsVKDKx4CZyNU4xwk1EREQPN7ZpAFScJ03kclhwExERuYgQD5XUIRBRA/DfZCIiIhdx7t59pP16TeowiKieWHATERG5CIUg4HJpGaxWq9ShEFE9sOAmIiJyEf4KOYxWK+6buY06kSthwU1EROQi/JUVl15xpRIi18KCm4iIyEV4ymSI9/aEANfa7ZOoseMqJURERC5CEAS8HNlM6jCIqJ44wk1ERORCLt4vxa+lBqnDIKJ64Ag3ERGRC/my4Df4KORooVFLHQoR1VGjKbj1er3D+r5165bD+rY3hUIBf39/6HQ6mFzsohvmWRzMs3iYa3G4W55VZeW4UWrFjRtGkaOrnb3z7MjPbSKxuX3BrdVqoVQqkZmZafe+DQYDcnNzERkZCbWaIw2OwjyLg3kWD3MtDnfP8ztSB/BfjsyzUqmEVqu1a59EUhCsjWD1/Dt37qCkpMTu/V6+fBmzZ8/GmjVr0LJlS7v3TxWYZ3Ewz+JhrsXBPIvDkXnWarXw8/Oza59EUnD7EW4A8PPzc8gf7N27dwEAQUFBCA0NtXv/VIF5FgfzLB7mWhzMsziYZ6KH4yolREREREQOJE9LS0uTOghXptFoEB8fzzlmDsY8i4N5Fg9zLQ7mWRzMM1HtGsUcbiIiIiIiqXBKCRERERGRA7HgJiIiIiJyoEaxSsmjKC4uxvr163Hu3DloNBqMHDkSw4cPr7btsGHD4OHhAUEQAADt2rUDp8jXTX3yXF5ejm3btuGrr75CeXk5QkNDsWzZMs4drIO65jkrKwsbNmyw3bZarSgrK0Nqaip69OghZsguqz6v6RMnTuDDDz+EXq+Hn58fkpKSkJiYKHLErqk+eT527BgyMjJQUFCAFi1aYMaMGQgLCxM5Yvexd+9eHD16FDk5OejevTv+/ve/Sx0SkdNiwf0QmzdvhtFoRHp6OvLz87Fo0SKEhYUhLi6u2vZr1qzhG3gD1CfPGzZsgMFgwNtvvw1fX1/k5uZCqVRKELXrqWueExISkJCQYLt99uxZrFq1qsbXPVVV11zrdDqsXr0aqamp6Ny5M3766ScsXrwYLVu25NrRdVDXPP/www/YsmULli5diqioKOzatQvLli3DunXrIJfLJYretTVp0gTJycn49ttvce/ePanDIXJqnFJSC4PBgJMnT2LChAnQarWIiopCYmIiDh06JHVobqU+eb527Rqys7MxY8YM+Pv7QyaTITo6mgV3HTzK6/nQoUPo1asXPDw8RIjU9dUn1zqdDp6enujSpQsEQUDbtm0RFhaGvLw8CSJ3LfXJ86lTp9CjRw+0bNkScrkco0ePxu3bt3HhwgUJIncPPXr0QLdu3eDj4yN1KEROjwV3La5fvw6r1YrIyEjbsejo6Fo/CBcuXIgJEyZgyZIl/MCso/rk+dKlSwgODsauXbswfvx4/O///i8OHjwoZrguqyGvZ6BiU4tvvvkGAwcOdHSIbqM+uY6JiUHz5s2RnZ0Ni8WCH374Abdv30b79u3FDNkl1SfPf16Q68HtnJwch8ZIRARwSkmtDAZDlXnBnp6eKC0trbb98uXLERMTA6PRiMzMTCxevBgbNmzg3OKHqE+edTodcnNz0aVLF6SnpyMnJweLFy9GaGgoYmNjxQrZJdX39fzA8ePH0axZM7Rt29aR4bmV+uRaLpejf//+WLt2LcrKyiAIAqZPn47g4GCxwnVZ9clzXFwcVq5cicTERERHR2PXrl0wm80oKysTK1wiasQ4wl0LtVpd5Y27pKQEGo2m2vaxsbFQKpXQarUYP3485HI5Ll68KEaoLq0+efbw8IBMJsOYMWOgVCrRunVr9OzZE6dPnxYrXJdV39fzA4cPH8aAAQMcGZrbqU+uz58/j/T0dLz22mvIzMzEP/7xD2RkZPA1XQf1yfPjjz+OSZMmYc2aNUhJSUFZWRnCw8MRGBgoVrhE1Iix4K5F8+bNAaDS15NXrlxBREREnc5/sFoJ1a4+eY6KihIrLLfTkNfz5cuXkZeXh379+jk8PndSn1zn5OTgscceQ9u2bSGTyRAREYH4+HicPXtWtHhdVX1f04MHD8bGjRuxY8cOjB49Gvn5+WjdurUosRJR48aCuxZqtRo9e/bE9u3bUVJSgtzcXBw8eBCDBg2q0jYvLw+XL1+2fUW5c+dOlJeXIyYmRoLIXUt98hwbG4uQkBB8/PHHMJvNuHz5Mk6ePInOnTtLELlrqU+eHzhy5Aji4uLg7+8vYqSurz65bt26NX788UdcunQJQMWFwWfOnEF0dLTYYbuc+uTZaDTiypUrsFgsKCoqwj//+U90796dq0o9ArPZjPLyclgsFlgsFpSXl8NkMkkdFpFT4tbuD1FcXIx169bZ1nh9+umnbWu8Jicn49VXX0X79u3xn//8Bxs3boRer4dKpUKrVq2QkpLCD806qmuegYqCZN26dbh8+TKaNGmCZ555hhf01VF98mw0GpGSkoIXXngB3bp1kzJsl1SfXB84cACfffYZioqK4OnpiYSEBIwfPx4yGcdEHqaueS4pKcErr7yCmzdvQqVSoXfv3khJSeHKO49g586d+Oijjyod69+/P2bNmiVRRETOiwU3EREREZEDcfiEiIiIiMiBWHATERERETkQC24iIiIiIgdiwU1ERERE5EAsuImIiIiIHIgFNxERERGRA7HgJiIiIiJyIBbcREREREQOxIKbiIiIiMiBWHATkd2kpaVBEATbT0BAAHr16oX9+/dLEk9WVhYEQcCZM2dsxwRBwJtvvilJPERE1Dix4CYiu9JoNMjOzkZ2dja2bNkCg8GAp556Cl9//bXUoREREUlCIXUAROReZDIZunXrZrvdtWtXhIeHY9u2bejRo4eEkREREUmDI9xE5FDNmzdHUFAQ8vLybMeys7PRv39/eHp6wtfXF2PHjkV+fn6l88rKyrBw4UK0aNECHh4eCAsLQ0pKSqU+hg0bhtDQUHh6eqJjx47Yvn27WE+LiIiozjjCTUQOVVxcjMLCQkRHRwOoKJQTEhIwZMgQ7Nq1C/fv38fChQsxfPhwZGdn285LSkrC0aNHMX/+fHTr1g06nQ6ZmZm2+3Nzc9GzZ09MmzYNarUaJ0+exPPPPw+LxYJJkyaJ/jyJiIhqwoKbiOzOZDIBAG7cuIG5c+fC29sbM2fOBACkpqYiPj4emZmZEAQBANChQwfExsZi//79GDJkCA4dOoR9+/Zh586dePbZZ239/vH3MWPG2H63Wq3o06cPrl27hs2bN7PgJiIip8KCm4js6v79+1Aqlbbbcrkcn332GWJiYlBSUoKTJ0/izTffhNlstrVp06YNwsPDcfr0aQwZMgRHjhyBVqutVFT/WVFREV599VV89tlnuH79uq2/gIAAxz05IiKiBuAcbiKyK41Gg9OnT+PUqVP44IMP0KxZM0ycOBE3b95EUVERzGYzZs+eDaVSWeknLy8PV69eBQAUFBSgWbNmthHw6qSkpODDDz/Eyy+/jIMHD+L06dN47rnnYDAYxHqqREREdcIRbiKyK5lMhvj4eABAly5dEBMTg65du2LJkiV48803IQgC5s+fjxEjRlQ5NzAwEEDFKPXNmzdhtVqrLboNBgP27t2L1atX44UXXrAdt1gsDnpWREREDccRbiJyqPj4eDz77LNIT0/HvXv30L17d1y8eBHx8fFVfqKiogAAAwcORElJCTIyMqrts6ysDBaLBSqVynbs3r17+Pzzz8V4SkRERPXCEW4icrhFixbho48+wtq1a7Fq1Sr0798fo0ePxpgxY+Dv749r167h0KFDmDx5MhISEjBw4EAMGTIEzz33HC5fvoyuXbuisLAQn3zyCXbt2gVfX1907twZK1euRFBQEBQKBVauXAlfX98qywsSERFJjSPcRORwMTExGDNmDDZu3Ij27dvjxIkTKC4uxuTJkzFkyBAsWbIEWq0WrVq1sp2ze/duvPjii9i8eTOefPJJzJkzB15eXrb7d+7ciVatWmHSpEl48cUXMWrUKEycOFGKp0dERFQrwWq1WqUOgoiIiIjIXXGEm4iIiIjIgVhwExERERE5EAtuIiIiIiIHYsFNRERERORALLiJiIiIiByIBTcRERERkQOx4CYiIiIiciAW3EREREREDsSCm4iIiIjIgVhwExERERE5EAtuIiIiIiIH+v/7QaNPFiwuVAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 60 } ] }, { "cell_type": "markdown", "metadata": { "id": "aSOdgXSpHiYx" }, "source": [ "# Blast comparison" ] }, { "cell_type": "markdown", "metadata": { "id": "vvFBBYlmToXe" }, "source": [ "Let's do the same sort of analysis for a BLAST baseline." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VXqzzmJOHjr8", "outputId": "9cbe0846-be9e-412f-c9bd-dd8950df2e5b" }, "source": [ "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/blast_output/random/blast_out_test.tsv\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_GO/eval_test.fasta\n", "!wget -qN https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/blast_baseline/fasta_files/SWISSPROT_RANDOM_GO/train.fasta\n", "train_ground_truth = colab_evaluation.make_tidy_df_from_ground_truth(baseline_utils.load_ground_truth('train.fasta')).rename(columns={\"up_id\":\"train_seq_id\"}).drop(columns=[\"gt\"])" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "877044it [00:14, 58510.66it/s]\n", "100%|██████████| 438522/438522 [00:11<00:00, 39569.33it/s]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "NwD3Ers7IIBe" }, "source": [ "blast_out = colab_evaluation.read_blast_table(\"blast_out_test.tsv\")\n", "blast_df = blast_out.merge(train_ground_truth,\n", " left_on=\"target\",\n", " right_on=\"train_seq_id\")\n", "blast_df.rename(columns={'bit_score': 'value', \"query\": \"up_id\"}, inplace=True)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kcqNR0hQJwHv", "outputId": "86bc6d3f-bf47-4cdd-9842-e31854f40216" }, "source": [ "min_decision_threshold = 0\n", "blast_pr_data = colab_evaluation.get_pr_curve_df(blast_df, ground_truth_df)\n", "blast_pr_data['method'] = 'BLAST'" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:01<00:00, 1.61s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533 }, "id": "2IIjwAbrOmTT", "outputId": "54aabba5-79c2-44a7-f87b-5f206c701863" }, "source": [ "cnn_pr_data['method'] = 'CNN'\n", "ens_cnn_pr_data['method'] = 'Ensembled CNN'\n", "method_comparison = pd.concat([\n", " cnn_pr_data.drop(index=0),\n", " ens_cnn_pr_data.drop(index=0),\n", " blast_pr_data.drop(index=0)\n", "],\n", " ignore_index=True)\n", "ggplot(method_comparison,\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"method\")) + geom_line() + coord_cartesian(\n", " xlim=(0.5, 1), ylim=(0.5, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\")\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning:\n", "\n", "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", "\n" ] }, { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 57 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 174 }, "id": "DdPDKvc7axjD", "outputId": "d5dc4b2b-ad19-485f-c2db-bdaa2179d39c" }, "source": [ "method_comparison.groupby(\"method\")[['f1']].agg(max)" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
f1
method
BLAST0.902019
CNN0.884862
Ensembled CNN0.899472
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" ], "text/plain": [ " f1\n", "method \n", "BLAST 0.902019\n", "CNN 0.884862\n", "Ensembled CNN 0.899472" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 144 }, "id": "JoW4Rg7Ln2Bd", "outputId": "ced739a3-ddc9-41bd-e6de-e6dc1cd9d3a9" }, "source": [ "method_comparison.sort_values('f1',\n", " ascending=False).drop_duplicates(['method'])\n" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
groupprecisionrecallthresholdf1method
5590all0.8954590.90867646.6000000.902019BLAST
2780all0.9194770.8803190.3768710.899472Ensembled CNN
632all0.9180180.8540180.7101020.884862CNN
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" ], "text/plain": [ " group precision recall threshold f1 method\n", "5590 all 0.895459 0.908676 46.600000 0.902019 BLAST\n", "2780 all 0.919477 0.880319 0.376871 0.899472 Ensembled CNN\n", "632 all 0.918018 0.854018 0.710102 0.884862 CNN" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "_VyNMIBZcAfk" }, "source": [ "Let's investigate what's going on at the left hand side of the graph where the CNN and ensemble achieve greater precision than BLAST." ] }, { "cell_type": "code", "metadata": { "id": "UQ0as8vXMNEW" }, "source": [ "def get_x_where_y_is_closest_to_z(df, x, y, z):\n", " return df.iloc[(df[y] - z).abs().argsort()[:1]][x]\n", "\n", "\n", "cnn_threshold = float(\n", " get_x_where_y_is_closest_to_z(cnn_pr_data,\n", " x=\"threshold\",\n", " y=\"recall\",\n", " z=0.96))\n", "blast_threshold = float(\n", " get_x_where_y_is_closest_to_z(blast_pr_data,\n", " x=\"threshold\",\n", " y=\"recall\",\n", " z=0.96))\n", "\n", "cnn_results = colab_evaluation.assign_tp_fp_fn(ens_predictions_df,\n", " ground_truth_df, cnn_threshold)\n", "\n", "blast_results = colab_evaluation.assign_tp_fp_fn(blast_df, ground_truth_df,\n", " blast_threshold)\n", "\n", "merged = cnn_results.merge(blast_results,\n", " how=\"outer\",\n", " suffixes=(\"_ens_cnn\", \"_blast\"),\n", " left_on=[\"label\", \"up_id\", \"gt\"],\n", " right_on=[\"label\", \"up_id\", \"gt\"])\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "v4f6jjepehLW" }, "source": [ "blast_info = blast_out[['up_id', 'target', 'pc_identity']]\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PF2TB_jNomft" }, "source": [ "Let's list some of the BLAST false-positives in case we want to investigate what's going on." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49 }, "id": "fchf2u1wdihM", "outputId": "27c81a57-e73c-47c3-e996-d9df10046221" }, "source": [ "merged.query(\"fp_blast==True and fp_ens_cnn==False\").head()" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
up_idlabelvalue_ens_cnngttp_ens_cnnfp_ens_cnnfn_ens_cnntargetpc_identityalignment_lengthvalue_blasttrain_seq_idtp_blastfp_blastfn_blast
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [up_id, label, value_ens_cnn, gt, tp_ens_cnn, fp_ens_cnn, fn_ens_cnn, target, pc_identity, alignment_length, value_blast, train_seq_id, tp_blast, fp_blast, fn_blast]\n", "Index: []" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "kXvgYdoIEC8x" }, "source": [ "# An ensemble of BLAST and ensembled-CNNs\n", "\n", "We've seen that the CNN-ensemble and BLAST have different strengths - at lower recalls the CNN appears to have greater precision than BLAST at lower recalls, but BLAST has better recall at lower precisions. Can we combine these approaches to get a predictor with the best of both worlds?" ] }, { "cell_type": "code", "metadata": { "id": "8c3FW0x6GgR5" }, "source": [ "blast_and_cnn_ensemble = ens_predictions_df.merge(blast_df,\n", " how=\"outer\",\n", " suffixes=(\"_ens_cnn\",\n", " \"_blast\"),\n", " left_on=[\"label\", \"up_id\"],\n", " right_on=[\"label\", \"up_id\"])\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "WqkATRN3GqDE" }, "source": [ "blast_and_cnn_ensemble = blast_and_cnn_ensemble.fillna(False)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "QNO1n03TJp6z" }, "source": [ "We will create a simple ensemble where the value of the predictor is simply the multiple of the probability assigned by the ensemble of neural networks and the bit-score linking this sequence to to an example with this label by BLAST." ] }, { "cell_type": "code", "metadata": { "id": "8FbzU_6QGvZS" }, "source": [ "blast_and_cnn_ensemble['value'] = blast_and_cnn_ensemble[\n", " 'value_ens_cnn'] * blast_and_cnn_ensemble['value_blast']\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ahX0RAesG6Nb", "outputId": "2c924900-abf4-492a-eba3-9152d9f70440" }, "source": [ "blast_and_cnn_ensemble_pr = colab_evaluation.get_pr_curve_df(\n", " blast_and_cnn_ensemble, ground_truth_df)" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:05<00:00, 5.61s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "JjVIWQabHI9z" }, "source": [ "blast_and_cnn_ensemble_pr['method'] = 'Ensemble of BLAST with Ensembled-CNN'\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533 }, "id": "BXLI2DaoHIRd", "outputId": "701d88b0-5ef9-4e5f-a8fd-a7107aaeb1c6" }, "source": [ "cnn_pr_data['method'] = 'CNN'\n", "ens_cnn_pr_data['method'] = 'Ensembled CNN'\n", "method_comparison = pd.concat([\n", " cnn_pr_data.drop(index=0),\n", " ens_cnn_pr_data.drop(index=0),\n", " blast_pr_data.drop(index=0),\n", " blast_and_cnn_ensemble_pr.drop(index=0)\n", "],\n", " ignore_index=True)\n", "ggplot(method_comparison,\n", " aes(x=\"recall\", y=\"precision\",\n", " color=\"method\")) + geom_line() + coord_cartesian(\n", " xlim=(0.5, 1), ylim=(0.5, 1)) + theme_bw() + labs(\n", " x=\"Recall\", y=\"Precision\", color=\"Method\")\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/plotnine/utils.py:1246: FutureWarning:\n", "\n", "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", "\n" ] }, { "output_type": "display_data", "data": { "image/png": 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\n", "\n", "" ] }, "metadata": {}, "output_type": "display_data" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "mLOljUevHki1", "outputId": "9bbb9b12-ea7c-4fbd-f7da-911682b671b7" }, "source": [ "method_comparison.groupby(\"method\")[['f1']].agg(max)" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "
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f1
method
BLAST0.902019
CNN0.884862
Ensemble of BLAST with Ensembled-CNN0.908158
Ensembled CNN0.899472
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" ], "text/plain": [ " f1\n", "method \n", "BLAST 0.902019\n", "CNN 0.884862\n", "Ensemble of BLAST with Ensembled-CNN 0.908158\n", "Ensembled CNN 0.899472" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "bfV0tzotJ5PK" }, "source": [ "As we hoped, this method seems to provide the best of both worlds, boosting recall compared to the ensembled CNN alone and boosting recall compared to BLAST alone, for a better F1." ] }, { "cell_type": "markdown", "metadata": { "id": "Nz4dbIOk0M9a" }, "source": [ "# Bootstrapping" ] }, { "cell_type": "code", "metadata": { "id": "6ZrJzUIVcjm6" }, "source": [ "import collections\n", "\n", "THRESHOLD = 1e-3\n", "\n", "\n", "def get_bootstrapped_pr_curves(predictions_df,\n", " ground_truth_df,\n", " grouping=None,\n", " n=100,\n", " method_label=None,\n", " sample_with_replacement=True):\n", "\n", " joined = predictions_df[predictions_df.value > THRESHOLD].merge(\n", " ground_truth_df, on=['up_id', 'label'], how='outer')\n", " print(joined.shape)\n", " #joined.loc[joined['value'].isna(),'value']=0\n", " #joined.loc[joined['gt'].isna(),'gt']=False\n", " unique_up_ids = joined['up_id'].unique()\n", "\n", " pr_samples = []\n", " for _ in tqdm.tqdm(range(n)):\n", " sampled_up_ids = np.random.choice(unique_up_ids, len(unique_up_ids),\n", " sample_with_replacement)\n", "\n", " count_by_sample = collections.Counter(sampled_up_ids)\n", " count_by_sample_ordered = [count_by_sample[x] for x in joined.up_id]\n", " print(len(sampled_up_ids), len(unique_up_ids),\n", " sum(count_by_sample.values()), \"ee\")\n", "\n", " joined_sampled = pd.DataFrame(np.repeat(joined.values,\n", " count_by_sample_ordered,\n", " axis=0),\n", " columns=joined.columns)\n", " print(joined_sampled.shape, joined.shape, \"cc\")\n", " unique_suffixes_counter = collections.defaultdict(lambda: 0)\n", " unique_suffixes = []\n", " for row in joined_sampled.values:\n", " lookup_key = (row[0], row[1])\n", " unique_suffixes.append(unique_suffixes_counter[lookup_key])\n", " unique_suffixes_counter[lookup_key] += 1\n", "\n", " joined_sampled['up_id'] = [\n", " f'{x}-{y}' for x, y in zip(joined_sampled.up_id, unique_suffixes)\n", " ]\n", "\n", " pred = joined_sampled[joined_sampled['value'].notna()][[\n", " 'up_id', 'label', 'value'\n", " ]]\n", " #return joined_sampled\n", " gt = joined_sampled[joined_sampled['gt'].notna()][[\n", " 'up_id', 'label', 'gt'\n", " ]]\n", " print(pred.shape)\n", "\n", " pr_curves = colab_evaluation.get_pr_curve_df(pred,\n", " gt,\n", " grouping=grouping)\n", " pr_curves.loc[pr_curves['threshold'] == 0.0, 'precision'] = 0\n", " pr_curves['type'] = method_label\n", " pr_samples.append(pr_curves)\n", " return pr_samples\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/" }, "id": "oiUrFouj0qKF", "outputId": "a1fa6db8-33a1-4467-d034-9a3930ea4f70" }, "source": [ "n = 100\n", "non_ensembled_prs = get_bootstrapped_pr_curves(predictions_df,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"CNN\")\n", "ensembled_prs = get_bootstrapped_pr_curves(ens_predictions_df,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"Ensemble\")\n", "blast_prs = get_bootstrapped_pr_curves(blast_df,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"Blast\")\n", "blast_and_cnn_ensemble_prs = get_bootstrapped_pr_curves(\n", " blast_and_cnn_ensemble,\n", " ground_truth_df,\n", " n=n,\n", " method_label=\"Blast/CNN-ensemble\")\n" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r 0%| | 0/100 [00:000.5\")\n", " interp_recall_fn = interp1d(single_curve.recall,\n", " single_curve.precision,\n", " bounds_error=False)\n", " recall = np.linspace(0.5, 1, 5001)\n", " interpolated_precisions = interp_recall_fn(recall)\n", " return pd.DataFrame({\n", " \"type\": single_curve.type.to_list()[0],\n", " \"group\": single_curve.group.to_list()[0],\n", " \"precision\": interpolated_precisions,\n", " \"recall\": recall\n", " })\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "pnHKZRColhaK" }, "source": [ "curves = [\n", " ensembled_prs, non_ensembled_prs, blast_and_cnn_ensemble_prs, blast_prs\n", "]\n", "dfs = []\n", "\n", "for curve_set in curves:\n", " for c2 in curve_set:\n", " for group_name, df_group in c2.groupby(\"group\"):\n", " dfs.append(create_interpolated_df(df_group))\n", "all = pd.concat(dfs)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "hjcHpocRSm67" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "0YIkZtjeoJkU" }, "source": [ "def lower_func(x):\n", " return x.quantile(0.025)\n", "\n", "\n", "def upper_func(x):\n", " return x.quantile(0.975)\n", "\n", "\n", "for_graph = all.groupby(['type', 'group', 'recall'\n", " ]).agg(lower=(\"precision\", lower_func),\n", " upper=(\"precision\", upper_func)).reset_index()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "4ARrq09Kr8cM", "outputId": "b66acbee-5bfd-4869-fe22-71ad9b5d2881" }, "source": [ "a = get_bootstrapped_pr_curves(predictions_df,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"CNN\")[0]\n", "b = get_bootstrapped_pr_curves(ens_predictions_df,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"Ensemble\")[0]\n", "c = get_bootstrapped_pr_curves(blast_df,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"Blast\")[0]\n", "d = get_bootstrapped_pr_curves(blast_and_cnn_ensemble,\n", " ground_truth_df,\n", " n=1,\n", " sample_with_replacement=False,\n", " method_label=\"Blast/CNN-ensemble\")[0]\n", "all_single = pd.concat([a, b, c, d])\n" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r 0%| | 0/1 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
typegrouprecalllowerupper
0Blastall0.50000.8981470.908463
1Blastall0.50010.8981130.908471
2Blastall0.50020.8980790.908478
3Blastall0.50030.8980450.908486
4Blastall0.50040.8980110.908494
..................
19999Ensembleall0.9996NaNNaN
20000Ensembleall0.9997NaNNaN
20001Ensembleall0.9998NaNNaN
20002Ensembleall0.9999NaNNaN
20003Ensembleall1.0000NaNNaN
\n", "

20004 rows × 5 columns

\n", "" ], "text/plain": [ " type group recall lower upper\n", "0 Blast all 0.5000 0.898147 0.908463\n", "1 Blast all 0.5001 0.898113 0.908471\n", "2 Blast all 0.5002 0.898079 0.908478\n", "3 Blast all 0.5003 0.898045 0.908486\n", "4 Blast all 0.5004 0.898011 0.908494\n", "... ... ... ... ... ...\n", "19999 Ensemble all 0.9996 NaN NaN\n", "20000 Ensemble all 0.9997 NaN NaN\n", "20001 Ensemble all 0.9998 NaN NaN\n", "20002 Ensemble all 0.9999 NaN NaN\n", "20003 Ensemble all 1.0000 NaN NaN\n", "\n", "[20004 rows x 5 columns]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "Ii8vVgD9omWs", "outputId": "5b12c5c1-530b-4d8e-d912-e5875e671b91" }, "source": [ "import plotly.graph_objects as go\n", "\n", "fig = go.Figure()\n", "\n", "\n", "def get_color(index, transparent):\n", " colors = {\n", " 'CNN': [150, 0, 0],\n", " 'Ensemble': [0, 125, 125],\n", " 'Blast/CNN-ensemble': [0, 200, 0],\n", " 'Blast': [125, 0, 255]\n", " }\n", " transparency = 0.2 if transparent else 1\n", " return f\"rgba({colors[index][0]}, {colors[index][1]}, {colors[index][2]}, {transparency})\"\n", "\n", "\n", "colors = {\n", " 'CNN': 'green',\n", " 'Ensemble': 'red',\n", " 'Blast/CNN-ensemble': 'blue',\n", " 'Blast': 'orange'\n", "}\n", "for the_type, new in for_graph.groupby('type'):\n", " fig.add_trace(\n", " go.Scatter(\n", " x=new['recall'],\n", " y=new['upper'],\n", " mode='lines',\n", " showlegend=False,\n", " line=dict(width=0.0, color=get_color(the_type, False)),\n", " name=\"\",\n", " hoverinfo='skip',\n", " ))\n", " fig.add_trace(\n", " go.Scatter(\n", " x=new['recall'],\n", " y=new['lower'],\n", " name=the_type,\n", " hoverinfo='skip',\n", " showlegend=False,\n", " line=dict(width=0.0, color=get_color(the_type, False)),\n", " fill='tonexty',\n", " fillcolor=get_color(the_type, True),\n", " ))\n", "\n", "for the_type, new in all_single.query(\"precision>0.5\").groupby('type'):\n", " fig.add_trace(\n", " go.Scatter(x=new['recall'],\n", " y=new['precision'],\n", " name=the_type,\n", " line=dict(width=1, color=get_color(the_type, False))))\n", "\n", "fig.update_xaxes(title=\"Recall\", range=[0.8, 1])\n", "fig.update_yaxes(title=\"Precision\", range=[0.8, 1])\n", "fig.update_layout(template=\"plotly_white\")\n", "fig.update_layout(legend_title_text='Method')\n", "\n", "fig.update_layout(title=\"Precision and recall by method\", )\n", "\n", "fig.show()\n" ], "execution_count": null, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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and recall by method\"},\"xaxis\":{\"range\":[0.8,1],\"title\":{\"text\":\"Recall\"}},\"yaxis\":{\"range\":[0.8,1],\"title\":{\"text\":\"Precision\"}}}}'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "markdown", "metadata": { "id": "0q_aYzUWxMIL" }, "source": [ "## Examine effect of number of training examples on performance\n", "\n" ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "2IwNqpUVxMIM" }, "source": [ "def resample_with_replacement(df):\n", " indices = np.random.randint(0, df.shape[0], df.shape[0])\n", " return df.iloc[indices, :]\n", "\n", "\n", "def bootstrap(df, n=100):\n", " resampled_results = []\n", " for x in tqdm.tqdm(range(n), position=0):\n", " resampled = resample_with_replacement(df)\n", " data = colab_evaluation.stats_by_group(resampled.groupby('count_cut'))\n", " resampled_results.append(data)\n", " return pd.concat(resampled_results)\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "T5okaIZSxMIM" }, "source": [ "train_counts = train_ground_truth.groupby(\n", " \"label\", as_index=False).count().rename(columns={\"train_seq_id\": \"count\"})\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "t1FeqUo1xMIM", "outputId": "c76fec25-1455-4b34-edb7-c60994341a44" }, "source": [ "both = colab_evaluation.assign_tp_fp_fn(predictions_df, ground_truth_df,\n", " 0.625205)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "bootstrapped_data = bootstrap(both, n=5)\n", "bootstrapped_data['count_cut_str'] = bootstrapped_data['count_cut'].astype(str)" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 5/5 [00:30<00:00, 6.07s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "qgWT8a_e_RO0" }, "source": [ "bootstrapped_data['type'] = \"CNN\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "VD9BtlLoT4bq", "outputId": "b26ad2a2-aa37-4d30-9150-50ea9564f004" }, "source": [ "both = colab_evaluation.assign_tp_fp_fn(blast_df, ground_truth_df, 60.5)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "\n", "bootstrapped_data_blast = bootstrap(both, n=100)\n", "bootstrapped_data_blast['type'] = \"BLAST\"" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [13:28<00:00, 7.99s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "mkhFKt53Z10D", "outputId": "15d4a0cb-7397-4464-e46f-edf51323071b" }, "source": [ "both = colab_evaluation.assign_tp_fp_fn(ens_predictions_df, ground_truth_df,\n", " 0.25)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "\n", "bootstrapped_data_ens = bootstrap(both, n=100)\n", "bootstrapped_data_ens['type'] = \"Ensembled CNNs\"" ], "execution_count": null, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [21:19<00:00, 12.79s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "xLuIUP6ZKsHc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "933b201d-420c-4b8a-9fb9-a45145ac6854" }, "source": [ "both = colab_evaluation.assign_tp_fp_fn(blast_and_cnn_ensemble,\n", " ground_truth_df, 0.17)\n", "both = both.merge(train_counts, left_on=\"label\", right_on=\"label\", how=\"outer\")\n", "both.fillna(0)\n", "both['count_cut'] = pd.cut(both['count'],\n", " bins=(0, 5, 10, 20, 40, 100, 1000, 500000))\n", "\n", "bootstrapped_data_combo = bootstrap(both, n=100)\n", "bootstrapped_data_combo['type'] = \"Ensembled CNNs with BLAST\"\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 100/100 [35:02<00:00, 21.01s/it]\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "dIYzGXMzKBlt", "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "outputId": "9d4134d1-c662-4ba4-de2a-4ab235e0c218" }, "source": [ "bootstrapped_merge = pd.concat([\n", " bootstrapped_data_blast, bootstrapped_data, bootstrapped_data_ens,\n", " bootstrapped_data_combo\n", "],\n", " ignore_index=True)\n", "\n", "bootstrapped_merge['count_cut_str'] = bootstrapped_merge['count_cut'].astype(\n", " str)\n", "fig = px.box(bootstrapped_merge,\n", " width=700,\n", " color=\"type\",\n", " x=\"count_cut_str\",\n", " y=\"f1\",\n", " labels={\n", " \"count_cut_str\": \"Number of training examples per label\",\n", " \"f1\": \"F1\"\n", " },\n", " template=\"simple_white\")\n", "fig.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
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of training examples per label\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"F1\"}}}}'" ] }, "metadata": {}, "execution_count": 55 } ] }, { "cell_type": "code", "metadata": { "id": "vm5xO2aJsvDl", "colab": { "base_uri": "https://localhost:8080/", "height": 174 }, "outputId": "13bb589e-2329-4ae1-d77f-727b808ebf10" }, "source": [ "curves = [\n", " ensembled_prs, non_ensembled_prs, blast_and_cnn_ensemble_prs, blast_prs\n", "]\n", "dfs = []\n", "\n", "\n", "def create_f1(single_curve):\n", "\n", " return pd.DataFrame(\n", " {\n", " \"type\": single_curve.type.to_list()[0],\n", " \"group\": single_curve.group.to_list()[0],\n", " \"f1\": single_curve.f1.max()\n", " },\n", " index=[0])\n", "\n", "\n", "for curve_set in curves:\n", " for c2 in curve_set:\n", " for group_name, df_group in c2.groupby(\"group\"):\n", " dfs.append(create_f1(df_group.query(\"precision>0\")))\n", "f1 = pd.concat(dfs)\n", "\n", "\n", "def lower_func(x):\n", " return x.quantile(0.025)\n", "\n", "\n", "def upper_func(x):\n", " return x.quantile(0.975)\n", "\n", "\n", "f1_data = f1.groupby(['type',\n", " 'group']).agg(lower=(\"f1\", lower_func),\n", " upper=(\"f1\", upper_func)).reset_index()\n", "f1_data" ], "execution_count": 61, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " type group lower upper\n", "0 Blast all 0.899690 0.904512\n", "1 Blast/CNN-ensemble all 0.905858 0.910567\n", "2 CNN all 0.882438 0.887224\n", "3 Ensemble all 0.897309 0.901293" ] }, "metadata": {}, "execution_count": 61 } ] }, { "cell_type": "code", "metadata": { "id": "sRpW412LIdbU" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] } ================================================ FILE: evaluation.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Evaluation utilities for uniprot predictions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import itertools from typing import Dict, FrozenSet, List, Optional, Sequence, Set, Text, Tuple, Union import numpy as np import pandas as pd from pandas.core.groupby.generic import DataFrameGroupBy as pd_DataFrameGroupBy import inference import parenthood_lib import sklearn.metrics import tqdm FALSE_NEGATIVES_KEY = 'false_negatives' FALSE_POSITIVES_KEY = 'false_positives' TRUE_POSITIVES_KEY = 'true_positives' PrecisionRecallF1 = Tuple[float, float, float] def normalize_confidences( predictions, label_vocab, applicable_label_dict): """Set confidences of parent labels to the max of their children. Args: predictions: [num_sequences, num_labels] ndarray. label_vocab: list of vocab strings in an order that corresponds to `predictions`. applicable_label_dict: Mapping from labels to their parents (including indirect parents). Returns: A numpy array [num_sequences, num_labels] with confidences where: if label_vocab[k] in applicable_label_dict[label_vocab[j]], then arr[i, j] >= arr[i, k] for all i. """ vocab_indices = {v: i for i, v in enumerate(label_vocab)} children = parenthood_lib.reverse_map(applicable_label_dict, set(vocab_indices.keys())) # Only vectorize this along the sequences dimension as the number of children # varies between labels. label_confidences = [] for label in label_vocab: child_indices = np.array( [vocab_indices[child] for child in children[label]]) if child_indices.size > 1: confidences = np.max(predictions[:, child_indices], axis=1) label_confidences.append(confidences) else: label_confidences.append(predictions[:, vocab_indices[label]]) return np.stack(label_confidences, axis=1) def get_ground_truth_multihots(label_sets, label_vocab): """Get a multihot matrix from label sets and a vocab.""" vocab_indices = {v: i for i, v in enumerate(label_vocab)} ground_truths = [] for s in label_sets: indices = np.array([vocab_indices[v] for v in s], dtype=np.int32) multihots = np.zeros([len(label_vocab)]) multihots[indices] = 1 ground_truths.append(multihots) return np.vstack(ground_truths) def get_pr_f1_df_from_arrays( ground_truths, normalized_predictions, prediction_precision_limit = None, ): """Convenience method for making a PR curve dataframe. Args: ground_truths: multihot array of shape (num_examples, num_labels). normalized_predictions: array of shape (num_samples, num_labels). prediction_precision_limit: Used to truncate the predictions to a fixed level of precision. Predictions are truncated to `prediction_precision_limit` number of decimal places. This argument is useful to increase the speed of computation, and also to decrease the size of the dataframe returned. If None, no truncation is performed. Returns: pd.DataFrame with columns precision (float); recall (float); threshold (float); f1 (float). """ if prediction_precision_limit: normalized_predictions = np.around(normalized_predictions, prediction_precision_limit) precisions, recalls, thresholds = sklearn.metrics.precision_recall_curve( ground_truths.flatten(), normalized_predictions.flatten()) # Throw away last precision and recall as they are always 0 and 1 # respectively, and have no associated threshold precisions = precisions[:-1] recalls = recalls[:-1] f1s = 2 * (precisions * recalls) / (precisions + recalls) return pd.DataFrame( data={ 'precision': precisions, 'recall': recalls, 'threshold': thresholds, 'f1': f1s }) def get_pr_f1_df( prediction_df, label_vocab, label_normalizer, eval_vocab = None, prediction_precision_limit = 18, ): """Make a dataframe with each possible threshold and it's corresponding values. Args: prediction_df: A dataframe with columns `predictions` and `true_label`. label_vocab: A list of labels. label_normalizer: A mapping from labels to their children. eval_vocab: An optional subset of `label_vocab` on which to restrict analysis. prediction_precision_limit: Used to truncate the predictions to a fixed level of precision. Predictions are truncated to `prediction_precision_limit` number of decimal places. This argument is useful to increase the speed of computation, and also to decrease the size of the dataframe returned. If None, no truncation is performed. Returns: A dataframe with 4 columns; precision, recall, f1, and threshold. At each threshold precision, recall, and f1 are calculated relative to the normalized confidences and true labels given in `prediction_df`. """ if not eval_vocab: eval_vocab = set(label_vocab) label_vocab = np.array(label_vocab) prediction_array = np.vstack(prediction_df.predictions) normalized_predictions = normalize_confidences(prediction_array, label_vocab, label_normalizer) true_label_sets = prediction_df.true_label.apply( eval_vocab.intersection).values eval_indices = np.array( [i for i, v in enumerate(label_vocab) if v in eval_vocab]) ground_truths = get_ground_truth_multihots(true_label_sets, label_vocab[eval_indices]) return get_pr_f1_df_from_arrays(ground_truths, normalized_predictions[:, eval_indices], prediction_precision_limit) def true_false_positive_negative_df(df): """Computes df of all example/label pairs, and whether they were correct. Args: df: pd.Dataframe that has columns: true_label. Contains a set of true labels. predicted_label. Contains a set of true labels. sequence_name. string. Accession. Returns: pd.DataFrame that has columns: sequence_name. string. Name of sequence (accession). class. string. Class name. Either predicted or true. predicted. np.bool. Whether the class was predicted for the sequence. true. np.bool. Whether the class label is true for the sequence. true_positive. Whether the prediction is a true positive. false_positive. Whether the prediction is a false positive. false_negative. Whether the prediction is a false negative. """ dict_prep_for_df = { 'sequence_name': [], 'class': [], 'predicted': [], 'true': [] } for _, row in tqdm.tqdm(df.iterrows(), position=0, total=len(df)): all_classes = row.predicted_label.union(row.true_label) for cls in all_classes: dict_prep_for_df['sequence_name'].append(row.sequence_name) dict_prep_for_df['class'].append(cls) dict_prep_for_df['predicted'].append(cls in row.predicted_label) dict_prep_for_df['true'].append(cls in row.true_label) working_df = pd.DataFrame(dict_prep_for_df) working_df.predicted = working_df.predicted.astype(np.bool) working_df.true = working_df.true.astype(np.bool) working_df['true_positive'] = working_df.predicted & working_df.true working_df['false_positive'] = working_df.predicted & ~working_df.true working_df['false_negative'] = ~working_df.predicted & working_df.true return working_df def multilabel_precision_per_example_label_pair( df): """Computes precision score of predictions in dataframe. Each (example, prediction) pair counts as "one" toward the precision. This is different than counting each class equally, or counting each example evenly. Args: df: E.g. output of true_false_positive_negative_df that has columns true_positive. bool. false_positive. bool. Returns: precision. Does not consider any thresholds. """ true_positive = df.true_positive.sum().astype(float) false_positive = df.false_positive.sum().astype(float) return true_positive / (true_positive + false_positive) def multilabel_recall_per_example_label_pair( df): """Computes f1 score of predictions in dataframe. Each (example, prediction) pair counts as "one" toward the recall. This is different than counting each class equally, or counting each example evenly. Args: df: E.g. output of true_false_positive_negative_df that has columns true_positive. bool. false_negative. bool. Returns: recall. Does not consider any thresholds. """ true_positive = df.true_positive.sum().astype(float) false_negative = df.false_negative.sum().astype(float) return true_positive / (true_positive + false_negative) def multilabel_f1_per_example_label_pair( df): """Computes f1 score of predictions in dataframe. Each (example, prediction) pair counts as "one" toward the f1 score. This is different than counting each class equally, or counting each example evenly. Args: df: has columns: true_positive. bool. false_positive. bool. false_negative. bool. Returns: f1 score. Harmonic mean of precision and recall. Does not consider any thresholds. """ precision = multilabel_precision_per_example_label_pair(df) recall = multilabel_recall_per_example_label_pair(df) return 2. * (precision * recall) / (precision + recall) def normalize_predictions( to_normalize, normalize_map): normalized_non_flattened = [normalize_map[e] for e in to_normalize] return frozenset(itertools.chain(*normalized_non_flattened)) # Flatten list. def precision_recall_f1( df, label_normalizing_dict): """Returns precision, recall, and f1 for a dataframe. Args: df: pd.DataFrame with columns sequence_name, true__label, and predicted_label. label_normalizing_dict: dictionary of label to implied labels. Used to normalize labels into canonical form (e.g. an obsolete label is normalized into its replacement). See parenthood_lib.get_applicable_label_dict. Returns: PrecisionRecallF1. """ blast_prepped = pd.DataFrame() blast_prepped['sequence_name'] = df.sequence_name blast_prepped['predicted_label'] = df.predicted_label.apply( lambda x: normalize_predictions(x, label_normalizing_dict)) blast_prepped['true_label'] = df.true_label denormalized = true_false_positive_negative_df(blast_prepped) precision = multilabel_precision_per_example_label_pair(denormalized) recall = multilabel_recall_per_example_label_pair(denormalized) f1 = multilabel_f1_per_example_label_pair(denormalized) return precision, recall, f1 def filter_predictions_to_above_threshold( predictions, decision_threshold, label_vocab): """Computes predictions above `decision_threshold` for each example. Args: predictions: np.array, (2-d) of float. Outer dimension is example, inner dimension is class label. I.e. predictions[2, 3] is the probability that for example 2, the third class is true. decision_threshold: float. Classes with predictions above this threshold will be included in the output. label_vocab: np.array (1-d) of string. List of classes that corresponds to prediction. Returns: List of FrozenSet. Outer dimension is the example number within this batch, inner is a set of labels predicted that have confidence over decision_threshold. """ preds_per_sequence = [] for row in predictions: preds_per_sequence.append( frozenset(label_vocab[np.array(row) > decision_threshold])) return preds_per_sequence def get_predictions_above_threshold( input_df, inferrer, decision_threshold, label_vocab = None): """Return df of predictions above a threshold for each sequence. Args: input_df: pd.DataFrame with columns sequence_name, sequence. inferrer: inferrer from a savedmodel model (see protein_task.MultiDiscreteLabelProteinTask) with activation_type 'serving_default'. decision_threshold: float. Classes with predictions above this threshold will be included in the output. label_vocab: A numpy array with the string labels in vocab order. If None, will try to fetch this tensor from `inferrer`. Returns: pd.DataFrame with columns sequence_name, sequence, and predicted_label, where the values in column predicted_label are frozenset of labels that had confidences above decision_threshold. """ if label_vocab is None: label_vocab = inferrer.get_variable('label_vocab:0') working_df = input_df.copy() df_with_predictions = inference.predictions_for_df(working_df, inferrer) preds_per_sequence = filter_predictions_to_above_threshold( predictions=df_with_predictions['predictions'].values, decision_threshold=decision_threshold, label_vocab=label_vocab) df_with_predictions.drop(columns=['predictions'], inplace=True) df_with_predictions['predicted_label'] = preds_per_sequence return df_with_predictions def _family_and_clan_to_just_clan( family_and_clan): """Converts family_and_clan to just a clan if there is one. Args: family_and_clan: a set of either just a family, or a family and its associated clan. Returns: If family_and_clan is only a family, return family_and_clan. If family_and_clan has a clan, only return the clan. Raises: ValueError if len(family_and_clan != 1 or 2. Also raises if len(family-and_clan) == 2 and there's no clan in it. """ if len(family_and_clan) == 1: return frozenset(family_and_clan) if len(family_and_clan) == 2: for f_or_c in family_and_clan: if f_or_c.startswith('Pfam:CL'): return frozenset([f_or_c]) raise ValueError('family_and_clan was length 2, but did not have a clan in ' 'it. family_and_clan was {}'.format(family_and_clan)) raise ValueError('Expected either one or two values for family_and_clan. ' 'Was {}'.format(family_and_clan)) def pfam_label_normalizer_to_lifted_clan( label_normalizer): """Converts label_normalizer (that may contan EC etc.) to lifted clans.""" working_dict = copy.deepcopy(label_normalizer) working_dict = {k: v for k, v in working_dict.items() if k.startswith('Pfam')} return {k: _family_and_clan_to_just_clan(v) for k, v in working_dict.items()} def convert_pfam_ground_truth_to_lifted_clans( ground_truth, label_normalizer): """Converts ground truth to only have labels that are lifted clans. The label normalizer may be already a lifted clan normalizer or it may be a non-lifted clan normalizer. Args: ground_truth: pd.DataFrame with columns sequence_name (str), true_label(Set[str]). label_normalizer: label normalizer. This will be converted to a lifted clan normalizer for use internally. Returns: pd.DataFrame with columns sequence_name (str), true_label(FrozenSet[str]). """ lifted_label_normalizer = pfam_label_normalizer_to_lifted_clan( label_normalizer) working_df = ground_truth.copy() working_df['true_label'] = working_df.true_label.apply( lambda l: normalize_predictions(l, lifted_label_normalizer)) return working_df def _ec_label_at_level(label, level): """Return EC label up to and including level, or nan if it's a hyphen.""" # nan is a useful value for pd.DataFrame that's used to indicate missing # data. label = label.replace('EC:', '') split = label.split('.') if split[level - 1] == '-': return np.nan return '.'.join(split[:level]) def ec_agreement_for_level(df, level): """Returns agreement and disagreement between predictions and truth. Computes agreement, disagreement, and no-calls between true labels and predicted labels at level 1, 2, 3, or 4 in the EC hierarchy. If the ground truth label has a dash at this level, this is not included in our analysis of agreement/disagreement, as there's nothing to agree or disagree about. See the test for a more exhaustive listing of cases. Args: df: pd.DataFrame with columns 'true_label' (str) and 'predicted_label' (str). It's assumed that the input proteins are single-function enzymes, and that true and predicted label are the most specific predictions available (e.g. EC:1.2.3.4 instead of EC:1.2.3.-). level: int between 1 and 4. Returns: tuple of ints: [agreement, disagreement, no call made]. Raises: ValueError if level is not between 1 and 4 inclusive. """ if not (level >= 1 and level <= 4): raise ValueError( 'Expected level to be between 1 and 4. Was {}'.format(level)) get_ec_at_level = lambda x: _ec_label_at_level(x, level) true_labels = df.true_label.apply(get_ec_at_level) predicted_labels = df.predicted_label.apply(get_ec_at_level) has_ground_truth_label = true_labels.notna() pred_made = has_ground_truth_label & (predicted_labels.notna()) agree = has_ground_truth_label & pred_made & (true_labels == predicted_labels) disagree = has_ground_truth_label & pred_made & ( true_labels != predicted_labels) agree_numerator = sum(agree) disagree_numerator = sum(disagree) no_pred_made_numerator = sum(~pred_made & has_ground_truth_label) if (agree_numerator + disagree_numerator + no_pred_made_numerator != sum(has_ground_truth_label)): # Internal correctness check, so raise an AssertionError, not a ValueError. error_msg = (f'Expected the sum agree_numerator + disagree_numerator + ' f'no_pred_made_numerator == sum(has_ground_truth_label): were ' f'{agree_numerator}, {disagree_numerator}, ' f'{no_pred_made_numerator} {sum(has_ground_truth_label)}') raise AssertionError(error_msg) return agree_numerator, disagree_numerator, no_pred_made_numerator ================================================ FILE: evaluation_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python2, python3 """Tests for evaluation.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import absltest from absl.testing import parameterized import numpy as np import pandas as pd import evaluation from six.moves import range class _InferrerFixture(object): activation_type = "serving_default" def __init__(self, vocab_size): self._vocab_size = vocab_size def get_activations(self, l): # Return the first l rows of the identity matrix. return np.eye(self._vocab_size)[:len(l)] def get_variable(self, _): return np.array(["CLASS_{}".format(i) for i in range(self._vocab_size)]) class TestEvaluation(parameterized.TestCase): @parameterized.named_parameters( dict( testcase_name="one_label", confidences=np.array([[1.0], [1.0]]), vocab=["a"], normalized=np.array([[1.0], [1.0]]), parents={"a": ["a"]}), dict( testcase_name="missing_implied_label", confidences=np.array([[1.0], [1.0]]), vocab=["a"], normalized=np.array([[1.0], [1.0]]), parents={"a": ["a", "b"]}), dict( testcase_name="one_example", confidences=np.array([[0.6, 0.4]]), vocab=["a", "b"], normalized=np.array([[0.6, 0.6]]), parents={ "a": ["a", "b"], "b": ["b"] })) def test_normalize_confidences_edge(self, confidences, vocab, normalized, parents): test_normalized = evaluation.normalize_confidences(confidences, vocab, parents) np.testing.assert_array_equal(test_normalized, normalized) def test_normalize_confidences(self): test_confidences = np.array([[0.2, 0.5, 0.1, 0.2], [0.1, 0.5, 0.2, 0.1], [0.1, 0.1, 0.6, 0.2]]) test_vocab = ["a", "b", "c", "d"] test_parents = {"b": ["a", "b", "c"], "d": ["d"], "a": ["a"], "c": ["c"]} normalized = evaluation.normalize_confidences(test_confidences, test_vocab, test_parents) for child, parents in test_parents.items(): children = normalized[:, test_vocab.index(child)] for parent in parents: non_violations = (children <= normalized[:, test_vocab.index(parent)]) self.assertTrue( np.all(non_violations), msg=( "Parent: {} violates parenthood invariant at indices:\n " # pylint: disable=g-explicit-bool-comparison, singleton-comparison "{}".format(parent, np.where(non_violations == False)))) def test_get_ground_truth_multihots(self): label_sets = [{"foo"}, {"foo", "bar"}, {"bar", "baz"}, set()] label_vocab = sorted(["foo", "bar", "baz"]) expected = np.array([[0, 0, 1], [1, 0, 1], [1, 1, 0], [0, 0, 0]]) multihots = evaluation.get_ground_truth_multihots(label_sets, label_vocab) np.testing.assert_array_equal(expected, multihots) @parameterized.named_parameters( dict( testcase_name="no_subset", # After normalization predictions are: # [0.4, 0.3, 0.1], [0.4, 0.4, 0.3] # Threshold of .3 gives 4 correct out of 5 expected_precision=np.array([4. / 5, 1.0]), # Threshold of .4 gives 3 found out of 4. expected_recall=np.array([1.0, 3. / 4])), dict( testcase_name="subset", eval_subset={"a", "c"}, # Without label "b" all correct expected_precision=np.array([1.0, 1.0]), # Threshold of 0.4 gives 2 out of 3 positive labels. expected_recall=np.array([1.0, 2.0 / 3]))) def test_get_pr_f1_df(self, expected_precision, expected_recall, eval_subset=None): df = pd.DataFrame( dict( true_label=[{"a"}, {"a", "b", "c"}], predictions=[[0.4, 0.3, 0.1], [0.1, 0.4, 0.3]])) test_vocab = ["a", "b", "c"] test_parents = {"b": ["a", "b"], "a": ["a"], "c": ["c"]} # After normalization predictions are: # [0.4, 0.3, 0.1], [0.4, 0.4, 0.3] expected_thresholds = np.array([0.3, 0.4]) expected_f1 = ((2 * expected_recall * expected_precision) / (expected_recall + expected_precision)) pr_f1_df = evaluation.get_pr_f1_df( df, test_vocab, test_parents, eval_vocab=eval_subset) np.testing.assert_array_equal(pr_f1_df.precision.values, expected_precision) np.testing.assert_array_equal(pr_f1_df.recall.values, expected_recall) np.testing.assert_array_equal(pr_f1_df.threshold.values, expected_thresholds) np.testing.assert_array_equal(pr_f1_df.f1.values, expected_f1) def test_get_pr_f1_df_precision_truncation(self): df = pd.DataFrame( dict( true_label=[{"a", "b", "c"}], predictions=[np.array([0.0001, 0.0002, 0.0003])])) test_vocab = ["a", "b", "c"] test_parents = {"a": ["a"], "b": ["b"], "c": ["c"]} # We expect that when truncating precision, the precision recall df is # shorter. pr_f1_df_unlimited_precision = evaluation.get_pr_f1_df( df, test_vocab, test_parents, prediction_precision_limit=None) pr_f1_df_limited_precision = evaluation.get_pr_f1_df( df, test_vocab, test_parents, prediction_precision_limit=1) self.assertLess( len(pr_f1_df_limited_precision), len(pr_f1_df_unlimited_precision), "{}\nis not shorter than\n{}".format( pr_f1_df_limited_precision, pr_f1_df_unlimited_precision, )) @parameterized.named_parameters( dict( testcase_name="one class, perfect", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_1"}], true_label=[{"CLASS_1"}])), expected=1., ), dict( testcase_name="one class, imperfect", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_1"}], true_label=[{"CLASS_0"}])), expected=0., ), dict( testcase_name="two classes, perfect", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_0", "CLASS_1"}], true_label=[{"CLASS_0", "CLASS_1"}])), expected=1.), dict( testcase_name="two classes, too many predictions, all recalled", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_0", "CLASS_1"}], true_label=[{"CLASS_0"}])), expected=1., ), dict( testcase_name="two classes, not enough predictions", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_0"}], true_label=[{"CLASS_0", "CLASS_1"}])), expected=.5, ), dict( testcase_name="two examples, perfect", df=pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0"}, {"CLASS_1"}], true_label=[{"CLASS_0"}, {"CLASS_1"}])), expected=1., ), dict( testcase_name="two examples, imperfect", df=pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0"}, {"CLASS_0"}], true_label=[{"CLASS_0"}, {"CLASS_1"}])), expected=.5, ), dict( testcase_name="two examples, one with two labels, one not recalled", df=pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0"}, {"CLASS_1"}], true_label=[{"CLASS_0", "CLASS_1"}, {"CLASS_1"}])), expected=2. / 3, ), ) def test_multilabel_recall_per_example_label_pair(self, df, expected): working_df = evaluation.true_false_positive_negative_df(df) actual = evaluation.multilabel_recall_per_example_label_pair(working_df) self.assertEqual(actual, expected) @parameterized.named_parameters( dict( testcase_name="one class, perfect", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_1"}], true_label=[{"CLASS_1"}])), expected=1., ), dict( testcase_name="one class, imperfect", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_1"}], true_label=[{"CLASS_0"}])), expected=0., ), dict( testcase_name="two classes, perfect", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_0", "CLASS_1"}], true_label=[{"CLASS_0", "CLASS_1"}])), expected=1., ), dict( testcase_name="two classes, too many predictions", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_0", "CLASS_1"}], true_label=[{"CLASS_0"}])), expected=.5, ), dict( testcase_name="two classes, not enough predictions", df=pd.DataFrame( dict( sequence_name=["seq1"], predicted_label=[{"CLASS_0"}], true_label=[{"CLASS_0", "CLASS_1"}])), expected=1., ), dict( testcase_name="two examples, perfect", df=pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0"}, {"CLASS_1"}], true_label=[{"CLASS_0"}, {"CLASS_1"}])), expected=1., ), dict( testcase_name="two examples, imperfect", df=pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0"}, {"CLASS_0"}], true_label=[{"CLASS_0"}, {"CLASS_1"}])), expected=.5, ), dict( testcase_name="two examples, one with two predicted labels, one wrong", df=pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0", "CLASS_1"}, {"CLASS_1"}], true_label=[{"CLASS_0"}, {"CLASS_1"}])), expected=2. / 3, ), ) def test_multilabel_precision_per_example_label_pair(self, df, expected): working_df = evaluation.true_false_positive_negative_df(df) actual = evaluation.multilabel_precision_per_example_label_pair(working_df) self.assertEqual(actual, expected) def test_multilabel_f1_per_example_label_pair(self): df = pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0", "CLASS_1"}, {"CLASS_1"}], true_label=[{"CLASS_0"}, {"CLASS_1"}])) df = evaluation.true_false_positive_negative_df(df) expected = .8 # Harmonic mean of 1. and 2./3 actual = evaluation.multilabel_f1_per_example_label_pair(df) self.assertEqual(expected, actual) def test_precision_recall_f1_integration_test(self): df = pd.DataFrame( dict( sequence_name=["seq1", "seq2"], predicted_label=[{"CLASS_0", "CLASS_1"}, {"CLASS_1"}], true_label=[{"CLASS_1"}, {"CLASS_1"}])) normalizing_dict = { "CLASS_0": list(), # Not included in output. "CLASS_1": ["CLASS_1"] } actual_precision, actual_recall, actual_f1 = evaluation.precision_recall_f1( df, normalizing_dict) expected_precision = 1. # CLASS_0 is normalized out. expected_recall = 1. # Both sequences have their true label recalled. expected_f1 = 1. self.assertEqual(actual_precision, expected_precision) self.assertEqual(actual_recall, expected_recall) self.assertEqual(actual_f1, expected_f1) @parameterized.named_parameters( dict( testcase_name="no sequences", input_predictions=np.array([]), decision_threshold=1., label_vocab=np.array([]), expected=[], ), dict( testcase_name="two sequences, no predictions make it above threshold", input_predictions=np.array([[.5, .5], [.5, .5]]), decision_threshold=1., label_vocab=np.array(["class1", "class2"]), expected=[frozenset(), frozenset()], ), dict( testcase_name="two sequences, one prediction makes it above threshold", input_predictions=np.array([[1., .5], [.5, .5]]), decision_threshold=.75, label_vocab=np.array(["class1", "class2"]), expected=[frozenset(["class1"]), frozenset()], ), dict( testcase_name="two sequences, one prediction from each makes it above threshold", input_predictions=np.array([[1., .5], [.5, 1.]]), decision_threshold=.75, label_vocab=np.array(["class1", "class2"]), expected=[frozenset(["class1"]), frozenset(["class2"])], ), dict( testcase_name="two sequences, all classes are predicted", input_predictions=np.array([[1., 1.], [1., 1.]]), decision_threshold=.75, label_vocab=np.array(["class1", "class2"]), expected=[ frozenset(["class1", "class2"]), frozenset(["class1", "class2"]) ], ), ) def test_filter_predictions_to_above_threshold(self, input_predictions, decision_threshold, label_vocab, expected): actual = evaluation.filter_predictions_to_above_threshold( predictions=input_predictions, decision_threshold=decision_threshold, label_vocab=label_vocab) self.assertEqual(actual, expected) @parameterized.named_parameters( dict( testcase_name="medium threshold, all make the cut", input_seqs=["AAAA", "DDD", "EE", "W"], decision_threshold=.5, vocab_size=10, # Inferrer fixture outputs values of 1, so everything is above .5. expected_count_over_threshold=4, ), dict( testcase_name="high threshold, all make the cut", input_seqs=["AAAA", "DDD", "EE", "W"], decision_threshold=999, vocab_size=10, # Inferrer fixture outputs values of 1, so everything is below 999. expected_count_over_threshold=0, ), ) def test_get_predictions_above_threshold(self, input_seqs, decision_threshold, vocab_size, expected_count_over_threshold): input_df = pd.DataFrame({ "sequence_name": input_seqs, "sequence": input_seqs }) inferrer_fixture = _InferrerFixture(vocab_size) actual = evaluation.get_predictions_above_threshold( input_df=input_df, inferrer=inferrer_fixture, decision_threshold=decision_threshold) # Assert columns are correct. expected_columns = ["sequence_name", "sequence", "predicted_label"] self.assertCountEqual(actual.columns, expected_columns) # Assert predicted_label values are all type frozenset. for predicted_label in actual.predicted_label.values: self.assertEqual(type(predicted_label), frozenset) # Assert number of called labels is correct. actual_count_over_threshold = len( [s for s in actual.predicted_label if len(s) > 0]) # pylint: disable=g-explicit-length-test self.assertEqual(actual_count_over_threshold, expected_count_over_threshold) @parameterized.named_parameters( dict( testcase_name="empty dict", input_normalizer={}, expected={}, ), dict( testcase_name="one entry", input_normalizer={"Pfam:PF00001": frozenset(["Pfam:PF00001"])}, expected={"Pfam:PF00001": frozenset(["Pfam:PF00001"])}, ), dict( testcase_name="one entry with clan", input_normalizer={ "Pfam:PF00001": frozenset(["Pfam:PF00001", "Pfam:CL0192"]) }, expected={"Pfam:PF00001": frozenset(["Pfam:CL0192"])}, ), ) def test_pfam_label_normalizer_to_lifted_clan(self, input_normalizer, expected): actual = evaluation.pfam_label_normalizer_to_lifted_clan(input_normalizer) self.assertDictEqual(actual, expected) # Assert that pfam_label_normalizer_to_lifted_clan is idempotent by calling # the function again. second_actual = evaluation.pfam_label_normalizer_to_lifted_clan( input_normalizer) self.assertDictEqual(second_actual, actual) def test_pfam_label_normalizer_to_lifted_clan_raises_when_wrong_num(self): with self.assertRaisesRegex(ValueError, "one or two"): # This label implies 3 labels, which is not allowed. evaluation.pfam_label_normalizer_to_lifted_clan( {"Pfam:PF00001": frozenset(["Pfam:ONE", "Pfam:TWO", "Pfam:THREE"])}) def test_pfam_label_normalizer_to_lifted_clan_raises_when_no_clan(self): with self.assertRaisesRegex(ValueError, "did not have a clan"): # This label implies 2 labels, none of which is a clan evaluation.pfam_label_normalizer_to_lifted_clan({ "Pfam:PF00001": frozenset(["Pfam:NOT_A_CLAN_1", "PFam:NOT_A_CLAN_2"]) }) def test_convert_pfam_ground_truth_to_lifted_clans(self): input_df = pd.DataFrame({ "sequence_name": ["SEQ1", "SEQ2"], "true_label": [ frozenset(["Pfam:PF00001"]), frozenset(["Pfam:PF99999", "Pfam:PF88888"]) ] }) input_label_normalizer = { "Pfam:PF00001": frozenset(["Pfam:PF00001", "Pfam:CL0192"]), "Pfam:PF99999": frozenset(["Pfam:PF99999"]), "Pfam:PF88888": frozenset(["Pfam:PF88888"]) } actual = evaluation.convert_pfam_ground_truth_to_lifted_clans( input_df, input_label_normalizer) # Expect PF00001 was converted to only CL0192, everything else stays the # same. expected = pd.DataFrame({ "sequence_name": ["SEQ1", "SEQ2"], "true_label": [ frozenset(["Pfam:CL0192"]), frozenset(["Pfam:PF99999", "Pfam:PF88888"]) ] }) np.testing.assert_array_equal(actual.sequence_name.values, expected.sequence_name.values) self.assertSetEqual(actual.true_label.values[0], expected.true_label.values[0]) self.assertSetEqual(actual.true_label.values[1], expected.true_label.values[1]) @parameterized.named_parameters( dict( testcase_name="agree at this level", input_df=pd.DataFrame({ "predicted_label": ["EC:1.1.1.1"], "true_label": ["EC:1.1.1.2"], }), input_level=1, expected_agree=1, expected_disagree=0, expected_no_call=0, ), dict( testcase_name="disagree at this level", input_df=pd.DataFrame({ "predicted_label": ["EC:1.1.1.1"], "true_label": ["EC:1.1.1.2"], }), input_level=4, expected_agree=0, expected_disagree=1, expected_no_call=0, ), dict( testcase_name="ground truth had no prediction at this level, but we did", input_df=pd.DataFrame({ "predicted_label": ["EC:1.1.1.1"], "true_label": ["EC:1.1.1.-"], }), input_level=4, expected_agree=0, expected_disagree=0, expected_no_call=0, ), dict( testcase_name="ground truth had no prediction at this level, and neither did we", input_df=pd.DataFrame({ "predicted_label": ["EC:1.1.1.-"], "true_label": ["EC:1.1.1.-"], }), input_level=4, expected_agree=0, expected_disagree=0, expected_no_call=0, ), dict( testcase_name="ground truth had no prediction at this level, we did make a prediction, but we made a mistake earlier", input_df=pd.DataFrame({ "predicted_label": ["EC:2.2.2.2"], "true_label": ["EC:1.1.1.-"], }), input_level=4, expected_agree=0, # We shouldn't be penalized for making a prediction at a level # where the ground truth also didn't make a prediction. expected_disagree=0, expected_no_call=0, ), dict( testcase_name="ground truth had prediction at this level, we did not make a prediction, and we made no mistake earlier", input_df=pd.DataFrame({ "predicted_label": ["EC:1.1.1.-"], "true_label": ["EC:1.1.1.1"], }), input_level=4, expected_agree=0, # We shouldn't be penalized for making no prediction, even if we # made a mistake higher up. expected_disagree=0, expected_no_call=1, ), dict( testcase_name="ground truth had prediction at this level, we did not make a prediction, but we made a mistake earlier", input_df=pd.DataFrame({ "predicted_label": ["EC:2.2.2.-"], "true_label": ["EC:1.1.1.1"], }), input_level=4, expected_agree=0, # We shouldn't be penalized for making no prediction, even if we # made a mistake higher up. expected_disagree=0, expected_no_call=1, ), dict( testcase_name="ground truth had no prediction at this level, and we didn't make a prediction at this level, but we made a mistake earlier", input_df=pd.DataFrame({ "predicted_label": ["EC:2.2.2.-"], "true_label": ["EC:1.1.1.-"], }), input_level=4, expected_agree=0, # We shouldn't be penalized for making a non-prediction at a level # where the ground truth also didn't make a prediction. expected_disagree=0, expected_no_call=0, ), dict( testcase_name="we made a mistake earlier on, but happened to agree on a lesser classification", input_df=pd.DataFrame({ "predicted_label": ["EC:2.1.1.1"], "true_label": ["EC:1.1.1.1"], }), input_level=3, expected_agree=0, # Even though we agree that the 3rd level is a '1', because we made # a mistake in the first level, this should be counted as a # disagreement. expected_disagree=1, expected_no_call=0, ), dict( testcase_name="more than one prediction", input_df=pd.DataFrame({ "predicted_label": [ "EC:1.1.1.1", "EC:1.1.1.1", "EC:1.1.1.1", "EC:1.1.1.-" ], "true_label": [ "EC:1.1.1.1", "EC:1.1.1.2", "EC:1.1.1.-", "EC:1.1.1.-" ], }), input_level=4, # First element is an agreement. expected_agree=1, # Second element is a disagreement. expected_disagree=1, # Last two elements are discarded because they're not called by the # ground truth. expected_no_call=0, ), ) def test_ec_agreement_for_level(self, input_level, input_df, expected_agree, expected_disagree, expected_no_call): actual_agree, actual_disagree, actual_no_call = evaluation.ec_agreement_for_level( input_df, input_level) self.assertEqual(actual_agree, expected_agree, "agreement numbers were not the same.") self.assertEqual(actual_disagree, expected_disagree, "disagreement numbers were not the same.") self.assertEqual(actual_no_call, expected_no_call, "no-call numbers were not the same.") if __name__ == "__main__": absltest.main() ================================================ FILE: hparams_sets.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. r"""Hyperparameter sets. These are defined as functions to allow for inheritance. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib import training as contrib_training def _starting_hparams(): """Set of shared starting parameters used in sets below.""" hparams = contrib_training.HParams() hparams.add_hparam('batch_style', 'bucket') hparams.add_hparam('gradient_clipping_decay', 0.9999) hparams.add_hparam('learning_rate', 0.0005) hparams.add_hparam('lr_decay_rate', .997) hparams.add_hparam('lr_decay_steps', 1000) hparams.add_hparam('lr_warmup_steps', 3000) hparams.add_hparam('model_type', 'cnn') hparams.add_hparam('resnet_bottleneck_factor', 0.5) hparams.add_hparam('decision_threshold', 0.5) hparams.add_hparam('denominator_power', 1.0) # Standard mean-pooling. return hparams def tuned_for_ec(): """Hyperparameters tuned for EC classification.""" # TODO(theosanderson): update these to true SOTA values hparams = contrib_training.HParams() hparams.add_hparam('gradient_clipping_decay', 0.9999) hparams.add_hparam('batch_style', 'bucket') hparams.add_hparam('batch_size', 34) hparams.add_hparam('dilation_rate', 5) hparams.add_hparam('filters', 411) hparams.add_hparam('first_dilated_layer', 1) # This is 0-indexed hparams.add_hparam('kernel_size', 7) hparams.add_hparam('num_layers', 5) hparams.add_hparam('pooling', 'mean') hparams.add_hparam('resnet_bottleneck_factor', 0.88152) hparams.add_hparam('lr_decay_rate', 0.9977) hparams.add_hparam('learning_rate', 0.00028748) hparams.add_hparam('decision_threshold', 0.3746) hparams.add_hparam('denominator_power', 0.88) hparams.add_hparam('train_steps', 650000) return hparams def small_test_model(): """A small test model that will run on a CPU quickly.""" hparams = _starting_hparams() hparams.add_hparam('batch_size', 8) hparams.add_hparam('dilation_rate', 1) hparams.add_hparam('first_dilated_layer', 1) # This is 0-indexed hparams.add_hparam('filters', 10) hparams.add_hparam('kernel_size', 3) hparams.add_hparam('num_layers', 1) hparams.add_hparam('train_steps', 100) return hparams ================================================ FILE: inference.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Compute activations for trained model from input sequences.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import base64 import functools import gzip import io import itertools import os from typing import Dict, FrozenSet, Iterator, List, Text, Tuple from absl import logging import numpy as np import pandas as pd import utils import six import tensorflow.compat.v1 as tf import tensorflow_hub as hub import tqdm import scipy.sparse def call_module(module, one_hots, row_lengths, signature): """Call a tf_hub.Module using the standard blundell signature. This expects that `module` has a signature named `signature` which conforms to ('sequence', 'sequence_length') -> output To use an existing SavedModel file you may want to create a module_spec with `tensorflow_hub.saved_model_module.create_module_spec_from_saved_model`. Args: module: a tf_hub.Module to call. one_hots: a rank 3 tensor with one-hot encoded sequences of residues. row_lengths: a rank 1 tensor with sequence lengths. signature: the graph signature to validate and call. Returns: The output tensor of `module`. """ if signature not in module.get_signature_names(): raise ValueError('signature not in ' + six.ensure_str(str(module.get_signature_names())) + '. Was ' + six.ensure_str(signature) + '.') inputs = module.get_input_info_dict(signature=signature) expected_inputs = [ 'sequence', 'sequence_length', ] if set(inputs.keys()) != set(expected_inputs): raise ValueError( 'The signature_def does not have the expected inputs. Please ' 'reconfigure your saved model to only export signatures ' 'with sequence and length inputs. (Inputs were %s, expected %s)' % (str(inputs), str(expected_inputs))) outputs = module.get_output_info_dict(signature=signature) if len(outputs) > 1: raise ValueError('The signature_def given has more than one output. Please ' 'reconfigure your saved model to only export signatures ' 'with one output. (Outputs were %s)' % str(outputs)) return list( module({ 'sequence': one_hots, 'sequence_length': row_lengths, }, signature=signature, as_dict=True).values())[0] def in_graph_inferrer(sequences, savedmodel_dir_path, signature, name_scope='inferrer'): """Add an in-graph inferrer to the active default graph. Additionally performs in-graph preprocessing, splitting strings, and encoding residues. Args: sequences: A tf.string Tensor representing a batch of sequences with shape [None]. savedmodel_dir_path: Path to the directory with the SavedModel binary. signature: Name of the signature to use in `savedmodel_dir_path`. e.g. 'pooled_representation' name_scope: Name scope to use for the loaded saved model. Returns: Output Tensor Raises: ValueError if signature does not conform to ('sequence', 'sequence_length') -> output or if the specified signature is not present. """ # Add variable to make it easier to refactor with multiple tags in future. tags = [tf.saved_model.tag_constants.SERVING] # Tokenization residues = tf.strings.unicode_split(sequences, 'UTF-8') # Convert to one-hots and pad. one_hots, row_lengths = utils.in_graph_residues_to_onehot(residues) module_spec = hub.saved_model_module.create_module_spec_from_saved_model( savedmodel_dir_path) module = hub.Module(module_spec, trainable=False, tags=tags, name=name_scope) return call_module(module, one_hots, row_lengths, signature) @functools.lru_cache(maxsize=None) def memoized_inferrer( savedmodel_dir_path, activation_type=tf.saved_model.signature_constants .DEFAULT_SERVING_SIGNATURE_DEF_KEY, batch_size=16, use_tqdm=False, session_config=None, memoize_inference_results=False, use_latest_savedmodel=False, ): """Alternative constructor for Inferrer that is memoized.""" return Inferrer( savedmodel_dir_path=savedmodel_dir_path, activation_type=activation_type, batch_size=batch_size, use_tqdm=use_tqdm, session_config=session_config, memoize_inference_results=memoize_inference_results, use_latest_savedmodel=use_latest_savedmodel, ) class Inferrer(object): """Uses a SavedModel to provide batched inference.""" def __init__( self, savedmodel_dir_path, activation_type=tf.saved_model.signature_constants .DEFAULT_SERVING_SIGNATURE_DEF_KEY, batch_size=64, use_tqdm=False, session_config=None, memoize_inference_results=False, use_latest_savedmodel=False, ): """Construct Inferrer. Args: savedmodel_dir_path: path to directory where a SavedModel pb or pbtxt is stored. The SavedModel must only have one input per signature and only one output per signature. activation_type: one of the keys in saved_model.signature_def.keys(). batch_size: batch size to use for individual inference ops. use_tqdm: Whether to print progress using tqdm. session_config: tf.ConfigProto for tf.Session creation. memoize_inference_results: if True, calls to inference.get_activations will be memoized. use_latest_savedmodel: If True, the model will be loaded from latest_savedmodel_path_from_base_path(savedmodel_dir_path). Raises: ValueError: if activation_type is not the name of a signature_def in the SavedModel. ValueError: if SavedModel.signature_def[activation_type] has an input other than 'sequence'. ValueError: if SavedModel.signature_def[activation_type] has more than one output. """ if use_latest_savedmodel: savedmodel_dir_path = latest_savedmodel_path_from_base_path( savedmodel_dir_path) self.batch_size = batch_size self._graph = tf.Graph() self._model_name_scope = 'inferrer' with self._graph.as_default(): self._sequences = tf.placeholder( shape=[None], dtype=tf.string, name='sequences') self._fetch = in_graph_inferrer( self._sequences, savedmodel_dir_path, activation_type, name_scope=self._model_name_scope) self._sess = tf.Session( config=session_config if session_config else tf.ConfigProto()) self._sess.run([ tf.initializers.global_variables(), tf.initializers.local_variables(), tf.initializers.tables_initializer(), ]) self._savedmodel_dir_path = savedmodel_dir_path self.activation_type = activation_type self._use_tqdm = use_tqdm if memoize_inference_results: self._get_activations_for_batch = self._get_activations_for_batch_memoized else: self._get_activations_for_batch = self._get_activations_for_batch_unmemoized def __repr__(self): return ('{} with feed tensors savedmodel_dir_path {} and ' 'activation_type {}').format( type(self).__name__, self._savedmodel_dir_path, self.activation_type) def _get_tensor_by_name(self, name): return self._graph.get_tensor_by_name('{}/{}'.format( self._model_name_scope, name)) def _get_activations_for_batch_unmemoized(self, seqs, custom_tensor_to_retrieve=None): """Gets activations for each sequence in list_of_seqs. [ [activation_1, activation_2, ...] # For list_of_seqs[0] [activation_1, activation_2, ...] # For list_of_seqs[1] ... ] In the case that the activations are the normalized probabilities that a sequence belongs to a class, entry `i, j` of `inferrer.get_activations(batch)` contains the probability that sequence `i` is in family `j`. Args: seqs: tuple of strings, with characters that are amino acids. custom_tensor_to_retrieve: string name for a tensor to retrieve, if unset uses default for signature. Returns: np.array of floats containing the value from fetch_op. """ if custom_tensor_to_retrieve: fetch = self._get_tensor_by_name(custom_tensor_to_retrieve) else: fetch = self._fetch with self._graph.as_default(): return self._sess.run(fetch, {self._sequences: seqs}) @functools.lru_cache(maxsize=None) def _get_activations_for_batch_memoized(self, seqs, custom_tensor_to_retrieve=None): return self._get_activations_for_batch_unmemoized( seqs, custom_tensor_to_retrieve) def get_activations(self, list_of_seqs, custom_tensor_to_retrieve=None): """Gets activations where batching may be needed to avoid OOM. Inputs are strings of amino acids, outputs are activations from the network. Args: list_of_seqs: iterable of strings as input for inference. custom_tensor_to_retrieve: string name for a tensor to retrieve, if unset uses default for signature. Returns: np.array of scipy sparse coo matrices of shape [num_of_seqs_in_batch, ...] where ... is the shape of the fetch tensor. """ np_seqs = np.array(list_of_seqs, dtype=np.object) if np_seqs.size == 0: return np.array([], dtype=float) if len(np_seqs.shape) != 1: raise ValueError('`list_of_seqs` should be convertible to a numpy vector ' 'of strings. Got {}'.format(np_seqs)) logging.debug('Predicting for %d sequences', len(list_of_seqs)) lengths = np.array([len(seq) for seq in np_seqs]) # Sort by reverse length, so that the longest element is first. # This is because the longest element can cause memory issues, and we'd like # to fail-fast in this case. sorter = np.argsort(lengths)[::-1] # The inverse of a permutation A is the permutation B such that B(A) is the # the identity permutation (a sorted list). reverser = np.argsort(sorter) activation_list = [] batches = np.array_split(np_seqs[sorter], np.ceil(len(np_seqs) / self.batch_size)) if self._use_tqdm: batches = tqdm.tqdm( batches, position=0, desc='Annotating batches of sequences', leave=True, dynamic_ncols=True) for batch in batches: batch_activations = self._get_activations_for_batch( tuple(batch), custom_tensor_to_retrieve=custom_tensor_to_retrieve) batch_activations_sparse = [scipy.sparse.coo_matrix(x) for x in batch_activations] activation_list.append(batch_activations_sparse) activations = np.concatenate(activation_list, axis=0)[reverser] return activations def get_variable(self, variable_name): """Gets the value of a variable from the graph. Args: variable_name: string name for retrieval. E.g. "vocab_name:0" Returns: output from TensorFlow from attempt to retrieve this value. """ with self._graph.as_default(): return self._sess.run(self._get_tensor_by_name(variable_name)) def latest_savedmodel_path_from_base_path(base_path): """Get the most recent savedmodel from a base directory path.""" protein_export_base_path = os.path.join(base_path, 'export/protein_exporter') suffixes = [ x for x in tf.io.gfile.listdir(protein_export_base_path) if 'temp-' not in x ] if not suffixes: raise ValueError('No SavedModels found in %s' % protein_export_base_path) # Sort by suffix to take the model corresponding the most # recent training step. return os.path.join(protein_export_base_path, sorted(suffixes)[-1]) def predictions_for_df(df, inferrer): """Returns df with column that's the activations for each sequence. Args: df: DataFrame with columns 'sequence' and 'sequence_name'. inferrer: inferrer. Returns: pd.DataFrame with columns 'sequence_name', 'predicted_label', and 'predictions'. 'predictions' has type np.ndarray, whose shape depends on inferrer.activation_type. """ working_df = df.copy() working_df['predictions'] = inferrer.get_activations( working_df.sequence.values).tolist() return working_df def serialize_inference_result(sequence_name, activations): """Serializes an inference result. This function is the opposite of deserialize_inference_result. The full format returned is a base-64 encoded ( np compressed array of ( dict of (seq_name: activations)))) Benefits of this setup: - Takes advantage of np compression. - Avoids explicit use of pickle (e.g. np.save(allow_pickle)). - Is somewhat agnostic to the dictionary contents (i.e. you can put whatever you want in the dictionary if we wanted to reuse this serialization format) - No protos, so no build dependencies for colab. - Entries are serialized row-wise, so they're easy to iterate through, and it's possible to decode them on the fly. Args: sequence_name: sequence name. activations: np.ndarray. Returns: encoded/serialized version of sequence_name and activations. """ with io.BytesIO() as bytes_io: np.savez_compressed(bytes_io, **{sequence_name: activations}) return base64.b64encode(bytes_io.getvalue()) def deserialize_inference_result(results_b64): """Deserializes an inference result. This function is the opposite of serialize_inference_result. The full format expected is a base-64 encoded ( np compressed array of ( dict of (seq_name: activations)))) Benefits of this setup: - Takes advantage of np compression. - Avoids explicit use of pickle (e.g. np.save(allow_pickle)). - Is somewhat agnostic to the dictionary contents (i.e. you can put whatever you want in the dictionary if we wanted to reuse this serialization format) - No protos, so no build dependencies for colab. - Entries are serialized row-wise, so they're easy to iterate through, and it's possible to decode them on the fly. Args: results_b64: bytes with the above contents. Returns: tuple of sequence_name, np.ndarray (the activations). Raises: ValueError if the structured np.array containing the activations doesn't have exactly 1 element. """ bytes_io = io.BytesIO(base64.b64decode(results_b64)) single_pred_dict = dict(np.load(bytes_io)) if len(single_pred_dict) != 1: raise ValueError('Expected exactly one object in the structured np array. ' f'Saw {len(single_pred_dict)}') sequence_name = list(single_pred_dict.keys())[0] activations = list(single_pred_dict.values())[0] return sequence_name, activations def parse_shard(shard_path): """Parses file of gzipped, newline-separated inference results. The contents of each line are expected to be serialized as in `serialize_inference_result` above. Args: shard_path: file path. Yields: Tuple of (accession, activation). """ with tf.io.gfile.GFile(shard_path, 'rb') as f: with gzip.GzipFile(fileobj=f, mode='rb') as f_gz: for line in f_gz: # Line-by-line. yield deserialize_inference_result(line) def parse_all_shards(shard_dir_path): """Parses directory of files of gzipped, newline-separated inference results. The contents of each line are expected to be serialized as in `serialize_inference_result` above. Args: shard_dir_path: path to directory containing shards. Returns: DataFrame with columns sequence_name (str); predictions (rank 1 np.ndarray of activations). """ files_to_process = utils.absolute_paths_of_files_in_dir(shard_dir_path) list_of_shard_results = [parse_shard(f) for f in files_to_process] return pd.DataFrame( list(itertools.chain(*list_of_shard_results)), columns=['sequence_name', 'predictions']) def get_preds_at_or_above_threshold(input_df, inferrer_list, threshold): """Runs ensembled inference; returns dataframe of filtered inference results. Includes predictions >= threshold. Because more than one label can be predicted for a sequence, the same sequence_name may appear in multiple output rows Args: input_df: pd.DataFrame with columns sequence (str), sequence_name (str). inferrer_list: list of ensemble elements. threshold: float. Keep inference results above this threshold. Returns: pd.DataFrame with columns sequence_name (str), predicted_label (str), and confidence (float). `sequence_name`s are sorted in the original order they came in. """ if threshold == 0.: raise ValueError('The given threshold was 0. Please supply a ' 'value between 0 (exclusive) and 1 (inclusive). A value ' 'of zero will report every label for every protein.') predictions = np.mean([ inferrer.get_activations(input_df.sequence.values.tolist()) for inferrer in inferrer_list ], axis=0) cnn_label_vocab = inferrer_list[0].get_variable('label_vocab:0').astype(str) output_dict = {'sequence_name': [], 'predicted_label': [], 'confidence': []} for idx, protein_sparse in enumerate(predictions): protein = np.asarray(protein_sparse.todense())[0] proteins_above_threshold = protein >= threshold labels_predicted = cnn_label_vocab[proteins_above_threshold] for label, confidence in zip(labels_predicted, protein[proteins_above_threshold]): output_dict['sequence_name'].append(input_df.sequence_name.values[idx]) output_dict['predicted_label'].append(label) output_dict['confidence'].append(confidence) return pd.DataFrame(output_dict) ================================================ FILE: inference_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for module inference.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import gzip from absl import flags from absl.testing import absltest from absl.testing import parameterized import numpy as np import pandas as pd import scipy.sparse import inference import test_util import utils import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS class _InferrerFixture(object): """A mock inferrer object. See docstring for get_activations. """ activation_type = 'serving_default' def __init__(self, activation_rank=1): """Constructs a mock inferrer with activation output of specified rank. Args: activation_rank: int. Use 1 for activations that have a single float per sequence, 2. for a vector per sequence, etc. """ self._activation_rank = activation_rank def get_variable(self, x): if x == 'label_vocab:0': return np.array(['LABEL1']) else: raise ValueError( 'Fixture does not have an implementation for this variable') def get_activations(self, input_seqs): """Returns a np.array with contents that are the length of each seq. The shape of the np.array is dictated by self._activation_rank - see docstring of __init__ for more information. Args: input_seqs: list of string. Returns: np.array of rank self._activation_rank, where the entries are the length of each input seq. See Inferrer.get_activations for more information about what this class is mocking. """ dense = np.reshape([len(s) for s in input_seqs], [-1] + [1] * (self._activation_rank - 1)) return np.array([scipy.sparse.coo_matrix(x) for x in dense]) class InGraphInferrerTest(tf.test.TestCase, parameterized.TestCase): def testCanInfer(self): graph = tf.Graph() with graph.as_default(): sequences = tf.placeholder(shape=[None], dtype=tf.string) output_tensor = inference.in_graph_inferrer( sequences, test_util.savedmodel_path(), tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY) input_seqs = [''.join(utils.FULL_RESIDUE_VOCAB), 'ACD'] with self.session(graph=graph) as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.tables_initializer()) result = sess.run(output_tensor, feed_dict={sequences: input_seqs}) self.assertLen(result, 2) class InferenceLibTest(parameterized.TestCase, tf.test.TestCase): def testBatchedInference(self): inferrer = inference.Inferrer(test_util.savedmodel_path(), batch_size=5) input_seq = 'AP' for total_size in range(15): full_list = [input_seq] * total_size activations = inferrer.get_activations(full_list) self.assertLen(full_list, activations.shape[0]) def testSortUnsortInference(self): inferrer = inference.Inferrer(test_util.savedmodel_path(), batch_size=1) input_seqs = ['AP', 'APP', 'AP'] # Sorting will move long sequence to the end. activations = inferrer.get_activations(input_seqs) # Make sure it gets moved back to the middle. self.assertAllClose(activations[0].todense(), activations[2].todense()) self.assertNotAllClose(activations[0].todense(), activations[1].todense()) def testStringInput(self): inferrer = inference.Inferrer(test_util.savedmodel_path()) # Simulate failure to use a list. with self.assertRaisesRegex( ValueError, '`list_of_seqs` should be convertible to a ' 'numpy vector of strings. Got *'): inferrer.get_activations('QP') def testMemoizedInferrerLoading(self): inferrer = inference.memoized_inferrer( test_util.savedmodel_path(), memoize_inference_results=True) memoized_inferrer = inference.memoized_inferrer( test_util.savedmodel_path(), memoize_inference_results=True) self.assertIs(inferrer, memoized_inferrer) def testMemoizedInferenceResults(self): inferrer = inference.Inferrer( test_util.savedmodel_path(), memoize_inference_results=True) activations = inferrer._get_activations_for_batch(('ADE',)) memoized_activations = inferrer._get_activations_for_batch(('ADE',)) self.assertIs(activations, memoized_activations) def testGetVariable(self): inferrer = inference.Inferrer(test_util.savedmodel_path()) output = inferrer.get_variable('conv1d/bias:0') self.assertNotEmpty(output) def test_predictions_for_df(self): inferrer_fixture = _InferrerFixture() input_seqs = ['AAAA', 'DDD', 'EE', 'W'] input_df = pd.DataFrame({ 'sequence_name': input_seqs, 'sequence': input_seqs }) actual_output_df = inference.predictions_for_df(input_df, inferrer_fixture) self.assertEqual(actual_output_df['predictions'].values.tolist(), [4, 3, 2, 1]) self.assertEqual(actual_output_df.sequence_name.values.tolist(), input_seqs) def test_serialize_deserialize_inference_result(self): input_accession = 'ACCESSION' input_activations = np.array([1., 2., 3.]) serialized = inference.serialize_inference_result(input_accession, input_activations) deserialized_actual_accession, deserialized_actual_activations = inference.deserialize_inference_result( serialized) self.assertEqual(deserialized_actual_accession, input_accession) np.testing.assert_array_equal(deserialized_actual_activations, input_activations) def test_parse_sharded_inference_results(self): # Create input inference results. input_accession_1 = 'ACCESSION_1' input_activations_1 = np.array([1., 2., 3.]) input_accession_2 = 'ACCESSION_2' input_activations_2 = np.array([4., 5., 6.]) input_accession_3 = 'ACCESSION_3' input_activations_3 = np.array([7., 8., 9.]) # Create files and a directory containing those inference results. shard_1_contents = inference.serialize_inference_result( input_accession_1, input_activations_1) + b'\n' + inference.serialize_inference_result( input_accession_2, input_activations_2) shard_2_contents = inference.serialize_inference_result( input_accession_3, input_activations_3) shard_dir = self.create_tempdir() shard_1_filename = shard_dir.create_file('shard_1').full_path shard_2_filename = shard_dir.create_file('shard_2').full_path # Write contents to a gzipped file. with tf.io.gfile.GFile(shard_1_filename, 'wb') as f: with gzip.GzipFile(fileobj=f, mode='wb') as f_gz: f_gz.write(shard_1_contents) with tf.io.gfile.GFile(shard_2_filename, 'wb') as f: with gzip.GzipFile(fileobj=f, mode='wb') as f_gz: f_gz.write(shard_2_contents) actual = inference.parse_all_shards(shard_dir.full_path).values actual = sorted(actual, key=lambda x: x[0]) self.assertEqual(actual[0][0], input_accession_1) self.assertEqual(actual[1][0], input_accession_2) self.assertEqual(actual[2][0], input_accession_3) np.testing.assert_array_equal(actual[0][1], input_activations_1) np.testing.assert_array_equal(actual[1][1], input_activations_2) np.testing.assert_array_equal(actual[2][1], input_activations_3) @parameterized.named_parameters( dict( testcase_name='filters one sequence', input_df=pd.DataFrame({ 'sequence_name': ['seq1', 'seq2'], 'sequence': ['ACDE', 'WWWYYY'] }), threshold=5., expected=pd.DataFrame({ 'sequence_name': ['seq2'], 'confidence': [6.], 'predicted_label': ['LABEL1'], })), dict( testcase_name='filters no sequences, but preserves input sequence_name ordering', input_df=pd.DataFrame({ 'sequence_name': ['seq2', 'seq1'], 'sequence': ['WWWYYY', 'ACDE'] }), threshold=2., expected=pd.DataFrame({ # Note: doesn't sort by sequence_name. 'sequence_name': ['seq2', 'seq1'], 'confidence': [6., 4.], 'predicted_label': ['LABEL1', 'LABEL1'], })), ) def testGetPredsAboveThreshold(self, input_df, expected, threshold): inferrer_list = [_InferrerFixture(activation_rank=2)] # Assert that the first sequence was removed. actual = inference.get_preds_at_or_above_threshold(input_df, inferrer_list, threshold) test_util.assert_dataframes_equal(self, actual, expected) def testGetPredsAboveThresholdRaisesOnZeroThreshold(self): inferrer_list = [] input_df = pd.DataFrame() with self.assertRaisesRegex(ValueError, '0'): inference.get_preds_at_or_above_threshold(input_df, inferrer_list, 0.) if __name__ == '__main__': absltest.main() ================================================ FILE: install_models.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Download models and associated metadata.""" import logging import os from typing import Optional, Text import urllib from absl import app from absl import flags import utils import tensorflow.compat.v1 as tf import tqdm _logger = logging.getLogger('proteinfer') FLAGS = flags.FLAGS flags.DEFINE_bool( 'install_ensemble', False, 'Set to true to install an ensemble of models, not just one. ' 'More ensemble elements takes more time, but tends to be more accurate.') flags.DEFINE_string('model_cache_path', 'cached_models', 'Path to which to store downloaded models and metadata.') def download_models(model_cache_path, num_ensemble_elements = None): """Downloads Pfam, EC and GO models, defaulting to downloading ensembles.""" _logger.info('Downloading models') for model_type in tqdm.tqdm(['Pfam', 'EC', 'GO'], desc='Overall progress', position=1, leave=True): utils.fetch_oss_pretrained_models( model_type, model_cache_path, num_ensemble_elements=num_ensemble_elements) print('\n') # Because the tqdm bar is position 1, we need to print a newline. def get_description_file(model_cache_path): out_path = os.path.join(model_cache_path, utils.INSTALLED_LABEL_DESCRIPTION_FILE_NAME) with tf.io.gfile.GFile(out_path, 'wb') as out_file: with urllib.request.urlopen(utils.LABEL_DESCRIPTION_URL) as url_contents: out_file.write(url_contents.read()) def run(install_ensemble, model_cache_path): """Download and untar models and metadata.""" if install_ensemble: _logger.warning('Full installation downloads and unpacks ~10GB of data; ' 'Download time may take up to a half hour on ' 'slow internet connections. If you are looking for ' 'a lighter-weight installation or are a new user, we ' 'recommend running without the flag --install_ensemble ' 'set.') download_models( model_cache_path, num_ensemble_elements=utils.MAX_NUM_ENSEMBLE_ELS_FOR_INFERENCE) else: download_models(model_cache_path, num_ensemble_elements=1) get_description_file(model_cache_path) def main(_): run(install_ensemble=FLAGS.install_ensemble, model_cache_path=FLAGS.model_cache_path) if __name__ == '__main__': _logger.info('Process started.') app.run(main) ================================================ FILE: misc/price_et_al_table_s12.csv ================================================ Category,organism,orgId,locusId,sysName,locus_tag,protein_id,uniprotId,new_annotation,comment,original_description,SEED_description,KEGG_description,protein_sequence catabolism,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2078,,A1D30_RS07410,WP_063460136.1,tr|A0A165KQE5|A0A165KQE5_9BURK,D-mannose isomerase (EC 5.3.1.7),Specifically important for: D-Mannitol; D-Mannose. Often annotated as N-acylglucosamine 2-epimerase instead (SEED_correct),D-mannose isomerase (EC 5.3.1.7),D-mannose isomerase (EC 5.3.1.7),,MAIPPYPDFRSAAFLRQHLRATMAFYDPVATDASGGQFHFFLDDGTVYNTHTRHLVSATRFVVTHAMLYRTTGEARYQVGMRHALEFLRTAFLDPATGGYAWLIDWQDGRATVQDTTRHCYGMAFVMLAYARAYEAGVPEARVWLAEAFDTAEQHFWQPAAGLYADEASPDWQLTSYRGQNANMHACEAMISAFRATGERRYIERAEQLAQGICQRQAALSDRTHAPAAEGWVWEHFHADWSVDWDYNRHDRSNIFRPWGYQVGHQTEWAKLLLQLDALLPADWHLPCAQRLFDTAVERGWDAEHGGLYYGMAPDGSICDDGKYHWVQAESMAAAAVLAVRTGDARYWQWYDRIWAYCWAHFVDHEHGAWFRILHRDNRNTTREKSNAGKVDYHNMGACYDVLLWALDAPGFSKESRSAALGRP transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2554,,A1D30_RS09780,WP_063460535.1,tr|A0A162DKX0|A0A162DKX0_9BURK,"ABC transporter for L-Histidine, permease component 1",Specific phenotypes on L-Histidine. In a gene cluster that also includes hutD and imidazolonepropionase. Some subunits are annotated as transporting glutamine.,"Amino acid ABC transporter, permease protein","Amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MELDFSPVWAGVPQLLAGALVTVEITAASLLLGCVMGLLVGIGRLNPKRRVVYALCTAYVAAIRGTPLLVQLFILFFGLPQFGILLPAFVCGVIGLGIYSGAYVSEVVRGAIQSIDKGQMEAARSIGMSSGLAMRTVVLPQAVVRMIPPLGNEFIALIKNSALVSLLTIHDLMHEGQKIISVSYRSLEVYLAIAVVYFILTGATTLVLRRIELRLRAGGMVQ transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2555,,A1D30_RS09785,WP_063460536.1,tr|A0A165KWF3|A0A165KWF3_9BURK,"ABC transporter for L-Histidine, periplasmic substrate-binding component 1","Specific phenotype on L-Histidine. Do not understand why there seem to be two substrate binding proteins for this ABC transporter. Both SBPs have signal peptides, so they seem unlikely to be involved in efflux of a waste product instead.","Glutamine ABC transporter, periplasmic glutamine-binding protein (TC 3.A.1.3.2)","Glutamine ABC transporter, periplasmic glutamine-binding protein (TC 3.A.1.3.2)",polar amino acid transport system substrate-binding protein,MNLRRNLLLASLAAAAFCTTGAQAQDNVLRVGTDATFPPMEFVENGKRTGFDIELVEAIAKTMGKQVEWVDIDFKGLIPGLISKRFDMAVSAIYITDERKKVVDFTDSYYAGGLVVMVKADNKAINKLADLDGKKVSVQVGTKSVSYLTEKFPKVQRVEVEKNQEMFNLVDIGRADAAVTGKPAAFQYVRTRPGLRVLDEQLTTEEYGMALRKDTPELTKAVNGAITKLKADGTYAAIVKKWFSNSAAK transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2560,,A1D30_RS09810,WP_063460541.1,tr|A0A165KWL1|A0A165KWL1_9BURK,"ABC transporter for L-Histidine, ATPase component","Specific phenotype on L-Histidine. There may also be a second ATPase component, Ac3H11_2553, but it has no fitness data","Urea carboxylase-related ABC transporter, ATPase protein","Urea carboxylase-related ABC transporter, ATPase protein",sulfonate/nitrate/taurine transport system ATP-binding protein,MSSVSIQAVSRVFETAKGQRTQALQPVDFEVRDNDFVTILGPSGCGKSTLLRIVAGLDHATSGRVLLDGAPVEGPGAERGMVFQSYTLFPWLTIEQNIRFGLRERGMPEAQQKERAAYFIAKVGLRGFEQHFPKQLSGGMQQRTAIARALANDPKILLMDEPFGALDNQTRVLMQELLLGIWEAERKTVLFVTHDIDEAIFMANRVAVFSARPGRIKTELAVDLPHPRHYTIKTSPEFMDLKARLTEEIRAESMAADLH transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2561,,A1D30_RS09815,WP_063460542.1,tr|A0A165KWL8|A0A165KWL8_9BURK,"ABC transporter for L-Histidine, permease component 2",Specific phenotypes on L-Histidine. In a gene cluster that also includes hutD and imidazolonepropionase. Some subunits are annotated as transporting glutamine.,"ABC transporter, permease protein","ABC transporter, permease protein",sulfonate/nitrate/taurine transport system permease protein,VSARARWMLGLAFFVVFVAVWAFFTLGGFVSPTFLASPITMAKEGWLLFTEYGFIKDIGMTIWRVVGGFVLAAVIAVPLGIAMGAYKGIEAFFEPFISFCRYLPASAFIPLLILWAGIGEAQKILVIFIGSVFQITLMVAVTVGGARRDLVEAAYTLGAGHKGIVTRVLIPGAAPEIAETLRLVLGWAWTYVIVAELIGSSSGIGHMITDSQSLLNTGQIIFGIIIIGLIGLVSDFAFKALNHRLFAWSFVR transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2562,,A1D30_RS09820,WP_082836420.1,tr|A0A165KWM6|A0A165KWM6_9BURK,"ABC transporter for L-Histidine, periplasmic substrate-binding component 2","Specific phenotype on L-Histidine. Do not understand why there seem to be two substrate binding proteins for this ABC transporter. Both SBPs have signal peptides, so they seem unlikely to be involved in efflux of a waste product instead.",ABC transporter substrate-binding protein,ABC transporter substrate-binding protein,sulfonate/nitrate/taurine transport system substrate-binding protein,MTRSVVKWTSLAAAAACVMALAAPAQAQDTKIVLGMSGWTGFAPLTLADKAGIFKKNGLDVEIKMIPQKDRHLALASKSIQCAATTVETHVAWNANGVPIVQIFQMDKSYGADGIAVRGDIKSFADLKGKTIGVDAPGTAPYFGLAWMLSKNGMSLKDVKTTTLSPQAAAQAFVAGQNDAAMTYEPYLSTVRDNPAAGKILATTLDYPMVMDTVGCDPTWLKANGKAAQALANSYFQALDMIKADPAKSNEIMGAAVKQTGEQFAKSSSFLRWQDKAANQKFFAGELTTFMKDATAILLEAGIIRKANEDLAVTFDASFIK catabolism,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2940,,A1D30_RS11765,WP_063460789.1,tr|A0A165L336|A0A165L336_9BURK,D-sorbitol 2-dehydrogenase (EC 1.1.1.14),"Specifically important for: D-Sorbitol. Annotated as ribitol dehydrogenase; it could well have both activities, as with AT5G51970",Ribitol 2-dehydrogenase (EC 1.1.1.56),Ribitol 2-dehydrogenase (EC 1.1.1.56),ribitol 2-dehydrogenase,MSAARNLQGQVAAITGAASGIGFASAQTMADAGARVVLIDRDEAALAKACATIGPNALPLVLDLLDARQCASLLQRTLALAGQLDIFHANAGLYVGGDLVDADPDAIDRMLNLNVNVVMKNVHNVLPHMIERGTGDIIVTSSLAAHFPTPWEPVYASSKWAVNCFVQTVRRQVFKHGIRVGSISPGPVITSLLADWPAEKLAEAKASGSLIEAAEVAEVVLFMLTRPRGMTIRDVVMMPTNFDL transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2941,,A1D30_RS11770,WP_063460790.1,tr|A0A165L344|A0A165L344_9BURK,"ABC transporter for D-Sorbitol, ATPase component",Specific phenotypes on D-Sorbitol.,"Various polyols ABC transporter, ATP-binding component","Various polyols ABC transporter, ATP-binding component",,MAYLQLRGIEKFFGEHRAIKGIDLTIQQGEFIVFVGPSGCGKSTLLRLIAGLEAIDGGSLMLDGRDITDQPSSKRDLAMVFQSYALYPHMSVYENMSFALKLAKVDKQVIDEKVQNAARILNLTQYLQRTPKELSGGQRQRVAIGRAIVRAPKVFLFDEPLSNLDAALRGQTRVEIAKLHRDLGATTIYVTHDQVEAMTLADRVVVLRDGIIEQVGTPLELYDKPANQFVAQFIGTPQMNVVPVDKLPQPVQQQAPAAPAGAAVGAIGLRPENITVRTTGATPVGGQVDLIEALGAETLIYVTTPGGAQFVSRQNDRTDLRVGDAVSLDIDASQAHWFDTAGRVVAGHAA transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2942,,A1D30_RS11775,WP_063460791.1,tr|A0A165L353|A0A165L353_9BURK,"ABC transporter for D-Sorbitol, permease component 1","Specific phenotypes on D-Sorbitol. homolog in P. simiae WCS417 is important for mannitol and mannose as well, but in acidovorax, no phenotype on mannitol","Various polyols ABC transporter, permease component 2","Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MTVRRRSNFPLGLLALRTAAAWGVALLLFFPLGWLFLTAFKTELQAIAVPPLFVFTPTLENFHEVQERSDYLLYAKNSVITSVLSTVLGLMLAAPAAYAMAFFKGKYTKDILMWMLSTKMMPAVGALVPIYVLAQKSHLLDTQLALIIVFALSNLPIMVWMLYSHFKDIPHEILEAARMDGATLWQEVRLVLLPLGMGGLASTGLLCLVLSWNEAFWSLNLSAAKAGTLATLIASYSSPEGLFWAKLSAASLMAIAPIVVFGWFSQKQLVQGLTFGAVK transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2943,,A1D30_RS11780,WP_063460792.1,tr|A0A165L360|A0A165L360_9BURK,"ABC transporter for D-Sorbitol, permease component 2","Specific phenotypes on D-Sorbitol. homolog in P. simiae WCS417 is important for mannitol and mannose as well, but in acidovorax, no phenotype on mannitol","Various polyols ABC transporter, permease component 1","Various polyols ABC transporter, permease component 1",sorbitol/mannitol transport system permease protein,VELRTSPPIAHATACPFSAAFFRCFDGLSMNRRLLPRLLLTPAMATLFLWMIVPLVMTIYFSLIRYNLMQPDQTGFAGLENFEFFVTDPSFGTAVVNTILLLGSVILITVVLGVAIALLINEPFPGRGIVRVLLISPFFVMPTVNALMWKNMMMNPIYGVLAQVWIFFGAAPVDWLTDHPLFSVIVMVSWQWLPFATLIFMTALQSMNHEQLEASRMDGANYLQQLRYLYVPHLGRSVAVVVMIELIFLLSIFAEIYTTTAGGPGDASTNVTFLIFKQALLNFDAGVASAGALFAVVLANIAAVFLIRMVGKNLDK transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_2944,,A1D30_RS11785,WP_063460793.1,tr|A0A165L365|A0A165L365_9BURK,"ABC transporter for D-Sorbitol, periplasmic substrate-binding component",Specific phenotype on D-Sorbitol.,"Various polyols ABC transporter, periplasmic substrate-binding protein","Various polyols ABC transporter, periplasmic substrate-binding protein",sorbitol/mannitol transport system substrate-binding protein,MPRSTRPHTRLALAAAALAASLCGAAAHAQTELVIATVNNGHMIEMQKLSKNFEQAHPDIKLKWVTLEEGVLRQRVTTDIATKGGQFDVMTIGMYEAPIWGKKGWLQELKTDAAYDVDDLLPAVRNGLSIDGKLFAAPFYGESSMLMYRKDLADKAGVQVPERPTWPQIKDLAAKIHDPKNGVYGICLRGKPGWGDNMAFLSTLVNTFGGQWFDMQWKPQLESKPWKEAITFYVDLLKNYGPPGSSANSFNEILALTNSGKCGMWIDATIAASFVSDPKQSKVADQMAFAQAPTMNTPKGANWLWSWNLAIPAGSKKVDAAQKFITWSTSKEYVQLVAKTNGWANVPTGTRKSTYASPEFQKAARFAAAEKVAIDSANPTDSTLPKSPYVGVQFAAIPEFQAIGIAVGQQMSAALAGKTTVDAALKASQVSADREMKKAGYYK catabolism,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_603,,A1D30_RS22575,WP_063462980.1,tr|A0A165IRK1|A0A165IRK1_9BURK,L-2-keto-3-deoxyarabonate dehydratase (EC 4.2.1.43),"Specifically important for L-arabinose utilization and 80% similar to araD from A. brasilense (KDADA_AZOBR, PMID:16950779). This gene is also important for D-galactose utilization, which might be due to polar effects. SEED_correct",L-2-keto-3-deoxyarabonate dehydratase (EC 4.2.1.43),L-2-keto-3-deoxyarabonate dehydratase (EC 4.2.1.43),dihydrodipicolinate synthase,VPTTFHEDGTLDLDSQKRCLDFMIDAGVDGVCILANFSEQFSLSDAEREVLTRTSLEHVAGRVPVIVTTTHYGTRVCAERSRAAQDMGAAMVMVMPPYHGATFRVPEAQIYEFYARVSDAIRIPIMVQDAPASGTVLSAPFLARMAQEIENLAYFKIEVPGAASKLRELIRLGGDAIEGPWDGEEAITLLADLDAGATGAMTGGAFPDGIRPIIEAHRQGDMDQAFALYQRWLPLINHENRQGGILTAKALMKEGGVIACEAGRHPFPAMHPEVRRGLVDIARRLDPLVLRWAR catabolism,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_614,,A1D30_RS22630,WP_063462990.1,tr|A0A165IRU8|A0A165IRU8_9BURK,L-arabinose 1-dehydrogenase; D-galactose 1-dehydrogenase (EC 1.1.1.46; EC 1.1.1.48),Specifically important for: D-Galactose; L-Arabinose. Some other dehydrogenases are known to act on both of these substrates,Putative oxidoreductase in arabinose utilization cluster,Putative oxidoreductase in arabinose utilization cluster,,LIDCNIDMTQLFAPSSSSTDATGAPQGLAKFPSLQGRAVFVTGGGSGIGAAIVAAFAEQGARVAFVDVAREASEALAQHIADAGLPRPWWRVCDVRDVQALQACMADAAAELGSDFAVLVNNVASDDRHTLESVTPEYYDERMAINERPAFFAIQAVVPGMRRLGAGSVINLGSTGWQGKGTGYPCYAIAKSSVNGLTRGLAKTLGQDRIRINTVSPGWVMTERQIKLWLDAEGEKELARNQCLPDKLRPHDIARMVLFLASDDAAMCTAQEFKVDAGWV transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_791,,A1D30_RS19405,WP_063462373.1,tr|A0A165JCN6|A0A165JCN6_9BURK,"ABC transporter for Glycerol, ATPase component 1",Specific phenotype on Glycerol; D-Galactose. Has a TOBE domain after the ATPase domain. The phenotype on galactose is not explained.,"Glycerol-3-phosphate ABC transporter, ATP-binding protein UgpC (TC 3.A.1.1.3)","Glycerol-3-phosphate ABC transporter, ATP-binding protein UgpC (TC 3.A.1.1.3)",multiple sugar transport system ATP-binding protein,MQLALDSISKKVGAQTWLYDMSLALQSGAVTVLLGATQAGKTSLMRIMAGLDAPTAGRVTVDGKDVTGMPVRDRNVAMVYQQFINYPSMKVAANIASPLKLRGEKNIDARVREIASRLHIDMFLDRYPAELSGGQQQRVALARALAKGAPLMLLDEPLVNLDYKLREELREELTQLFAAGQSTVVYATTEPGEALLLGGYTAVLDEGQLLQYGPTAEVFHAPNSLRVARAFSDPPMNLMAASATAQGVRLQGGAELTLPLPQGAATAAGLTVGVRASALRVHARPGDVSVAGVVELAEISGSDTFVHASTPWGDLVAQLTGVHYFELGTAITLHLDPAQAYVFGADGRLAQAPARPVTAQGGR transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_792,,A1D30_RS19410,WP_063462374.1,tr|A0A165JCR1|A0A165JCR1_9BURK,"ABC transporter for Glycerol, ATPase component 2","Specific phenotype on Glycerol. Has a TOBE domain after the ATPase domain. Also mildly important on phenotype on galactose, which is not explained.","Glycerol-3-phosphate ABC transporter, ATP-binding protein UgpC (TC 3.A.1.1.3)","Glycerol-3-phosphate ABC transporter, ATP-binding protein UgpC (TC 3.A.1.1.3)",multiple sugar transport system ATP-binding protein,MARISLDLAHSYKPNPQQDSDYALLPLKMEFEDGGAYALLGPSGCGKTTMLNIMSGLLVPSHGKVLFDGRDVTRASPQERNIAQVFQFPVIYDTMTVAENLAFPLRNRKVPEGQIKQRVGVIAEMLEMSGQLNQRAAGLAADAKQKISLGRGLVRADVAAVLFDEPLTVIDPHLKWQLRRKLKQIHHELKLTLIYVTHDQVEALTFADQVVVMTRGKAVQVGSADALFERPAHTFVGHFIGSPGMNFLPAHRDGENLSVAGHRLASPVGRALPAGALQVGIRPEYLALAQPQQAGALPGTVVQVQDIGTYQMLTAKVGEHTVKARFTPETRLPSSGDTAWLQVLGEHTCYYKNEELLA transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_793,,A1D30_RS19415,WP_063462375.1,tr|A0A165JCS4|A0A165JCS4_9BURK,"ABC transporter for Glycerol, permease component 1",Specific phenotypes on Glycerol. in a gene cluster for glycerol utilization; see phenotype on both strands,"Glycerol-3-phosphate ABC transporter, permease protein UgpA (TC 3.A.1.1.3)","Glycerol-3-phosphate ABC transporter, permease protein UgpA (TC 3.A.1.1.3)",multiple sugar transport system permease protein,MSTTTKPVNQKAWFLILPVIICVAFSAILPLMTVVNYSVQDIISPERRVFVGTEWFAAVMRDEELHAALWRQLTFSLAVLAVEIPLGILLALSMPAQGWKSSAVLVVVALSLLIPWNVVGTIWQIYGRADIGLMGRMLQEMGIEYSYTGNATQAWLTVLLMDVWHWTPLVALLAFAGLRSIPDAYYQAARIDGASKFAVFRYIQLPKMRGVLMIAVLLRFMDSFMIYTEPFVLTGGGPGNATTFLSQYLTTKAVGQFDLGPAAAFSLIYFFIILLLCFILYNWMQRVGTVSDEGAGHE transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_794,,A1D30_RS19420,WP_063462376.1,tr|A0A165JCS9|A0A165JCS9_9BURK,"ABC transporter for Glycerol, permease component 2",Specific phenotype on Glycerol.,"Glycerol-3-phosphate ABC transporter, permease protein UgpE (TC 3.A.1.1.3)","Glycerol-3-phosphate ABC transporter, permease protein UgpE (TC 3.A.1.1.3)",multiple sugar transport system permease protein,MNEKRFQKRTLFLIAYLLFALLPIYWMVNMSFKTNAEILSTFSFFPQHFTWDNYKTIFTDESWYSGYINSLIYVSINMVITLTVALPAAYAFSRYSFLGDKHVFFWLLTNRMTPPAVFLLPFFQLYTTVGLMDTHIAVALAHLLFSVPLAVWILEGFMSGIPREIDETAYIDGYSFPRFFMTIFLPLIKAGVGVAAFFCFMFSWVELLLARTLTSVNAKPIVATMTRTVSASGMDWATLAAAGVLTIVPGAIVIWFVRHYIAKGFAMGRV transporters,Acidovorax sp. GW101-3H11,acidovorax_3H11,Ac3H11_796,,A1D30_RS19430,WP_063462378.1,tr|A0A165JCU2|A0A165JCU2_9BURK,"ABC transporter for Glycerol, periplasmic substrate-binding component","Specific phenotype on Glycerol. Has signal peptide but not transmembrane helices, and CDD confirms it in the SBP family, not a permease as sometimes annotated","Glycerol-3-phosphate ABC transporter, permease protein UgpA (TC 3.A.1.1.3)","Glycerol-3-phosphate ABC transporter, permease protein UgpA (TC 3.A.1.1.3)",multiple sugar transport system substrate-binding protein,MKMQFKAIAFAAAALAMGQAAWAGEAEAKKWIDSEFQPSTLNKDQQMAEMKWFIDAAKKLQAKGVKEISVVSETITTHEYESKTLAKAFEEITGIKVKHDLIQEGDVVEKLQTSMQSGKSIYDGWISDSDLIGTHYRYGKIMNLTDYMGGAGKEWTNPGIDLKDYIGTKFTTGPDGKLYQLPDQQFANLYWFRADLFDRKDIKDKFKAKYGYDLGVPLNWSAYEDIAEFFTNDVKNIDGKPIYGHMDYGKKDPSLGWRFTDAWLSMAGTADIGAPNGLPIDEWGIRVADDKCTPVGASVARGGATNSPAAVYALTKYVDWMKKYAPKEATGMTFGEAGPVPAQGQIAQQIFWYTAFTADMTKPGLPVVNADGTPKWRMAPGPNGPYWKQGMQNGYQDVGSWTFFKDHDANKTAAAWLYAQFVTAKTTSLKKTMVGLTPIRESDIQSKAMTDMAPKLGGLVEFYRSPARVAWSPTGTNVPDYPKLAQLWWKNVAQAVTGEKTPQGAMDTLADEMDQVMARLERAGMAHCAPKLNAKSDPNKWLSDKQAPWKKLANEKPKGETIAYGTLLQAWKDGKTR transporters,Shewanella sp. ANA-3,ANA3,7022816,Shewana3_0067,SHEWANA3_RS00365,WP_011715352.1,tr|A0KR93|A0KR93_SHESA,sucrose permease scrT,"specific phenotype on sucrose; 82% identical to the sucrose transporter scrTII or Sfri_3989, which was validated by complementation (PMCID:PMC2996990); SEED_correct",major facilitator transporter (RefSeq),"Predicted sucrose permease, MFS family, FucP subfamily",,MVSDSNSVENRHHTRIRVLTYLMFFMFAMTSDAVGVIIPQLISEFGLSLSQASAFHYMPMIFIAISGLFLGFLADKIGRKLTILLGLLLFAIACFLFALGESFYYFLLLLALVGLAIGVFKTGALALIGDISRSTKQHSSTMNTVEGFFGVGAMVGPAIVSYLLISGVSWKYLYFGAGVFCLLLCWLAFRADYPQVKRSSTETINLTNTFSMMKNPYALGFSLAIGLYVATEVAIYVWMPSLLQEYQGDYTVLAAYALTIFFTLRAGGRFLGGWILNHFSWQQVMFWFSLAISLCYLGSMLYGVEAAVILLPLSGLFMSMMYPTLNSKGISCFPVAQHGSVAGVILFFTAVSAALAPLCMGLVGDIFGHVKYGFYLATGFAVLLCLLAGVNLIKDPSQALLSRETA catabolism,Shewanella sp. ANA-3,ANA3,7024896,Shewana3_2070,SHEWANA3_RS10895,WP_041412631.1,tr|A0KWY1|A0KWY1_SHESA,L-arabonate dehydratase (EC 4.2.1.25),Specifically important for: L-Arabinose. L-arabonate is an intermediate in the oxidation of L-arabinose (SEED_correct),dihydroxy-acid dehydratase (RefSeq),L-arabonate dehydratase (EC 4.2.1.25),dihydroxy-acid dehydratase,MCLGRRRCHMNNKKPKTLRSASWFGSDDKNGFMYRSWMKNQGIPEHHFQNKPVIGICNTWSELTPCNGHLRELAQRVKNGIREAGGIPVEFPVFSNGESNLRPSAMLTRNLAAMDTEEAIRGNPIDGVVLLVGCDKTTPALLMGAASCDLPTIVVTGGPMLNGKHKGKDVGSGTLVWELHQEYKAGNISLAAFMNAEADMSRSTGTCNTMGTASTMACMVETLGVSLPHNATIPAVDSRRQVLAHMSGMRIVDMVKEDLTLSKILSRDAFINAIKVNAAIGGSTNAVIHLKAIAGRIGVELSLDDWRHGYTVPTIVNLKPSGQYLMEDFYYAGGLPAVLRQLFEHDLLSKNTLTVNAASLWDNVKEAPCYNQEVIMSLENPLVENGGIRVLRGNLAPRGAVIKPSAASAHLMQHRGKAVVFESFDDYNARIGDPELDIDENSIMVLKNCGPKGYPGMAEVGNMGLPPKLLKKGIKDMVRISDARMSGTAFGTVVLHVAPEAQALGPLAAVQNGDMIALDTYAGTLQLEISDQELQARLAKLATVKSIPVNGGYLSLFKEHVLQADEGCDFDFLVGCRGAEIPAHSH catabolism,Shewanella sp. ANA-3,ANA3,7024897,Shewana3_2071,SHEWANA3_RS10900,WP_011717047.1,tr|A0KWY2|A0KWY2_SHESA,L-arabinose 1-dehydrogenase (EC 1.1.1.46),"Specifically important for: L-Arabinose. Also, was correctly annotated by PMID:20836887",short-chain dehydrogenase/reductase SDR (RefSeq),Putative oxidoreductase in arabinose utilization cluster,,MKLTNQYPSLQGKTIFISGGATGIGACLVNAFLEQGAKVAFVDILVEESTQLVADLKQTQPEASVTFYHCDLVDIAALKRVIAQVEDDLGPISVLINNAACDQRHSIDEVTPEYWDQCLNTNLRHYFFAVQAVRPQMQRLGGGSVINLGSMSWHNRQAGMAGYTASKAGAMGLTRGLAADLGKDKIRINTLTPGWVMTKRQLTHWVDKDTAKHIENNQCIKEYVMPEDIAAMALFLAADDSKLCTAQNFIVDGGWI catabolism,Shewanella sp. ANA-3,ANA3,7024898,Shewana3_2072,SHEWANA3_RS10905,WP_011717048.1,tr|A0KWY3|A0KWY3_SHESA,L-arabinose 1-epimerase (mutarotase) (EC 5.1.3.3),Specifically important for: L-Arabinose. Was correctly annotated by PMID:20836887 (more specific than EC number); (SEED_correct),aldose 1-epimerase (RefSeq),L-arabinose-specific 1-epimerase (mutarotase),aldose 1-epimerase,MQLSVTQKSLQHAAFADELQLVTLTNSHGLEVVLSNYGASIWSVKLYGQAQEPISLSLGYQDIKDWVTNPYYFGITAGRVANRIGKAQFPLAGKTIQLVANEGNNQLHGGPKGLSHCTWQVEMLQDNDSVAAVFSITSPDGDQGFPGNLAITLEYRLNENNELTLNYQAVCDQTTPVCLTNHAYWNLASNKQQGILGHELSIQASHFLALDNEQIPTGELILVDDGAYDFKQPKLIATDIKQLTNGYDHYFVMDRNPDNKNSLQTIAVLKDPLSGRELEISTTEVGVQFYSGNFLDGSHIDDEGYSINQFHGLCLETHGYPNAVNIDHFPSVMLEANTQYQQITVHKFKNI catabolism,Shewanella sp. ANA-3,ANA3,7024914,Shewana3_2088,SHEWANA3_RS10985,WP_011717064.1,tr|A0KWZ9|A0KWZ9_SHESA,L-arabinolactonase (EC 3.1.1.15),Important on L-arabinose; Was correctly annotated by PMID:20836887 (SEED_correct),hypothetical protein (RefSeq),L-arabinolactonase (EC 3.1.1.15),,MQTVTVGELLMTIPVGNRLGEGVLWDDLHQSIWWTDILSSVIYRFHLASRSLETFPMPHRVGSFGLTAKPTTLIVAFDIGIAIYDIEDQSLTWLAQPESHFAGNRFNDGRIDRQGRFWAGTMVEQRDTLQQTAALYCLDEKGHCHQHLTNLEISNGLCWSVDGRTLYHADSPKHQIYQYDFDIEQGLLSRKRLFASTSHHIFPDGSDVDAAGYLWNAQWGGGQVVRYRPDGEVDLILKLPVTHPTSIAFGGEKRDLLIVTSAKHSLDASQLDQEPQAGDVFIYPLQGIYGVNSPRFCGQL catabolism,Shewanella sp. ANA-3,ANA3,7025618,Shewana3_2769,SHEWANA3_RS14565,WP_011717648.1,tr|A0KYX7|A0KYX7_SHESA,Branched-chain acyl-CoA dehydrogenase (EC 1.3.99.12),Specifically important for: L-Isoleucine. specificity for isoleucine indicates that 2-methylbutanoyl-CoA is a substrate (SEED_correct),acyl-CoA dehydrogenase domain-containing protein (RefSeq),Branched-chain acyl-CoA dehydrogenase (EC 1.3.99.12),,MDFNFNEDQRQFADLARQFAADELAPFAAKWDEEHHFPKDVIQKAGELGFCSLYSPESEGGMGLSRLDASIIFEELSKGCTATTAMLTIHNMATWMVTTWGTDTLRQAWSEPLTTGQMLASYCLTEPGAGSDAASLQTKAVREGDEYVVSGSKMFISGAGSTELLVVMCRTGQAGPKGISAIAIPADSEGIIYGKAEDKMGWNAQPTRLVTFDNVRVPVANLLGEEGQGFTFAMKGLDGGRINIATCSVGTAQAALERATQYMNERQQFGKPLAAFQALQFKLADMATELVAARQMVRLAAFKLDSGDPEATAYCAMAKRFATDVGFQVCDAALQIHGGYGYIREYPLERHFRDVRVHQILEGTNEIMRLIIARRLLDENAGQIL transporters,Shewanella sp. ANA-3,ANA3,7025962,Shewana3_3110,SHEWANA3_RS16315,WP_011717943.1,tr|A0KZW5|A0KZW5_SHESA,N-acetylglucosamine transporter nagP,"specific phenotype on NAG and D-glucosamine. This protein is similar to SO3503, which was named nagP (PMID:16857666). There is another putative glucosamine transporter (nagX, Shewana3_3111) but the phenotype of 3110 on glucosamine does not seem to be a polar effect. (It is observed on both strands, conserved in S. amazonensis SB2B, and 3110 is downstream of nagX). It apears that both nagP and nagX might be required for glucosamine utilization. NagX has weak similarity to acyltransferases (Q3UDW8, or possibly opgC from Rhodobacter sphaeroides) so nagX might convert glucosamine into another compound before transport by nagP. (SEED_correct)",glucose/galactose transporter (RefSeq),"N-acetyl glucosamine transporter, NagP",,MTLDKSQQKSSFLPMAIVAALFFILGFATWLNGSLMPYLKQILQLNPFQASLILFSFYIAVTFTALPSAWVIRKVGYKNGMALGMGIMMLAGLLFIPAAKTQIFGLFLCAQLVMGTGQTLLQTAVNPYVVRLGPEESAAARVSVMGILNKGAGVIAPLVFSALILDSFKDRIGTTLTQVQIDEMANSLVFPYLGMAIFIGVLALAVKKSPLPELSNEDEVAEHTDKGQIKAALSHPNLAFGVIALFVYVAVEVIAGDTIGTFALSLGVEHYGVMTSYTMVCMVLGYTLGIILIPRFISQPTALMISAILGLLLTLAILFGDNNSYAIANALLVPFGGVALPDTLLFIAFLGLANAIVWPAVWPLALSGLGKLTSTGSALLIMGIAGGAFGPLFWGLTSSATDMGQQGGYMVMLPCYLFILFYAVKGHKMRHW catabolism,Shewanella sp. ANA-3,ANA3,7025966,Shewana3_3114,SHEWANA3_RS16335,WP_011717945.1,tr|A0KZW9|A0KZW9_SHESA,N-acetylglucosamine kinase (EC 2.7.1.59),"Specifically important for: D-Glucosamine Hydrochloride; N-Acetyl-D-Glucosamine. The first step in NAG catabolism. The phenotype on glucosamine is strong and is present on both strands, but is not conserved. It may be a polar effect. (SEED_correct)","ATPase, BadF/BadG/BcrA/BcrD type (RefSeq)",N-acetylglucosamine kinase of eukaryotic type (EC 2.7.1.59),,MGLVQTKDQQLFIGVDGGGSKCRATIYTADGTVLGTGVAGRANPLHGLAQTFASIEASTRQALLDAGMKETDSHLLVAGLGLAGVNVPRLYQDVISWQHPFAAMYVTTDLHTACIGAHRGADGAVIITGTGSCGYAHVGDDSLSIGGHGFALGDKGSGAWLGLKAAEHVLLALDGFATPTALTEMLLKHFGVSDALGIVEHLAGKSSSCYAELARSVLDCANAGDEVARGIVQEGADYISEMARKLFSLNPVRFSMIGGLAEPLQAWLGSDVVAKISETLAPPELGAMYFAQQQFNSVNINE transporters,Shewanella sp. ANA-3,ANA3,7026137,Shewana3_3279,SHEWANA3_RS17205,WP_011718086.1,tr|A0L0D4|A0L0D4_SHESA,"Vitamin B12 transporter, permease component (fecCD-like)",This protein is cofit with the B12-dependent methionine synthase MetH and is FecCD-like. It probably works with Shewana3_3280 (ABC transporter domain protein with similar phenotypes) and Shewana3_3272 (periplasmic binding protein). This protein is similar to SO_1033 which is also involved in B12 uptake (it is strongly cofit with metH; also see PMCid:PMC3219624),transport system permease protein (RefSeq),"ABC transporter (iron.B12.siderophore.hemin) , permease component",iron complex transport system permease protein,MIDTLKLVTMNFFSHIFTPLTRQQWLILGLAGFALLTPIAAASFGAANISFLDVLQVFINKLSHLFMADEAVSTSMTERIVMELRLPRIILAFVAGAGLSLAGSVLQTVTRNPLADPYLFGISSGASFGAVVMLTLFTGSGLFGNAGTIANSGIFSGSGTFAGLLWFSLPFGAFIGASLSVLIVLALAGLGLKSQVERMLLSGVATSFMFSALTSLLLYFASPQATASVLFWSLGSFAKASWSLLVLPSLVVLLSFFIILGWKRQIMALQAGDETAHTLGVNVPKLRLNMLLLCSLITAILVATCGGIGFVGLMIPHTVRLLFPGRQPILLTALVGGLFMVWIDVLARCLLGNQELPVGIITAAIGSFFFLLILRRRKLSN catabolism,Azospirillum brasilense Sp245,azobra,AZOBR_RS05755,AZOBR_RS05755,AZOBR_RS05755,WP_014239822.1,tr|G8AJX1|G8AJX1_AZOBR,Butyryl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Sodium butyrate. KEGG's annotation of azl:AZL_005040 has been updated to 3-(methylthio)propanoyl-CoA dehydrogenase [EC:1.3.8.-], which is questionable; phenotype indicates that butyryl-CoA is a substrate (SEED_correct)",acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MIPYTAPVDDLRFVLNEVVGLETVAALPNCDSAAPDLVDAVLEEAGKFASGVLAPLNRVGDKEGSVLENGVVRTPTGWKDAYSQFTESGWNSLPFEPEYGGQGLPWTVAFAVNEMWQAANLSFGLCPLLTQGAVDLLTEHASAEQKALYLPKMIAGEWNGTMNLTEPQAGSDLAAVRTRAVRADDGTYRITGQKIFITYGEHDLTDNIVHLVLARLPDAPPGIKGISLFIVPKFLPNADGTPGARNDLRCASLEHKLGIMASPTAVMAFGDDGGAVGFLVGQENRGIEYMFTMMNNARLGVGIQGVAIAERAYQQARDYAKGRVQSKDLADPKGSGVAIIRHPDVRRMLLDMRAKTEAARALALYAGTQLDVSRHHEDATVRAAATARVDILTPIVKAWSTDIGCDVASTGVQIHGGMGFIEETGAAQHYRDARITPIYEGTNGIHANDLTFRKTGRDGGESARTFIAEMRATVAELESIPGDDMEAIRTNLSAGLDALEQAVAWVVKTQADGDLRAAAAGAVNYLKLWGTVAGGWMLARSAAKALEGLRQPGANAPFLEAKLVTARFYAEQILAQGPGLLLPILTAHRTVMALSEDQF transporters,Azospirillum brasilense Sp245,azobra,AZOBR_RS08235,AZOBR_RS08235,AZOBR_RS08235,WP_014240349.1,tr|G8ALI8|G8ALI8_AZOBR,"L-proline and D-alanine ABC transporter, permease component 1",specific phenotype on L-proline and D-alanine and cofit with other subunits nearby,branched-chain amino acid transporter permease subunit LivH,High-affinity branched-chain amino acid transport system permease protein LivH (TC 3.A.1.4.1),,MEYFLQQLINGLSLGAIYGLIAIGYTMVYGIIGMINFAHGEIYMIGAFVALITFLAIGSLGITWVPLALLVMLVASMLFTAVYGWTVERIAYRPLRSSPRLAPLISAIGMSIFLQNYVQILQGARSKPLQPILPGNLTLMDGAVSVSYVRLATIVITIALMYGFTQLITRTSLGRAQRACEQDKKMAGLLGVNVDRVISLTFVMGAALAAVAGMMVLLIYGVIDFYIGFLAGVKAFTAAVLGGIGSLPGAMLGGVVIGLIEAFWSGYMGSEWKDVATFTILVLVLIFRPTGLLGRPEIEKV transporters,Azospirillum brasilense Sp245,azobra,AZOBR_RS15920,AZOBR_RS15920,AZOBR_RS15920,WP_014197389.1,tr|G8AR25|G8AR25_AZOBR,Large component of TRAP-type D-gluconate transporter,"specific phenotype on D-gluconic acid and cofit with other components nearby; 56% identical to SMa0250 of S. meliloti, which is required for gluconate utilization (PMCid:PMC2631997). Also is 56% identical to Atu2743 of A. tumefaciens, which takes up galacturonate (PMCid:PMC4751846), so this might also be a galacturonate transporter (we did not study galacturonate utilizaton in this organism)",dehydroascorbate transporter,"TRAP-type C4-dicarboxylate transport system, large permease component",,MALAVFLSSLFGLMLLGMPIAFALMLTGVALMVHLDFFDAQLVAQNMLSGADNYPLMAVPFFILAGELMNAGGISQRIINLAVSLVGHIRGGLGYVTIGASVMLASLSGSAIADTAALATLLIPMMRDNGYPVPRSAGLIASGGIIAPIIPPSMPFIIFGVTTNTSISGLFMAGIVPGLLMGAGLVITWMFVVRGMTVKLQPKASWGERRTALVEGVWALALPVIIIGGLRGGIFTPTEAAVVAAVYSLVVALFVYRQVTLKDLVPLLVQAARTTSTVMFLCAAALVSSYMVTLADLPQQMNEMLAPLLHEPKLLMVAITLLLLAVGTVMDLTPTILVLGPVLTPLAAAAGIDPTYFGVMFVLTGTLGLIHPPVCTVLNVVCGVARISLESATRGIWPFLLTYLLLLCLLIAVPEIVTAPLAFFRH transporters,Azospirillum brasilense Sp245,azobra,AZOBR_RS18550,AZOBR_RS18550,AZOBR_RS18550,WP_014197980.1,tr|G8AST9|G8AST9_AZOBR,"ABC transporter for nitrate, ATPase component",Specific phenotype on Sodium nitrate. (SEED_correct),ABC transporter ATPase,"Nitrate ABC transporter, ATP-binding protein",,MAILELKNVAKSYGGTEVLRGIDLSIAEGEFVAIVGFSGAGKTTLVNLMGGLAAPDAGTVLFRGAPVRGPGAERGVVFQNYSLMPWLSVKDNVALAVDAVHKGKPSPERGVLTRRAVETVGLGHATDRRPKELSGGMRQRVGFARALAMSPEMLLLDEPLSALDALTRAKLQDEIEAIWRKDRKTVVLITNDVDEAILLADRIIPLTPGPGATLGPAFAVDLPRPRDRTAVNHDPDFKRLRAEVTAWLMEAGASRAAAAVDESLTMPAATPITAAPPPKAYLAGQPGGLSDDRYVEFSRVTKTFATPKGPLTVVDGFDLKMRKGEFISLIGHSGCGKSTVLSMVAGLADVSSGGIVLDGKEVVGAGPDRAVVFQAPSLFPWLTALENVMLGVDRVYPQAGRGERNDIARYYLSRVGLGNAMHKTAAELSNGMKQRVGIARAFALSPKLLLLDEPFGMLDSLTRWELQEVLMEVWARTQVTAICVTHDVDEAILLADRVVMMSNGPNARIGHILEVDIPHPRTRQALLEHPRYYAYREELLNFLEGGHHGTVKAA transporters,Azospirillum brasilense Sp245,azobra,AZOBR_RS18555,AZOBR_RS18555,AZOBR_RS18555,WP_014197981.1,tr|G8ASU0|G8ASU0_AZOBR,"ABC transporter for nitrate, permease component",Specific phenotypes on Sodium nitrate; Sodium nitrate. Other substrates proposed by KEGG )sulfonate and taurine) were not tested. (KEGG_correct),nitrate ABC transporter permease,,sulfonate/nitrate/taurine transport system permease protein,MTSVTEASTLSSAEAMRARRKARLAHALNRTAATLQIVGLGWLSPLLRMAAGDSPRQQGQELWQQMGVPLTALMLFLAAWAWLAPQVNTSLGAIPGPAQVWEQIGILHADHKAERAKEAAFYDRQAKRNAELLAKDPAAEVKTRPYAGKPTYLDQILTSLKTVFTGFLLGALVAVPLGVACGLSRTVNAAMNPLIQIFKPVSPLAWLPLVTMVVSATVSSDNPTFEKSFLTSAVTVTLCSLWPTLINTAVGVSSIDKDLMNVGKVLQLSGPTMVRRLVLPSALPYIFTGLRLSLGVGWMVLIAAEMLAQNPGLGKFVWDEFQNGSSSSLARIMVAVFTIGLIGFLLDRVMLAFQAAVSHTGTR transporters,Azospirillum brasilense Sp245,azobra,AZOBR_RS18560,AZOBR_RS18560,AZOBR_RS18560,WP_082188256.1,tr|G8ASU1|G8ASU1_AZOBR,"ABC transporter for nitrate, periplasmic substrate-binding component",Specific phenotype on Sodium nitrate. (SEED_correct),nitrate ABC transporter substrate-binding protein,"Nitrate ABC transporter, nitrate-binding protein",sulfonate/nitrate/taurine transport system ATP-binding protein,IRLPASPRTALLSAAATLALMLGSAQAAPLDVEKDQLKLGFIKLTDMAPLAIAAEKGFFEDEGLSVTLEPQANWKVLLDRVISGELDGAHMLAGQPLGATIGFGTQANVVTAFSMDLNGNGITLSNEVWERMKPNLPKGPDGKPLHPIKADALKPVIAQYRAEGKPFTMGMVFPVSTHNYELRYWLAAGGINPGYYAPNDVSGQIQADALLSVTPPPQMPATLEAGTIFGYSVGEPWNQQAVMKGIGVPVITDTEIWKNNPEKVFGVTEGWAAKNPKTHLALVKALIRAAMWLDENGNANRAEAVKILAKSEYVGADAKVIANSMTGTFEYEKGDKRAVPDFNVFFRYNATYPFYSDAVWYLTQMRRWGQIAEAKPDAWYDETARKVYKPEIYLKAARLLVEEGKAKEADFPWTSDGYKPLDNGFIDGIAYDGRKPNEYLTKLPIGLKGGQAVQGGQLVGG catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS02015,BPHYT_RS02015,BPHYT_RS02015,WP_012431485.1,tr|B2SWP8|B2SWP8_PARPJ,phenylpyruvate ferredoxin oxidoreductase (EC 1.2.7.8),"Specifically important for: L-Phenylalanine. Annotated as indolepyruvate ferredoxin oxidoreductase, which has the same EC number; alternatively, could be a decarboxylase, but did not identify a separate aldehyde dehydrogenase",MFS transporter,"Indolepyruvate ferredoxin oxidoreductase, alpha and beta subunits",indolepyruvate ferredoxin oxidoreductase,MTARLPIDGTPALADYKLSDNLTATRGRIFLTGTQALVRLVLMQRAADKARGMNTAGFISGYRGSPLGMVDQQLWKAKKLLAASDIRFLPAINEELGGTAVLGTQRVESDPERTVEGVFAMWYGKGPGVDRAGDALKHGNAYGSSPHGGVLVVAGDDHGCVSSSMPHQSDFAMMAWHMPVVNPSNIADMLEFGLYGWELSRFSGAWVGYKAISETVESGSTVDLDALRTDWTMPQDFEVPAGGLHNRWPDLPSLTIESRMHAKLDAVRHFARVNSIDKWIAPSPHANVGIVTCGKAHLDLMETLRRLDLTVADLEAAGVRIYKVGLSFPLEMTRIDAFVSGLSEVLVIEEKGPVIEQQIKDYLYNRTQGTRPIVVGKNAEDGTLLLSSLGELRPSRILPVFANWLAKHKPALDRRERVVDLVAPQILSNAADSVKRTPYFCSGCPHNTSTKVPEGSIAHAGIGCHFMASWMERDTTGLIQMGGEGVDWASHSMFTKTRHVFQNLGDGTYFHSGILAIRQAVAAKATITYKILYNDAVAMTGGQPVDGSISVPQIARQVEAEGVSRFVVVSDEPEKYDGHHDQFPKGTTFHHRSEMDTVQRQLRDTDGVTVLIYDQTCAAEKRRRRKKGEFPDPDKRLFINEEVCEGCGDCGVQSNCLSVEPVETALGRKRRIDQSSCNKDYSCVNGFCPSFVTVEGGKLKKAAGAAFDPQALAARVEALPIPATHLDAAPYDILVTGVGGTGVVTVGALISMAAHLEGKSASVLDFMGFAQKGGSVLSFVRFAARDEWLNQVRIDTQQADVLLACDMVVGASADALQTVRHGRTRIVVNTHAIPNATFVTNPDATLHADALLDKMRHAAGAERMSTCDAQALATRFLGDTIGANILMLGYAWQLGLVPVSFGAMMRAIELNNVAVQMNQLAFSIGRLAAEDPAALEALWQARHLAKQSVRVDTLDELIAHREGRLQTYGGASYVKRYRALVDAARRAETSVDAKSERVTRAVATTFYRLLAVKDEYEVARLHTDAVFREALEAQFEGVAGKDFGIKFNLAPPTLTRPEPGKNPVKKTFGQWMWPVLGTLAKFSSLRGTMLDPFGRTLERKMERELAGDYETTLQRALARLDAGNLEDVAKLADLHARVRGYGHVKLANLAGVKRGERDLAARLQIEAATGESVRKSLEEMKGAGQLRGIPVVVAK catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS02050,BPHYT_RS02050,BPHYT_RS02050,WP_012431492.1,tr|B2SWQ5|B2SWQ5_PARPJ,D-mannose isomerase (EC 5.3.1.7),"Specifically important for: D-Mannose; D-Sorbitol; D-Mannitol; D-Fructose. Often annotated as N-acylglucosamine 2-epimerase, but phenotype on mannose, mannitol is consistent with annotation as mannose isomerase. Its role on sorbitol (which is probably oxidized to sorbose or fructose by BPHYT_RS16120) or fructose is unclear - this might be a polar effect on fructokinase (BPHYT_RS02045), as there is a stronger phenotype for insertions with the antibiotic resistance marker in the non-coding orientation (SEED_correct)",N-acylglucosamine 2-epimerase,D-mannose isomerase (EC 5.3.1.7),,MTQSDPLHPANGAAAAPPVESFRSRDFLLSHVQDTLRFYAPNVFDPSGGFFHFFRDDGSVYDKTTRHLVSSCRYVFNYAMAYRQFGDPQHLEYARHGLRFLREAHWDAQHEGYDWEIEWRDGKKRTLDATRHCYGLAFVLLAYSHAAMAGIEEAKPMIGATFELMEHRFWDAAAGLYADEASPDWRVSSYRGQNANMHTTEALLAAHEATRHLVYLDRAERVASNITLRQAKLSQGLVWEHFHADWSVDWHYNEEDSSNIFRPWGFQPGHQTEWAKLLLILERFRPLPWLLPRAIELFDAAMAHAWDEDHGGLYYGFGPDGTVCDHDKYFWVQAETFATAALLGKRTGNERFWDWYDEIWRYSWAHFVDHEYGAWYRILTCDNRKYSDEKSPAGKTDYHTMGACYEVLAHALPDGAAAAPESAEQTK transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS02740,BPHYT_RS02740,BPHYT_RS02740,WP_085966699.1,tr|B2SXE3|B2SXE3_PARPJ,"N-acetylglucosamine-specific PTS system, I, HPr, and IIA components (nagF)","Specifically important on NAG and cofit with the IIBC component nearby (BPHYT_RS02745). 58% identical to nagF or H16_A0311 of R. eutropha, which is important for NAG utilization (PMCid:PMC3127587). 58% similar to PA3760, which is required for NAG utilization in P. aeruginosa (PMCid:PMC2542419)",PTS glucose transporter subunit IIA,"Phosphoenolpyruvate-protein phosphotransferase of PTS system (EC 2.7.3.9) / PTS system, glucose-specific IIA component","PTS system, fructose-specific IIA component ; phosphotransferase system, enzyme I, PtsI ; phosphocarrier protein FPr",MSHSEGHIVLLAPMTGPVVPLANVPDPVFSGGMFGDGIGVDPLEGRLVAPCDATVTHLARTGHAVTLATAEGAEILLHIGIDTVELNGKGFAPMVAQGAHVRAGDVLIEFDQDQVALNAPSLVSVIAIANSDAFEIVERVQGGLLKAGETPLLVLRARDGAAAEASRQLSSTNVTEEARQQVTLVHAGGLHARPAARAREAARGFDARVEVRYEGRKAAIESVVGLLGLGAGEGATVELLGMGPQAAAAVAAIANELTREAHGEVEEKPARQSSPAPQAVARPAGETLAPNTLAGVCAAPGVAVGKLVRWDDADIDPPEKANGTSAAESRLLDKAIATVDADLDTTVRDASQRGAVGEAGIFSVHRVLLEDPTLLDAARDLISLGKSAGFAWREAIRAQIAILTNIEDALLAERAADLRDIEKRVLRALGYTSATARTLPEEAVLAAEEFTPSDLSTLDRSRVTALVMARGGATSHAAILARQAGIPALVAVGDALHAIPEGTQVVVNATTGRLEFAPTELDVERARLERTRLADVREANRRTSQQAAVTSDGRAIEVAANIATLDDAKTAVENGADSVGLLRTELLFIHRAAAPTTDEHRQSYQAIVDALSGRTAIIRTLDVGADKEVDYLTLPPEPNPALGLRGIRLAQVRPDLLDDQLRGLLAVQPLGAVRILLPMVTDVGELIRIRKRIDEFARELGRTEPIEVGVMIEVPSAALLADQLAQHADFLSIGTNDLTQYTLAMDRCQADLAAQADGLHPAVLRLIAATVQGADKHGKWVGVCGALAGDPLAMPLLVGLGVTELSVDPVSVPGIKARVRNLDYQLCRQRAQDALALESAQAVRAASRETWPLD transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS02745,BPHYT_RS02745,BPHYT_RS02745,WP_012431627.1,tr|B2SXE4|B2SXE4_PARPJ,"N-acetylglucosamine-specific PTS system, IIBC components (nagE)","Specifically important on NAG and cofit with the I-HPr-IIA component nearby (BPHYT_RS02740). CDD or PFam do not show any sign of this gene containing a IIA component, so the SEED annotation is misleading. 62% similar to H16_A0312 (nagE) and 58% similar to PA3761, which are both involved in NAG utilization (PMCid:PMC3127587, PMCid:PMC2542419). KEGG_correct.",PTS system N-acetylglucosamine-specific transporter subunit IIBC,"PTS system, N-acetylglucosamine-specific IIA component / PTS system, N-acetylglucosamine-specific IIB component (EC 2.7.1.69) / PTS system, N-acetylglucosamine-specific IIC component","PTS system, N-acetylglucosamine-specific IIB component ; PTS system, N-acetylglucosamine-specific IIC component",MDGNPFLKIQRLGRALMLPIAVLPVAGLLLRLGQPDVFNIKMIADAGGAIFDNLPLLFAIGVAVGFAKDNNGVAGLAGAIGYLIEVAVMKDINDKLNMGVLSGIVAGIVAGLLYNRYKDIKLPDYLAFFGGKRFVPIVTGVVCLVLGIAFGYVWQPVQAVIDTAGHWLTTAGALGAFVFGVLNRLLLVTGLHHILNSLTWFVFGTFTPPGGAAVTGDLHRFFAGDPTAGTFMTGFFPVMMFGLPAACLAMFHEAPKERRAVVGGLLFSMALTSFLTGVTEPIEFSFMFLAPVLYVIHALLTGISLAICSALGIHLGFTFSAGAIDYVLNYGLSTRGWWAIPIGLVYMVVYYGLFRFFIRKFNMATPGREPAAADEQVDSFAAGGFVSPVAGTAVPRAQRYIAALGGASNLSVVDACTTRLRLSVVDSNKVSENELKTIGARGVLKRGSTNVQVIIGPEADIIADEIRTVIAQGGGDAVKPAAAAPAQVVAAAPVAASVAQGSGPLDPDPLRWLAVFGGAGNVLSLDAIAATRLRIVVRDPSAVDRQRLATLDTAWISADTFHIVVGDAAQRYAEKLATRTTQSGGATPLPA catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS03220,BPHYT_RS03220,BPHYT_RS03220,WP_012431722.1,tr|B2SX46|B2SX46_PARPJ,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Tween 20; Trisodium citrate dihydrate. Tween 20 hydrolyzes to a mix of C12, C14, and C16 fatty acids; this is probably part of beta oxidation. The phenotype on citrate is milder and is not explained, but it is conserved. (SEED_correct)",acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MGQYAAPLRDMQFVLHELLNVEAEIKQMPKHADLDADTINAVLEEAGKFCSEVLFPLNHSGDQEGCTYVGDGVVTTPKGFKEAYAQYVEAGWPALGCDPEYGGQGLPAFVNNALYEMLNSANQAWTMYPGLSHGAYECLHAHGTPELQQRYLPKLVAGVWTGTMCLTEPHCGTDLGILRTKAEPNSDGSYAISGTKIFISSGEHDLAENIVHLVLARLPGAPNGTKGISLFIVPKFVPNEAGEPGERNGVKCGSIEHKMGIHGNATCVINLDNAKGWLVGEPNKGLNAMFVMMNAARLGVGMQSLGLTEIGYQNSLTYAKERLQMRSLTGPKAPEKAADPIIVHPDVRRMLLTQKAYAEAARAFSYWSALHIDKELSHADESVRKEAADLVALLTPILKAFLSDNAFESTNHAMQIYGGHGFIAEWGMEQYVRDARINMIYEGTNAIQALDLLGRKILGDMGAKMKKFGKLVSDFVEAEGVKPEMQEFINPLADIGEKVQKLTMEIGMKAMQNPDEVGAAAVPYLRTVGHLVFSYFWARMARVALDKEASGDPFYKAKLATARFYFAKLLPETAMTIRQARAGSKSLMDVEEALF transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS07675,BPHYT_RS07675,BPHYT_RS07675,WP_012432581.1,tr|B2T306|B2T306_PARPJ,"ABC transporter for L-Arginine, permease component 2",Very important for utilization of L-arginine,histidine/lysine/arginine/ornithine ABC transporter permease HisQ,"Histidine ABC transporter, permease protein HisQ (TC 3.A.1.3.1)",histidine transport system permease protein,MFLYGFGPVLLAGTIQTIELSVLSLAAAVLLGLAGAAAKLSFNRPLRAIATGYTTLIRSVPDLVLMLLLFYSIQIAVNNLTDALNLPQFDIDPFVAGVLTLGFIYGAYFTETFRGAFLAVPRGQLEAGSAYGMSGARVFTRILFPQMMRFALPGIGNNWQVLVKATALVSIIGLADVVKAAQDAGKSTFNMFFFILVAALIYLAITTASNLVLIWLEKRYSIGVRHAEL transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS07680,BPHYT_RS07680,BPHYT_RS07680,WP_012432582.1,tr|B2T307|B2T307_PARPJ,"ABC transporter for L-Arginine, permease component 1",Specific phenotypes on L-Arginine. no phenotype on histidine,histidine/lysine/arginine/ornithine ABC transporter permease HisM,"Histidine ABC transporter, permease protein HisM (TC 3.A.1.3.1)",histidine transport system permease protein,MIDILNQFWRAFLYWDGQRMSGLAVTLWLLVASVGIGFCAAIPLAVARVSKKRWLSTPVRLYTYVFRGTPLYVQLLLIYTGMYSLEFVRSHQLLDAFFRSGFHCAILAFALNTCAYTTEIFAGAIRSTSHGEVEAARAYGMSWFTMYRRIVIPSALRRALPLYSNEVILMLHATTVAFTATVPDILKVARDANSATYQSFDAFGLAALIYLVVSFVLVALFRRAERHWLGYLAVRTH transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS07735,BPHYT_RS07735,BPHYT_RS07735,WP_012432593.1,tr|B2T318|B2T318_PARPJ,"ABC transporter for L-Arginine, periplasmic substrate-binding component",Specific phenotype on L-Arginine. Not near the permease components,ABC transporter substrate-binding protein,Lysine-arginine-ornithine-binding periplasmic protein precursor (TC 3.A.1.3.1),lysine/arginine/ornithine transport system substrate-binding protein,MKMNWRNIAALALFAAASATAGTAAAADIKEVHFGVEASYAPFESKSPSGELQGFDIDVGNAVCAKLKAKCVWVENSFDGLIPALQARKFNAINSDMTITDQRRQAVDFTDPIYTIPNQMIAKKGSGLLPTPASLKGKHVGVLQGTIQETYAKARWAPAGVDVVPYQTQDQIYADLASGRLDAAFQDAEAASKGFLKKPQGAGFEFAGPAVTDEKLLGAGVGFGVRKGDKALKDALNQALKELKADGTIDRFAAKYFDVKVVLK catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS11290,BPHYT_RS11290,BPHYT_RS11290,WP_012433273.1,tr|B2T518|B2T518_PARPJ,2-ketogluconate 6-phosphate reductase (EC 1.1.1.43),"Specifically important for: D-Gluconic Acid sodium salt. Gluconate is probably primarily catabolized via gluconate kinase (BPHYT_RS16720) and the Entner-Doudoroff pathway (starting with phosphogluconate dehydratase, BPHYT_RS16735). However it appears that there is a side pathway involving gluconate 2-dehydrogenase (BPHYT_RS14520, which has no phenotype because this side path is not beneficial), 2-ketogluconate kinase (BPHYT_RS11300, no fitness data), and 2-ketogluconate-6-P reductase (this gene). A similar side path is known in P. putida (PMCID:PMC4646247) and this gene is similar to the P. putida 2-ketoglutarate-6-phosphate reductase (PP3376). (SEED_correct)",bifunctional glyoxylate/hydroxypyruvate reductase B,2-ketogluconate 6-phosphate reductase (EC 1.1.1.43),gluconate 2-dehydrogenase,MKKIVAWKSLPEDVLAYLQQHAQVVQVDATQHDAFVAALKDADGGIGSSVKITPAMLEGATRLKALSTISVGFDQFDVADLTRRGIVLANTPDVLTESTADTVFSLILASARRVVELAEWVKAGHWQHSIGPALFGVDVQGKTLGIVGLGRIGGAVARRAALGFNMKVLYTNRSANPQAEEAYGARRVELAELLATADFVCLQVPLTPETKHLIGAAELKSMKKSAILINASRGATVDEKALIEALQNGTIHGAGLDVFETEPLPSDSPLLKLANVVALPHIGSATHETRHAMARNAAENLVAALDGTLTSNIVNREVLSK catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16050,BPHYT_RS16050,BPHYT_RS16050,WP_012434194.1,tr|B2SY89|B2SY89_PARPJ,xylitol 2-dehydrogenase (EC 1.1.1.9),Specifically important for: Xylitol. (SEED_correct),iditol 2-dehydrogenase,Xylitol dehydrogenase (EC 1.1.1.9),L-iditol 2-dehydrogenase,MTTQSTDHDQQNMTAIVCHAPKDYRVEQVSKPRAGAHELVIRIAACGICASDCKCHSGAKMFWGGPSPWVKAPVIPGHEFFGFVEEIGEGAADHFGVKMGDRVIAEQIVPCGKCRYCKSGQYWMCEVHNIFGFQREVADGGMAEYMRIPPTAIVHKIPDGISLEDAAIIEPLACAIHTVNRGEVQLDDVVVIAGAGPLGLMMTQIAHLKTPKKLVVIDLVEERLALAREYGADVTINPKQDDALAIIHSLTDGYGCDVYIETTGAPIGVNQGMDLIRKLGRFVEFSVFGADTTLDWSVIGDRKELDVRGAHLGPYCYPIAIDLLARGLVTSKGIVTHGFSLEEWDEAIKIANSLDSIKVLLKPRA transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16095,BPHYT_RS16095,BPHYT_RS16095,WP_012434203.1,tr|B2SY98|B2SY98_PARPJ,"ABC transporter for D-Sorbitol, ATPase component",Specific phenotype on D-Sorbitol.,sugar ABC transporter ATP-binding protein,"Various polyols ABC transporter, ATP-binding component",sorbitol/mannitol transport system ATP-binding protein,MASVTLRNIRKAYDENEVMRDINLDIADGEFVVFVGPSGCGKSTLMRMIAGLEDISGGDLTIDGMRVNDVAPAKRGIAMVFQSYALYPHMTLYDNMAFGLKLAGTKKPEIDAAVRNAAKILHIDHLLDRKPKQLSGGQRQRVAIGRAITRKPKVFLFDEPLSNLDAALRVKMRLEFARLHDELKTTMIYVTHDQVEAMTLADKIVVLSAGNLEQVGSPTMLYHAPANRFVAGFIGSPKMNFMEGVVQSVTHDGVTVRYETGETQRVAVEPAAVKQGDKVTVGIRPEHLHVGMAEDGISARTMAVESLGDAAYLYAESSVAPDGLIARIPPLERHTKGETQKLGATPEHCHLFDSAGKAFQRKIVEVLAA transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16105,BPHYT_RS16105,BPHYT_RS16105,WP_012434205.1,tr|B2SYA0|B2SYA0_PARPJ,"ABC transporter for D-Sorbitol, permease component 1",Specific phenotypes on D-Sorbitol. no phenotype on mannitol; fitness data identifies a different mannitol transporter,mannitol ABC transporter permease,"Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MSQVASTPVSNPTAMTVPAKSPFAAIRRGIPGVIAWLVALLLFFPIFWMTITAFKTEQQAYASSLFFIPTLDSFREVFARSNYFSFAWNSILISAGVTILCLILAVPAAYAMAFFPTRRTQKVLLWMLSTKMMPSVGVLVPIYLLWKNSGLLDSVSGLVIVYTLINLPIAVWMSFTYFAEIPRDILEAGRIDGAATWQEIVYLLMPMSLPGLASTALLLVILSWNEAFWSINLSSSNAAPLTVFIASYSSPEGLFWAKLSAASLLAVAPILIVGWLSQKQLVRGLTFGAVK transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16110,BPHYT_RS16110,BPHYT_RS16110,WP_012434206.1,tr|B2SYA1|B2SYA1_PARPJ,"ABC transporter for D-Sorbitol, permease component 2",Specific phenotypes on D-Sorbitol. no phenotype on mannitol; fitness data identifies a different mannitol transporter,sugar ABC transporter permease,"Various polyols ABC transporter, permease component 1",sorbitol/mannitol transport system permease protein,MRPLRLPIMHAHPQTEKERETRKANSARWLATPSVAVLVLWMAIPLAMTIWFSFSRYNLLNPDLKGFAGFDNYKYLASDPSFGPSIGHTLELIISVLVITVVGGVLMAILFDRKFYGQGIARLLAIAPFFVMPTVSALIWKNMILHPVYGLIAQGMRAMGMQPIDWFAEYPLTAVIMIVAWQWLPFAFLILFTAIQSLDQEQKEAARIDGAGPFSMFFYITLPHLKRAIAVVVMMETIFLLSIFAEIYTTTGGGPGTATTNLSYLIYSLGLQQFDVGLASAGGILAVVLANIVSFFLVRMLAKNLKGEYEK transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16115,BPHYT_RS16115,BPHYT_RS16115,WP_012434207.1,tr|B2SYA2|B2SYA2_PARPJ,"ABC transporter for D-Sorbitol, periplasmic substrate-binding component",Specific phenotype on D-Sorbitol.,sugar ABC transporter substrate-binding protein,"Various polyols ABC transporter, periplasmic substrate-binding protein",sorbitol/mannitol transport system substrate-binding protein,MKPALKSTLKAVSAGAVACFALSASAATVTIATLNNPDMIELKKLSPEFEKANPDIKLNWVILEENVLRQRATTDITTGSGQFDVMTIGAYETPQWGKRGWLTPLTNLPADYDLNDVVKTARDGLSSGGQLYALPFYVESSMTYYRKDLFEKAGLKMPDQPTYDQIKQFADKLTDKANGIYGICLRGKAGWGENMAYGTTVVNTFGGRWFDEKWNAQLTSPEWKKAMTFYVDLLKKDGPPGASSNGFNENLTLMSSGKCAMWIDATVAAGMLYNKQQSQIADKVGFAAAPVAVTPKGSHWLWAWALAIPKSSKQADAAKKFITWATSKQYIELVAKDEGWASVPPGTRKSTYARAEYKQAAPFGDFVLKAIETADPEHPTLKPVPYTGVQFVGIPEFQSFGTVVGQSISGAIAGQMTIDQALAAGNATADRAVKQAGYQK catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16120,BPHYT_RS16120,BPHYT_RS16120,WP_012434208.1,tr|B2SYA3|B2SYA3_PARPJ,"sorbitol dehydrogenase, D-fructose forming (EC 1.1.1.14)","Specifically important for: D-Sorbitol. The KEGG EC number implies that sorbose would be the product, but it is not clear how sorbose would be consumed. The only other genes with specific phenotypes on sorbitol are an ABC transporter, the fructokinase BPHYT_RS02045, and an apparent polar effect on mannose isomerase BPHYT_RS02050. Also this protein is 60% similar to DHSO_RHOSH (polS), which is annotated in UniProt as forming sorbose but is believed to form D-fructose, see PMID: 15805591 (SEED_correct)",sorbitol dehydrogenase,Sorbitol dehydrogenase (EC 1.1.1.14),D-sorbitol dehydrogenase (acceptor),MAARLQDKVAILTGAASGIGEAVARRYLDEGARCVLVDVKPADSFGDSLRATYGDRVLTVSADVTRRDDIQRIVASTLERFGQIDILFNNAALFDMRPILEESWDVFDRLFAVNVKGMFFLMQAVAQKMVEQGCGGKIINMSSQAGRRGEALVSHYCATKAAVLSYTQSAALALAPHKINVNGIAPGVVDTPMWNEVDALFARYENRPLGEKKRLVGEAVPLGRMGVPDDLTGAALFLASADADYITAQTLNVDGGNWMS catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16405,BPHYT_RS16405,BPHYT_RS16405,WP_012434262.1,tr|B2SYF9|B2SYF9_PARPJ,galactonate dehydratase [EC: 4.2.1.6],Specifically important for: D-Galactose. galactonate is an intermediate in galactose oxidation (KEGG_correct),galactonate dehydratase,Gluconate dehydratase (EC 4.2.1.39),galactonate dehydratase,MKITKLETFIVPPRWCFLKIETDEGIVGWGEPVVEGRAHTVAAAVEELSDYLIGKDPLLIEDHWQVMYRSGFYRGGPITMSAIAGVDQALWDIKGKHHGVPIHALLGGQVRDKIKVYSWIGGDRPSDVANNARAVVERGFKAVKMNGSEELQIIDTFDKVQGVINNVAAVREAVGPNIGIGVDFHGRVHKPMAKVLAKELDPYKLLFIEEPVLSENAEALRDIVNQTNTPIALGERLYSRWDFKHILSGGYVDIIQPDASHAGGITECRKIASMAEAYDVALALHCPLGPIALATCLQIDAVSYNAFIQEQSLGIHYNQGNDLLDYIKNPEVFKYEDGFVSIPQGPGLGIEVNEEKVREMAKVGHRWRNPVWRHEDGSVAEW transporters,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16725,BPHYT_RS16725,BPHYT_RS16725,WP_012434323.1,tr|B2SYM4|B2SYM4_PARPJ,gluconate:H+ symporter (gntT),"Specific phenotype on gluconate and 72% identical to the gluconate permease (gntT, ACIAD0544) of A. baylayi ADP1 (PMCid:PMC4254613). KEGG_correct",permease,Gluconate transporter family protein,"gluconate:H+ symporter, GntP family",MEAVHGSTLLVFAVIAIALLILLITRYKVYPFLVLIIVSLLLGLASGMPMATIVKSFETGNGNTLGHIAIVVGLGTMLGKMMAESGGAERIATTLIDFFGEKNIHWAMMIVAIIVGLPVFFEVGFVLLIPIAFNVAKRTNKSLLLVGLPMVAGLSVVHGLLPPHPAAMLAVQAYHADIGRTIAYGLIVGVPTAIVAGPLFALMISRYIKLPKENALAAQFLGHGDETKNGAQTAAQNVAPKRELPSFGITLLTILLPVILMLVGSWADLFTTPKTLPNDLLHFAGNSDVALLIAVLVSFWTFGASRGFTREQIQKFCGDCLAPIAGITLIVGAGGGFGRVLMDSGISKEIVNVATAMHLSPLLFGWLVAALIRLATGSATVAMTTACGIVAPIASASGVHVEPELLVLATGSGSLIFSHVNDGGFWLIKEYFGMTVGQTFKTWSLLETIISLMGLGLTFALAAVV catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS16920,BPHYT_RS16920,BPHYT_RS16920,WP_012434361.1,tr|B2SYR3|B2SYR3_PARPJ,L-arabinose 1-dehydrogenase; D-galactose 1-dehydrogenase (EC 1.1.1.46; EC 1.1.1.48),# Specifically important in carbon source L-Arabinose; carbon source D-Galactose. Some other dehydrogenases are known to act on both of these substrates.,3-oxoacyl-ACP reductase,Putative oxidoreductase in arabinose utilization cluster,,MSSPANANVRLADSAFARYPSLVDRTVLITGGATGIGASFVEHFAAQGARVAFFDIDASAGEALADELGDSKHKPLFLSCDLTDIDALQKAIADVKAALGPIQVLVNNAANDKRHTIGEVTRESFDAGIAVNIRHQFFAAQAVMEDMKAANSGSIINLGSISWMLKNGGYPVYVMSKSAVQGLTRGLARDLGHFNIRVNTLVPGWVMTEKQKRLWLDDAGRRSIKEGQCIDAELEPADLARMALFLAADDSRMITAQDIVVDGGWA catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS17340,BPHYT_RS17340,BPHYT_RS17340,WP_012434437.1,tr|B2SYZ1|B2SYZ1_PARPJ,Phenylacetaldehyde dehydrogenase PaaZ (EC 1.2.1.3),"Specifically important for: L-Phenylalanine. phenylalanine is transaminated to phenylpyruvate, decarboxylated to phenylacetaldehyde, and this converts it to phenylacetate (SEED_correct)",aldehyde dehydrogenase,"Aldehyde dehydrogenase (EC 1.2.1.3), PaaZ",,MTHPLFTKHEDTLQKALTAVETRGYWSPFVEMPSPKVYGETANADGEAAFKAHLNATFQLDQPSTGETVGAEVSPFGFPLGVRYPKAEPAALIAAAAAAQRDWRAAGPQAWIGVCLEILARVNRASFEIGYSVMHTTGQAFMMAFQAGGPHAQDRALEAVVYAWDQLRRIPGDTHWEKPQGKNPPLAMHKRYTVVPRGTGLVLGCCTFPTWNGYPGLFADLATGNTVIVKPHPGAILPLALTVRIARDVLREAGFDPNVVTLLATEPNDGALVQDLALRPEIKLIDFTGSTQNGTWLERNAHQAQVYTEKAGVNQIVIDSVDDIKAAARNVAFSLALYSGQMCTAPQNIYVPRGGIRTAEGTLSFDEVAQAIAGAVQKLVADPARAVELLGAIQNEGVTQRIDEAAKLGRVLVESLTLEHPAFAGARVRTPLVLQLDAATDHAQFTKEWFGPISFVIATDSTAQSLDLAGAIASEHGALTLSVYSTDEAVLDDAHEASIRGGVALSINLTGGVFVNQSAAFSDFHGTGANPAANAALADAAFVANRFRVVQSRVHVEPKAAPAVAG catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS19730,BPHYT_RS19730,BPHYT_RS19730,WP_012434865.1,tr|B2T7R9|B2T7R9_PARPJ,L-arabonate dehydratase (EC 4.2.1.25),Specifically important for: L-Arabinose. L-arabonate is an intermediate in the oxidation of L-arabinose (SEED_correct),dihydroxy-acid dehydratase,L-arabonate dehydratase (EC 4.2.1.25),dihydroxy-acid dehydratase,MADSNQTKKPLRSQAWFGLKDRDGFLHRSWMKNQGIPHDEFDGRPVIGICNTWSELTPCNAHFRELAEYVKKGVHEAGGLPLEFPVMSLGETNLRPTAMLFRNLASMDVEESIRGNPMDGVILLVGCDKTTPALLMGAASCNLPALAVSGGPMLNGRFRGKNIGSGTGVWQMSEEVRAGTMTQEEFTEAESCMNRSRGHCMTMGTASTMASMVESLGMGLPHNAAIPAVDARRQVLAHLAGRRIVDMVREDLTMDKILTRQAFENAIRTNAAIGGSTNAVVHLIALAKRIGVELSLEDWELGSNVPCLVNLQPSGEYLMEDFYYAGGLPAVLKQLGEQGLLHKEALTVNGKTLWDNVRNAANYDEKVITTFAEPFKPKAGIAVLKGNLAPNGAVIKPSAATASLLKHRGRAVVFENIEELHAKIDDESLDIDEHCIMVLKGAGPKGYPGFAEVGNMPLPKKVLQKGITDMVRISDGRMSGTAYGAVVLHVSPEAAAGGPLAFVQTGDMIELDVEERRLHLDVTDEELARRRAAWQAPEAPKRGYYKLYVEHVLQADQGADLDFLVGSSGAPVPRDSH catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS22715,BPHYT_RS22715,BPHYT_RS22715,WP_012426462.1,tr|B2TD34|B2TD34_PARPJ,L-2-keto-3-deoxyarabonate dehydratase (EC 4.2.1.43),"important for L-arabinose utilization and 70% similar to araD from A. brasilense (KDADA_AZOBR, PMID:16950779). SEED_correct",cytochrome C biogenesis protein CcdA,L-2-keto-3-deoxyarabonate dehydratase (EC 4.2.1.43),dihydrodipicolinate synthase,MTQSSHPAAMRGVFPVAPTIFDDAGRLDLEGQKRCIDFMIDAGSNGLCILANFSEQFALSDDERNTLMHVVLEHVAGRVPVIVTTTHFSSYQCAERSRSAQAAGAAMVMVMPPYHGATIRIGERGIYEFYRTVSDAIGIPIMIQDAPVSGTPLSAPFLARMAREIDNVSYFKIEVPQAANKLRELIELGGDAIVGPWDGEEAITLMADLDAGATGSMTGGGYADGIRLIVDAYAAGDTEAAAAHYQQWLPLINYENRQGGLASCKALMKEGGVIRSDAVRHPLPQMHPATREGLLKVARRLDPLVLRWGR catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS23155,BPHYT_RS23155,BPHYT_RS23155,WP_012426549.1,tr|B2TDJ5|B2TDJ5_PARPJ,Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),"Important for utilization of putrescine as C or N source. This is the only transamination reaction in putrescine catabolism via gamma-glutamylputrescine (formed by BPHYT_RS23160), gamma-glutamylpurescine oxidase (possibly redundant, BPHYT_RS23125 or BPHYT_RS22700, neither has a phenotype), gamma-glutamylbutyraldehyde dehydrogenase (BPHYT_RS23175), and gamma-glutamyl-gamma-aminobutyrate hydrolase (probably BPHYT_RS23165, but no fitness data). Another gene (BPHYT_RS22435) likely has this activity as well, and is important (but with a weaker phenotype) on putresine as a carbon source. However that gene is not important with putrescine as a nitrogen source, which suggests that this gene is providing the activity.",aminotransferase,Omega-amino acid--pyruvate aminotransferase (EC 2.6.1.18),putrescine aminotransferase,MSYRTEEVAYVQPAQPAASAQASQAAQQQRSTAEYRALDAAHHIHPFSDMGSLNRSGSRVIVKAQGVYLWDSEGNKVIDGMAGLWCVNVGYGRKELADAAYKQMQELPYYNTFFKTTHPPVIELSALLAELAPEAFNHFFYCNSGSEGNDTVLRIVHQYWATQGKHSKKFVISRKNGYHGSTIAGGTLGGMGYMHEQMPSKVENIVHIDQPYFFGEAQGNLTPEEFALARAQQLEAKILEIGADNVAAFIGEPFQGAGGVIFPASTYWPEIQRICRKYDILLVADEVIGGFGRTGEWFAHQHFGFEPDLITLAKGLTSGYVPMGAVGLHDRVAKAIIENGDFNHGLTYSGHPVAAAVAVANLKLLRDEKIVDRVKNDTGPYFQKQLRETFANHPIIGEISGTGLVAGLQLAQDPKARKRFANGGDVGTICRDFCFNGNLIMRATGDRMLLSPPLVINKLEIDEIVSKAKKAIDATAQQLGIS catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS23160,BPHYT_RS23160,BPHYT_RS23160,WP_012426550.1,tr|B2TDJ6|B2TDJ6_PARPJ,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Specifically important for: Putrescine Dihydrochloride. The first step in putrescine catabolism via gamma-glutamyl-putrescine (SEED_correct),glutamine synthetase,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),glutamine synthetase,MHDIDDFLKKNRVTEIEAIIPDMAGIARGKIIPRSKFESGESMRLPQAVMIQTVTGDYPEDGTLTGVTDPDMVCVPDASTIRMIPWAVDPTAQVIHDCVHFDGTPVAISPRRVLRRVLELYKAKGWKPVIAPELEFYLVDMNKDPDLPLQPPIGRTGRPETGRQAYSIEAVNEFDPLFEDIYEYCEVQELEVDTLIHEVGAAQMEINFMHGDPLKLADSVFLFKRTVREAALRHKMYATFMAKPMEGEPGSAMHMHQSLVDEETGHNLFTGPDGKPTSLFTSYIAGLQKYTPALMPIFAPYINSYRRLSRFMAAPINVAWGYDNRTVGFRIPHSGPAARRIENRIPGVDCNPYLAIAATLAAGYLGMTQKLEATEPLLSDGYELPYQLPRNLEEGLTLMGACEPIAEVLGEKFVKAYLALKETEYEAFFRVISSWERRHLLLHV catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS34210,BPHYT_RS34210,BPHYT_RS34210,WP_012428702.1,tr|B2T9V2|B2T9V2_PARPJ,"2,4-diketo-3-deoxy-L-rhamnonate hydrolase (EC 3.7.1.-)","Specifically important for: L-Fucose; L-Rhamnose monohydrate. The substrate is also known as 2,4-diketo-3-deoxy-L-fuconate, and is an intermediate in both L-rhamnose and L-fucose degradation (SEED_correct)",ureidoglycolate lyase,"2,4-diketo-3-deoxy-L-fuconate hydrolase",,MKLLRYGPKGQEKPGLLDAQGKIRDLSKVVADIDGAALTDEGLAKLRALDPASLPLVEGNPRMGPCVGKIGKFICIGLNYADHAAESNLPVPAEPVIFNKWTSAISGPNDDVEIPRGSKKTDWEVELGVVIGKPAKYIDEANALDYVAGYCVINDVSEREWQIEKGGTWDKGKGFDTFGPIGPWVVTRDEVADPQNLSLWLEVDGHRYQNGSTKTMVFGVAKLVSYVSQCMSLQPGDVISTGTPPGVGMGVKPNPVFLKPGQTIRLGIEGLGEQTQKTYAAE catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS34225,BPHYT_RS34225,BPHYT_RS34225,WP_012428705.1,tr|B2T9V5|B2T9V5_PARPJ,L-fucose dehydrogenase; L-rhamnose 1-dehydrogenase (EC 1.1.1.122; EC 1.1.1.173),Specifically important for: L-Rhamnose monohydrate; L-Fucose. Annotated by SEED for the fucose dehydrogenase activity only,aldo/keto reductase,L-fuco-beta-pyranose dehydrogenase (EC 1.1.1.122),,MTATIGSKIGQRRRIGRGPLQVTGLGLGTAPLGGLYRDLSDEEAHATIAAAWDAGVRYFDTAPHYGNTKAEHRLGDALRRYPRADYVLSTKVGRRFVPRTTPFDDKEGWQNPLPFEAIYDYTHDGILRSFEDSQQRLGIVDIDILLVHDIGRVTHGDNHPHYWRQLTEGGGFRALDALRSSGAIKAVGLGVNEGAAILDAMAEFDIDCALLAGRYTLLEQTTLDDLLPACEKRGVSILLGGAFNSGILARGVQGDLKFNYGEAPPEVIERVARLEAVCRTHGVPLAAAALQFPYAHPTVATVLTGARSADELRENAASFELPIPAALWFALREEGLLDSRAPAPED catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS34230,BPHYT_RS34230,BPHYT_RS34230,WP_012428706.1,tr|B2T9V6|B2T9V6_PARPJ,L-fuconate dehydratase; L-rhamnonate dehydratase (EC 4.2.1.68; EC 4.2.1.90),Specifically important for: L-Rhamnose monohydrate; L-Fucose. annotated by SEED for fuconate activity only,hydrolase,"L-fuconate dehydratase (EC 4.2.1.68), type 2",altronate hydrolase,MPVAAQQPTLEGYLRGDGRKGIRNVVAVAYLVECAHHVAREIVTQFREPLDAFDDPSAEREPPVHLIGFPGCYPNGYAEKMLERLTTHPNVGAVLFVSLGCESMNKHYLVDVVRASGRPVEVLTIQEKGGTRSTIQYGVDWIRGAREQLAAQQKVPMALSELVIGTICGGSDGTSGITANPAVGRAFDHLIDAGATCIFEETGELVGCEFHMKTRAARPALGDEIVACVAKAARYYSILGHGSFAVGNADGGLTTQEEKSLGAYAKSGASPIVGIIKPGDIPPTGGLYLLDVVPDGEPRFGFPNISDNAEIGELIACGAHVILFTTGRGSVVGSAISPVIKVCANPATYRNLSGDMDVDAGRILEGRGTLDEVGREVFEQTVAVSRGAASKSETLGHQEFILTYKTFEPVGPACLPSSAAAQHRVVAIEPH catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS34235,BPHYT_RS34235,BPHYT_RS34235,WP_085966746.1,tr|B2T9V7|B2T9V7_PARPJ,putative accessory domain for L-fuconate/L-rhamnonate dehydratase (EC 4.2.1.68; EC 4.2.1.90),"Important for L-fucose and perhaps L-rhamnose catabolism, but seems to have milder phenotypes than the adjacent dehydratase _RS34230. CDD suggests (see cd11613) that it might be a dimerization domain for the dehydratase, but that wouldn't make sense for a separate ORF. Some homologs of this gene are fused to the dehydratase (which has a GD_AH_C domain).",hydrolase,Altronate dehydratase (EC 4.2.1.7),altronate hydrolase,MTSRLLPLQTDPRLILLSPADNCLIAAARLDCGTQVEIEGERVTLTRTIDLGHKVARHELAKDDKVLRYGAVIGHVTEAVARGAHLHTHNLESDYLPTYTHDAGHEFVHH catabolism,Burkholderia phytofirmans PsJN,BFirm,BPHYT_RS35445,BPHYT_RS35445,BPHYT_RS35445,WP_012428943.1,tr|B2TAE5|B2TAE5_PARPJ,2-aminobenzoate-CoA ligase [EC: 6.2.1.32],Specifically important for: L-Tryptophan. B. phytofirmans catabolizes tryptophan via 2-aminobenzoyl-CoA (KEGG_correct),2-aminobenzoate-CoA ligase,Acyl-coenzyme A synthetases/AMP-(fatty) acid ligases,2-aminobenzoate-CoA ligase,MEPSAHVDTFARDNLPPQDQWPVFLLDNPDVAYPARLNCASELLDRTIDAGHRDDPAIWSDVDGAPRATTYGELLALVNRSAHVLVDEMGLQPGNRVLLRGPNTLHMAVTALAALKVGLVVVPTMPLLRAKELKQIIDKAQVGAALCDARLTAELARCSDPEDEFYCAGLMQTRLFHDDSPDSLDTLAVNKPDHFTACDTAADDVCLIAFTSGTTGAPKGCMHFHRDVVAMCDLFPRHVLKPTSSDIFCGTPPLAFTFGLGGLLCFPLRVGASTVLIEKLTPETLLQTVERFHATVMFTAPTFYRQMAPLVAHHDVSSLKKTVSAGEALPDSTRRLWRDATGIDMIDGIGGTELIHIFISAQGDEIRPNAIGRAVPGYAVQAVDDDMQPVAPGTIGKLAVRGPTGCRYLADERQMKFVRDGWNLPGDSVYLDEDGYVFYQARADDMIVSAGYNISGPEVESVLLQHDAVSECGVIGVPDETRGQIVKAFVVVNPGYERDDKLVAQLQEFVKNSVAPYKYPRDIVFVDSLPRTETGKLKRFELRTMA transporters,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS03025,RR42_RS03025,RR42_RS03025,WP_052494419.1,tr|A0A0C4YBR9|A0A0C4YBR9_9BURK,"Inner membrane protein (FUSC-like) required for 4-hydroxybenzoate transport, together with NodT, MFP, and DUF1656 proteins (RR42_RS03040, RR42_RS03035, and RR42_RS03030)",specific phenotype on 4-hydroxybenzoate and cofit with other nearby components. Not clear if this is primarily an efflux pump (because of the high and potentially toxic concentration of hydroxybenzoate) or for uptake,transporter,Inner membrane component of tripartite multidrug resistance system,,MVSWPTGRDWIFSLKAFAAAMLALFIALYLGLPRPYWAMASVYIVAHPLTGATRSKALYRVLGTLLGAAASVAIVPPLVNAPVLLMAAVALWTGILLYVALLHRTPRSYVFMLAAYTLPMVALPSVSDPSGVFDIAVARAEEICLGIVCASIVGSTIFPAKVATVLGQKSAQWLADAALWATDMLSAHPAAKVSRHHSRHRLAADILALDQLISHLSYDAESAARVKHAQELRGRMTMLLPVLSSLASVVDALHQHAAGIPQRLSHNMALVSAWISAGAQAPLPALQLSQDAPIDDAGLEPGSAGPDWHAALAATAEGRLRELMELWQDCVSLQRRIGEAAPQGDWAPVFRRWGVGAARHYDHGMLLFSTATTALAIFSMGMVWIWTGWADGAGAVALGAVSCCFFAAMDEPAPMIRSFFRWNVVCLVLATIYLFVVLPNAHDFEMLVLMFAVPYLIIGVMMAQPRLALIAMPLAVVTANDIGIQGAYNADFNAFFNGNVAGIAGILFALVWTLVARPFGTRAAVRRLVRASWGDIARNATGREPGEHAHLRARMLDRLAQLVPRLAASEDETTGDGFTEVRVELSTLALQRELSALAPEHQHSVRRVLQGVASYYQARLDARAEAPPQVLRDRLAKAIRKVASRADQASREAFAALVEMHVALF transporters,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS03040,RR42_RS03040,RR42_RS03040,WP_043343879.1,tr|A0A0C4Y7B9|A0A0C4Y7B9_9BURK,"Outer membrane protein (NodT-like) required for 4-hydroxybenzoate transport, together with FUSC, MFP, and DUF1656 proteins (RR42_RS03025, RR42_RS03035, and RR42_RS03030)",specific phenotype on 4-hydroxybenzoate and cofit with other nearby components. Not clear if this is primarily an efflux pump (because of the high and potentially toxic concentration of hydroxybenzoate) or for uptake,RND transporter,Outer membrane component of tripartite multidrug resistance system,,MNTLRKALLPALVPLLFAACTTVGPDYHVPDNAAVKAQAANGPLLGTNNPAVSIAEVPDGWWRLYDDPKLDQLVQQALVANTDLRVAAANLKRAIAVYHEVEAENLPEARLSAGAERGQIAGEPLLKEEKIPVMNFGDVGFKVSYLIDFFGKLARADEAALAGAQASQAALDQARIGVVAETVRAYVQGCAATHELTVAEHQLALQSRGVELARKMVDAGRGQPLDLLRAQAQADVLRAALPRFKAEQEGAAYRLAVMLGKPPSATSQAEYACHEEPRLRQALPVGDGAALLKRRPDVRQAERELASATAKIGVATADLYPSIRIGASAGFTGILDHLGQAPTAHWGYGPLITWNIPTSGTRARVHGTEAGAEAALAHFDGVVLKALRETQSALSSYTRELERTQALRDARDKANEVARQNRQLYQAGRSPYLSSLDADRTLASTEASLAASESQVALDQINLFLALGGGWQNAPKVESRTMSEQAH catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS13535,RR42_RS13535,RR42_RS13535,WP_043352074.1,tr|A0A0C4Y492|A0A0C4Y492_9BURK,2-aminobenzoate-CoA ligase (EC 6.2.1.32),Specifically important for: L-Tryptophan. This bacterium catabolizes tryptophan via 2-aminobenzoyl-CoA; (KEGG_correct); SEED has a very broad annotation (Acyl-coenzyme A synthetases/AMP-(fatty) acid ligases),2-aminobenzoate-CoA ligase,Acyl-coenzyme A synthetases/AMP-(fatty) acid ligases,2-aminobenzoate-CoA ligase,MASTAHLDTYALDRLPPPQQWPAFLFNADTRYPERMNCALELVERHVREGRGERVAIRYEQDGAHRTVSYAELAALSNRIAHVLTGDMKLVPGSRVLLRGPNNLMMAACWLAVVKAGLIAVPTMPLLRAKELKQVIDRAAVSAALCDERLREELEANLKPGGEHHCPSLTQALYFNSSGAGSVEAAMANKPDGFEACDTAADDICLIAFTSGTTGQPKGTVHFHRDVIAMCDLFPRHVLKPGPDDIFCGTPPLAFTFGLGGILCFPLRVGASTVLAERLTPESLLALIQSWRATIVFTAPTFYRQMAALAPGYDLSSLQKSVSAGEALPDATRQSWKAATGIEMTDGIGGTEMMHIFISSAGADVRPGAIGKVVPGYIAQIVDEQMQPVPHGTVGKLAVRGPTGCRYLDDPRQANYVKDGWNLPGDTFKADADGYYYYQARSDDMIVSAGYNIAGPEVESALMRHEAVAECGVVGMPDPERGQIVAAYVVLRPGVAASDETCAALQSYVKAEIAPYKYPRKVIFVDALPRTETGKLQRFRLRQMAEQSGAPEGARQP catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS13555,RR42_RS13555,RR42_RS13555,WP_043347637.1,tr|A0A0C4YHI0|A0A0C4YHI0_9BURK,anthraniloyl-CoA monooxygenase (EC 1.14.13.40),"Specifically important for: L-Tryptophan. Antrhanioyl-CoA is a synonym for 2-aminobenzoyl-CoA, an intermediate in the degradation of Trp via 2-aminobenzoate (KEGG_correct)",salicylyl-CoA 5-hydroxylase,probable bifunctional hydroxylase/oxidoreductase,anthraniloyl-CoA monooxygenase,MRVLCIGGGPSGLYFGLLMKLRDPSSEVYVVERNRPYDTFGWGVVFSDATMDNLKQADPVSAEQINAAFNHWDDIDIHIGGRTLRSGGHGFIGIGRKRLLNILQARCEALGVHLVFETDVTDDQALAQQYQADIVIASDGINSRVRGRYAETFQPDIDTRLCRFVWLGTRKLFDAFTFAFEKTEHGWFQAHAYRFDDNTSTFIVEAPEPVWRAAGIEQMSQEEGVAFCERLFARYLDGNALISNATHLRGSANWIRFPRVICGTWVHWNTLAGGRRVPVVLMGDAAHTAHFSIGSGTKLALEDAIDLANNIRDAGPGGADVLPQALERYQATRSVEVLKIQNAARNSTEWFENVERYAGLAPEQFAYSLLTRSQRISHENLRVRDPGYVEGFERWLAGQAAANVANATSTGQAAEARVVPPMFTPLRVRNVTLKNRVVVSPMAMYSCEDGVPGDFHLTHLGARALGGAGMVVAEMTCTSPDARITPGCPGLWNDEQRDAWRRIVDFVHANSDARIAMQIGHAGRKGSTQLGWEQMDYPLAEGNWPVLSASPLPYMPGISQVPREMSRADMDAVRDDFVASARRAAQAGFDWLELHCAHGYLLSSFISPLTNQRSDEYGGSLQARLRYPLEVFAAVRAVWPQERPMSVRISAHDWVEGGITPDDAIEIARAFKAAGADMIDCSSGQVSPDQAPVYGRMYQTPFADRIRNEAGIATIAVGAIFEADHVDSIIAAGRADLCAIARPHLADPAWTIHEAARLGHRDLAWPKQYQAGKRQLETNLERAAQQARQARQPEDH catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS17300,RR42_RS17300,RR42_RS17300,WP_043349422.1,tr|A0A0C4Y642|A0A0C4Y642_9BURK,"D-lactate oxidase, FAD-linked subunit (EC 1.1.3.15)",Specifically important for: Sodium D-Lactate. KEGG has the correct EC # but describes it as glycolate oxidase; SEED has it as 1.1.99.14 (glycolate dehydrogenase),glycolate oxidase,"Glycolate dehydrogenase (EC 1.1.99.14), subunit GlcD",glycolate oxidase,MNAPHEVSLVADDARRSALLAGLAKILPDAALLWKPEDTVPYECDGLAAYRQVPMAVALPDNEDQVCAILRLCHSLQVPVVPRGAGTSLSGGAMPIATGLVLSLAKFKRIVSVDVRSRTAVVQPGVRNLAISEAAAQYNLYYAPDPSSQIACTIGGNVSENSGGVHCLKYGLTVHNVLRVRAVTMEGDVVEFGSEAPDAPGLDLLAAVIGSEGMLAVVTEVSVKLIPKPQLAQVIMASFDDVAKGGNAVADVIGAGIIPAGLEMMDKPATAAVEEFVRAGYDLDAAAILLCESDGTPEEVAEEVERMSEVLRASGASRIQVSQSEPERLRFWSGRKNAFPAAGRISPDYYCMDGTIPRKHIGTLLKRIEEMERKYGLRCMNVFHAGDGNMHPLILFDGADQDEWHRAELFGSDILESCVELGGTVTGEHGVGVEKLNSMCVQFSAQERDLFFGVKAAFDPARLLNPDKAIPTLARCAEYGRMHVKRGLLPHPDLPRF catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS17310,RR42_RS17310,RR42_RS17310,WP_043349427.1,tr|A0A0C4YFX9|A0A0C4YFX9_9BURK,"D-lactate oxidase, FAD binding subunit (EC 1.1.3.15)",Important on D-lactate; KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase),glycolate oxidase,"Glycolate dehydrogenase (EC 1.1.99.14), FAD-binding subunit GlcE",glycolate oxidase FAD binding subunit,MPATPADQQATLTAFRDAIRHATGTRTPLRLRGGGSKDFYGQHPQGTLLDTRAYSGIVDYDPPELVITARCGTPLAQIEAALAERRQMLAFEPPHFSTGADGSDVATIGGAVAAGLSGPRRQAVGALRDFVLGTRVMDGRGDVLSFGGQVMKNVAGYDVSRLMSGSLGTLGLILEVSLKVLPVPFDDATLRFALDEAAALDRLNDWGGQPLPIAASAWHDGVLHLRLSGAAAALRAARARLGGEAVDAAQADALWRALREHSHAFFAPVQAGRALWRIAVPTTAAPLALPGGQLIEWGGGQRWWLGGSDSAADSAIVRAAAKAAGGHATLFRNGDKAVGVFTPLSAPVAAIHQRLKATFDPAGIFNPQRMYAGL catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS17315,RR42_RS17315,RR42_RS17315,WP_043349430.1,tr|A0A0C4YDN4|A0A0C4YDN4_9BURK,"D-lactate oxidase, iron-sulfur subunit (EC 1.1.3.15)",Important on D-lactate; KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase),glycolate oxidase,"Glycolate dehydrogenase (EC 1.1.99.14), iron-sulfur subunit GlcF",glycolate oxidase iron-sulfur subunit,MQTTLAEFLRDTPDGEEAKSIVGKCVHCGFCTATCPTYQLLGDELDGPRGRIYLMKQVLEGQPVTQSTRLHLDRCLTCRNCESTCPSGVKYGRLVDIGRKVVDDRLEAQGIQRPARERFARWALRETMTRPALFGTAMRMGQRVRPLLPQALRNKVPQAVDAGAWPRTTHARKMLLLDGCVQPSMSPNINAATARVFDRLGVQLVMAREAGCCGAVRYHTGDHDGGLDNMRRNIDAWWPAVQAGAEAIVMTASGCGVMVKEYGHLLRNDAHYADRARQISALTKDLSELLPNFADALQDAAAEAGSSKGTNGTDGQRVAYHPPCTLQHGQQIRGKVEALLTGLGVDVKLCADSHLCCGSAGTYSVLQPALSYRLRDEKLANLQALKPEAIVSANIGCITHLQSGTGTPVMHWIELVDRMLG catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS25930,RR42_RS25930,RR42_RS25930,WP_043354222.1,tr|A0A0C4YL05|A0A0C4YL05_9BURK,glycine dehydrogenase (deaminating) (EC 1.4.1.10),"Specifically important for: L-Threonine. Annotated as D-amino acid dehydrogenase; SEED has it as the small subunit but do not see why another subunit is expected (i.e. E. coli dadA). Important on Thr as well as Gly because Thr is cleaved (via the threonine dehydrogenase and kbl) to glycine. The electron acceptor is uncertain -- the given EC number implies that NAD is the electron acceptor, which is consistent with an N-terminal NAD binding domain (PF13450).",amino acid dehydrogenase,D-amino acid dehydrogenase small subunit (EC 1.4.99.1),D-amino-acid dehydrogenase,MKTIAVIGGGITGVTTAYALAKRGFSVTLLEKHRYAAMETSFANGGQLSASNAEVWTHWSTILKGIKWMLKSDAPLLVNPRPSWHKLSWFAEFIAAIPHYRKNTIETTRLAIAARDHLFSWAAAEGIDFDLKKEGILHIYRDKAGFEHAGRVSKLLAEGGLARHAVTPEEMRAIEPTLAGQYYGGYFTQSDSTGDIHKFTSGMAAAIDRLGVRCLYNQDVQSVSTDGRQVTIVSGDGRQAESRVFDGVVVCAGTASRALAASLGDRVNIYPVKGYSITVNLNDAQSQAAAPVVSLLDDETKLVTSRLGVDRFRVAGTAEFNGYNRDIRADRIRPLVEWVNQCFPGVSTRSVVPWAGLRPMMPTMLPRVGRGRASCVFYNTGHGHLGWTLSAVTADMIGDVVQQAMGARRAHGVQEPVALVTP catabolism,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS28300,RR42_RS28300,RR42_RS28300,WP_043354857.1,tr|A0A0C4YKU6|A0A0C4YKU6_9BURK,L-threonine 3-dehydrogenase (EC 1.1.1.103),Specifically important for: L-Threonine. The first step in Thr catabolism (SEED_correct),NAD-dependent epimerase,L-threonine 3-dehydrogenase (EC 1.1.1.103),,MTKPARVLIIGANGQLGTELASALADRYGKQNVVTSDMVPHGRHLHLTHEMLDVTDRAHLRDVIERHGITQIYHLAAALSATGEKSPTWAWQLNMNGLLNVLEAARHQKLDKVFWPSSIAAFGPTTPPDGTPQSTIMEPKTVYGISKLAGEGWCRWYFENHGVDVRSLRYPGLISYKTPPGGGTTDYAIDIFHSALRAQPYACFLEKDEALPMMYMPDAVRATMELMEAPRASISERGSYNLAGLSFTPGEIANEIRRHCPGFDVRYEPDFRQEIAAGWPDSIDDSVARRDWNWRPEFGLKEMVADMLKNLATLNQGSALECVS transporters,Cupriavidus basilensis 4G11,Cup4G11,RR42_RS28835,RR42_RS28835,RR42_RS28835,WP_043355010.1,tr|A0A0C4YJP4|A0A0C4YJP4_9BURK,gluconate:H+ symporter (gntT),"67% identical to the gluconate permease (gntT, ACIAD0544) of A. baylayi ADP1 (PMCid:PMC4254613). KEGG_correct",permease DsdX,Gluconate transporter family protein,"gluconate:H+ symporter, GntP family",MGAVTGTTLLLYALIAVIALVVLIAKFKLNPFITLVVVSVLLGFAVGMPMGDIVKSFEAGVGGTLGHIALVVGLGTMLGKMMAESGGAERIARTLIDAFGEKNVHWAMVTIAFIVGLPVFFEVGFVLLVPIAFNVAKRTGTSMVLVGIPMVAGLSVVHGLIPPHPAALLAVTAYKADIGKTILYALIVGIPTAAIAGPLFAKLMTRYVTLPDVNPLAAQFTEEDEGVKASHELPGFGITLFTILLPVILMLIGSWADLITTPKTFANDFLKLIGNSVIALLIAALVSFYTFGKRRGFTRENILRFTNECVAPTAIITLVVGAGGGFGRVLRDSGISNAIVDVATGAHVSVLLLGWLVAVLIRIATGSATVAMTTAAGIVAPIAASVPGTRPELLVLTTGAGSLILSHVNDGGFWLVKEYFNMTVAQTFKTWSVCETLISVIALLLTLALATVV transporters,Dinoroseobacter shibae DFL-12,Dino,3607124,Dshi_0546,DSHI_RS02765,WP_012177224.1,tr|A8LPG9|A8LPG9_DINSH,"ABC transporter for Xylitol, ATPase component",Specific phenotype on Xylitol.,ABC transporter related (RefSeq),"Various polyols ABC transporter, ATP-binding component",multiple sugar transport system ATP-binding protein,MAGIKIDKINKFYGTTQALFDINLDIEDGEFVVFVGPSGCGKSTLLRTLAGLEGVSSGRIEIGGRDVTTVEPADRDLAMVFQSYALYPHMTVRENMEFGMKVNGFEPDLRKERIAEAARVLQLEDYLDRKPGQLSGGQRQRVAIGRAIVKNPSVFLFDEPLSNLDAKLRVQMRVELEGLHKQLGATMIYVTHDQVEAMTMADKIVVLNRGRIEQVGSPMDLYHKPNSRFVAEFIGSPAMNVFSSDVGLQDISLDASAAFVGCRPEHIEIVPDGDGHIAATVHVKERLGGESLLYLGLKGGGQIVARVGGDDETKVGAAVSLRFSRHRLHQFDEAGRAI transporters,Dinoroseobacter shibae DFL-12,Dino,3607125,Dshi_0547,DSHI_RS02770,WP_012177225.1,tr|A8LPH0|A8LPH0_DINSH,"ABC transporter for Xylitol, periplasmic substrate-binding component",Specific phenotype on Xylitol.,extracellular solute-binding protein family 1 (RefSeq),"Various polyols ABC transporter, periplasmic substrate-binding protein",multiple sugar transport system substrate-binding protein,MKTLRNLLCSAAVASVMASGAMADGHGWSLKDAAAPYAGTTVDVVFLLRPGYEAIEAMLPAFEEETGIKVNVIKHPYENALGEQVRDFVAGGDLDVALIDLVWIGSFAENEWILPLADVQAKFPDAVDPDLDIDDFFPLVLNAFGGWNDTIYGLPFDNYSGLMFYNRCMLEEAGFDGPPSTWEELKDVYGPALTKDGKYAFALQSKRNETQSADSFARMLWPFGGSFLNEEFRSNLMSEGSQAGLKFRQELMEYMPDGIVAYDHAETVNAFSQGDVAMITEWSAFYSSVVSPETSRVADCVEIAPEPMGPAGRKPALGGFSLAVASQADEAEQAAAYLFIQWATSKANAREYLERGGVPARQSVYQQDGLEEFKFVPALVESWQDGVPEFRPRFAEWPEITEIVQEWGTKMMLGEVTTEEGAQEIGTRMEEVLDAAGYYSGEKPLAQ transporters,Dinoroseobacter shibae DFL-12,Dino,3607126,Dshi_0548,DSHI_RS02775,WP_012177226.1,tr|A8LPH1|A8LPH1_DINSH,"ABC transporter for Xylitol, permease component 1",Specific phenotypes on Xylitol; Xylitol. Phenotype is very specific,binding-protein-dependent transport systems inner membrane component (RefSeq),"Various polyols ABC transporter, permease component 1",multiple sugar transport system permease protein,MSGQVPRKTVFAFIGPAVIGLALVGIAPLLYALWTSLHFYNLTKLRRVEFIGLENYWTVLTDEVFWQAMGRTFFLLGTALPLQIALGLGIALVLHQPGLTLVKTLARLSLVLPMATTYAVVGLLGQVMFNQKFGVVNQLLGGADINWIGDPENAFAMIIFWDVWQWTPFVALVLLAGLTMVPGEVEEAARLETKSKWTVLRYVQLPFLLPGLVAVLILRTADTLKLFDMVFTLTRGGPGSSTEFISLMIQRVGFRGFDQGLASAQAIILLIITIVLAQIYIRVFYKEV transporters,Dinoroseobacter shibae DFL-12,Dino,3607127,Dshi_0549,DSHI_RS02780,WP_012177227.1,tr|A8LPH2|A8LPH2_DINSH,"ABC transporter for Xylitol, permease component 2",Specific phenotypes on Xylitol; Xylitol. Phenotype is very specific,binding-protein-dependent transport systems inner membrane component (RefSeq),"Various polyols ABC transporter, permease component 2",multiple sugar transport system permease protein,MTSTRSLFSQIALLVLIITVCVFPFYWMVTTSLKTQIVALEAPPVWIFEPTLSNYREALFEDGVLRTLINSLIIAISTTFLALVLGVPAAFALARFEFRGKKDLWFWFITNRMISPIVLALPFFLIARNLGLLDKHITLILIYLTFNLPIVIWIVTDQFRGIPYDLDEAARLEGASQFTIMRKICLPLAMPGVAVSAIFSFIFSWNELMFGLILTRSEAKTAPAMAVSFMEGYNLPYGKIMATSTLIVIPVLIFALIASKQLVRGLTMGAVK catabolism,Dinoroseobacter shibae DFL-12,Dino,3609040,Dshi_2429,DSHI_RS12360,WP_012179094.1,tr|A8LS70|A8LS70_DINSH,Rhamnulokinase RhaK in alpha-proteobacteria (EC 2.7.1.5),Specifically important for: L-Rhamnose monohydrate. The second step in rhamnose catabolism (SEED_correct),carbohydrate kinase FGGY (RefSeq),Rhamnulokinase RhaK in alpha-proteobacteria (EC 2.7.1.5),,MTRHVAVIDIGKTNAKLALVDRQSLTEISVITRPNTVLPGPPWPHFDVDGHWAFLLDGLRDFQARHGIDAISITTHGACAALLQKDGALAAPILDYEHPGPDDTALAYDALRPPFSETGSPRLAGGLNIGAQLFWQFHTDPALRDRTRQIVTYPQFWGAKLTGVTATDVTSLGCHTDLWNPHAGAVSSLVDRLGLTGKLAPVRKPHDILGPVLPAVAAQTGLAPGTPVHCGIHDSNASLLPYVLTQTSPFSVISTGTWVVAMSVGGRPVVLDPDLDTLINVTALGQPAPSARFMGGREHDLATEGTGPPPSAQDLDHILSRQILLLPAVVPDTGPFKGQASRWEGDAPAPGTGARGAAVALYLALVTAECLSNIGHAGKIIVEGPFAGNRLYLEMLSVAMEADVVRASGTTGTSAGAALLAGGDAGAPPRPDSLVALPAASKTRLRHYAAHWRQRARRRTP catabolism,Dinoroseobacter shibae DFL-12,Dino,3609047,Dshi_2436,DSHI_RS12395,WP_012179101.1,tr|A8LS77|A8LS77_DINSH,rhamnulose-1-phosphate aldolase (EC 4.1.2.19) / lactaldehyde dehydrogenase (EC 1.2.1.22),Specifically important for: L-Rhamnose monohydrate. Part of rhamnose catabolism via rhamnulose. TIGR annotates the C terminal part as alcohol dehydrogenase rather than as lactaldehyde dehydrogenase but do not see why (SEED_correct),rhamnulose-1-phosphate aldolase/alcohol dehydrogenase (RefSeq),Predicted rhamnulose-1-phosphate aldolase (EC 4.1.2.19) / Predicted lactaldehyde dehydrogenase (EC 1.2.1.22),,MLKSLETQLLESRWDDEVAKGMSESELLLYRSNILGADKRVTNYGGGNTSAKVMEADPLTGAQVEVLWVKGSGGDIGSIKMDGFATLYMDKLRALKGLYRGVAFEDEMVSYLPHCTFRLNPRAASIDTPLHAYVPRKHVDHVHADAIIAIAASDNSKELTQEIFGNRIGWLPWKRPGFELGLWLEKFCRENPEADGVVLESHGLFTWADTAKECYDQTIDVINVATRWLAERSAGVPAFGGAIHESLPAAARREVAARLMPAIRGFVSDNQHMVGHFNDSDAVLEFVNARDMEALAALGTSCPDHFLRTKIRPLVVPFDPAQNNILAVLSELPDQVAAYREAYAAYYARCKHDDSPALRDPNAVVYLVPGVGMITFAKDKATARISGEFYVNAINVMRGASAVSTYQGLPEQEAFDIEYWLLEEAKLQRMPKPKSLAGRVALVTGGAGGIGAATAERFLAEGACVVLADINEDSLASTQERLSERFGADVVRSVVMNVTREEAVAAAFAEASVEFGGVDILVSNAGIASSAPIEETSLALWNKNMDILSTGYFLVSRAAFKLMRVQDMGGAVVFVASKNGLAASPNAAAYCTAKASEIHLARCLALEGAEAGIRVNVVNPDAVLRGSKIWEGDWLEQRAGTYGTDKDGLEEMYRQRSLLKRSVLPEDIAEACYFFAADASSKSTGNIINVDAGNVQAFTR transporters,Dinoroseobacter shibae DFL-12,Dino,3609738,Dshi_3121,DSHI_RS15825,WP_012179782.1,tr|A8LLA5|A8LLA5_DINSH,Tricarboxylate transport membrane protein TctA,specific phenotype on citrate and cofit with other nearby components; SEED_correct,protein of unknown function DUF112 transmembrane (RefSeq),Tricarboxylate transport membrane protein TctA,putative tricarboxylic transport membrane protein,MLEGLLIGLQTAFSIQNLAMVIGGCLIGTFIGMLPGLGPMSIIAIMIPVAISLGDPSAALILLAGVYYGAIFGGSTSSILLNAPGVAGTVATSFDGYPMAQQGKAGKALTIAAIASFAGGTIGAILLMVFAPALSSVALLFHSAEYFALMVVGLSAIAAFAGTGQVAKALLMTILGLIMATVGEGALFASPRFTMGLMDLQSGFGFITLAMAMFALPEALFLVMNPLRAASGQGGGEIKDLRITRAEARSIAPVIGRQSVQGFFIGVLPGAGATIASFLGYAVERNIASKDEQAEFGKGSVKGLAAPETANNAACTGSFVPLLTLGIPGSGTTAILLGALLALNVSPGPRLMIDAPEIFWAVIMSMFIGNLVLLILNLPLIPYIAKILSVPRNYLIPFILFFTLMGAYIGQNNATELLLLVGFGICATILKFADYPLAPLLIGFILGGLLENNFSRAMQLYDGISFIWERPMTLGLLVIAALLIILPSYRNRRAKARAAGVADGD transporters,Dinoroseobacter shibae DFL-12,Dino,3609739,Dshi_3122,DSHI_RS15830,WP_012179783.1,tr|A8LLA6|A8LLA6_DINSH,Tricarboxylate transport protein TctB,specific phenotype on citrate and cofit with other nearby components; SEED_correct,hypothetical protein (RefSeq),Tricarboxylate transport protein TctB,putative tricarboxylic transport membrane protein,MALDRWIALVLLGVCLIYGYTAWFTMDADLAPFMRRNPIWPSTFPKVLSVLGAVAALVILLGLEGPQKPPKAGDIDYRRLGDYKIGQAALLLGLMVGYALLLRPAGFLVSTTSFLILGSVILGERNWPVMIGVAVVATGAIWYLVQEVLGIFLRPLPMFMGV transporters,Dinoroseobacter shibae DFL-12,Dino,3609740,Dshi_3123,DSHI_RS15835,WP_012179784.1,tr|A8LLA7|A8LLA7_DINSH,Tricarboxylate transport protein TctC,specific phenotype on citrate and cofit with other nearby components; SEED_correct,hypothetical protein (RefSeq),Tricarboxylate transport protein TctC,putative tricarboxylic transport membrane protein,MTLEFTRRTLIAAAAALAMTGGAHAEGEQMLESIHFLIPGGAGGGWDGTARGTGEALTKAGLVGSASYENMSGGGGGKAIAYLIENANSSHGTLMVNSTPIVIRSLTGEISQSFRDLTLVAGTIGDYAAIVVGKDSPINSMADLIAAYDADPNATAVGGGSVPGGMDHLVAAMVMEAAGKDALGVKYIPYDAGGKAMAALLSGEIAALSTGFSEAIDLAEAGEVKIIGVTAPERVAAYDSAPTMVEQGIDTTFVNWRGFFAAPGLPEEQLAAYQATLEKMYDTPEWEEVRARNGWVNIHNSGADFQSFLEAQEAQIGDLMKKLGFL transporters,Escherichia coli BW25113,Keio,17022,b2943,BW25113_RS15285,WP_001112301.1,sp|P0AEP1|GALP_ECOLI,galactose:H+ symporter (galP),"A characterized E. coli protein, see EcoCyc. Not sure why SEED has it as transporting arabinose. KEGG_correct.",D-galactose transporter (NCBI),Arabinose-proton symporter,"MFS transporter, SP family, galactose:H+ symporter",MPDAKKQGRSNKAMTFFVCFLAALAGLLFGLDIGVIAGALPFIADEFQITSHTQEWVVSSMMFGAAVGAVGSGWLSFKLGRKKSLMIGAILFVAGSLFSAAAPNVEVLILSRVLLGLAVGVASYTAPLYLSEIAPEKIRGSMISMYQLMITIGILGAYLSDTAFSYTGAWRWMLGVIIIPAILLLIGVFFLPDSPRWFAAKRRFVDAERVLLRLRDTSAEAKRELDEIRESLQVKQSGWALFKENSNFRRAVFLGVLLQVMQQFTGMNVIMYYAPKIFELAGYTNTTEQMWGTVIVGLTNVLATFIAIGLVDRWGRKPTLTLGFLVMAAGMGVLGTMMHIGIHSPSAQYFAIAMLLMFIVGFAMSAGPLIWVLCSEIQPLKGRDFGITCSTATNWIANMIVGATFLTMLNTLGNANTFWVYAALNVLFILLTLWLVPETKHVSLEHIERNLMKGRKLREIGAHD transporters,Escherichia coli BW25113,Keio,17664,b3603,BW25113_RS18715,WP_001295233.1,sp|P33231|LLDP_ECOLI,(R)-lactate / (S)-lactate / glycolate:H+ symporter LldP,"This protein is annotated by EcoCyc as transporting both stereoisomers of lactate and glycolate, which is consistent with our genetic data and a more detailed study (PMID:11785976). The previous study found that yghK (glcA) also transported glycolate and was important for glycolyate utilization. We instead observed a very subtle defect of the glcA mutant during growth on glycolate (fitness = -0.5).",L-lactate permease (NCBI),L-lactate permease,L-lactate permease,MNLWQQNYDPAGNIWLSSLIASLPILFFFFALIKLKLKGYVAASWTVAIALAVALLFYKMPVANALASVVYGFFYGLWPIAWIIIAAVFVYKISVKTGQFDIIRSSILSITPDQRLQMLIVGFCFGAFLEGAAGFGAPVAITAALLVGLGFKPLYAAGLCLIVNTAPVAFGAMGIPILVAGQVTGIDSFEIGQMVGRQLPFMTIIVLFWIMAIMDGWRGIKETWPAVVVAGGSFAIAQYLSSNFIGPELPDIISSLVSLLCLTLFLKRWQPVRVFRFGDLGASQVDMTLAHTGYTAGQVLRAWTPFLFLTATVTLWSIPPFKALFASGGALYEWVINIPVPYLDKLVARMPPVVSEATAYAAVFKFDWFSATGTAILFAALLSIVWLKMKPSDAISTFGSTLKELALPIYSIGMVLAFAFISNYSGLSSTLALALAHTGHAFTFFSPFLGWLGVFLTGSDTSSNALFAALQATAAQQIGVSDLLLVAANTTGGVTGKMISPQSIAIACAAVGLVGKESDLFRFTVKHSLIFTCIVGVITTLQAYVLTWMIP transporters,Escherichia coli BW25113,Keio,17808,b3748,BW25113_RS19480,WP_001301979.1,sp|P04982|RBSD_ECOLI,D-ribose pyranase [EC:5.4.99.62],"This is a ribose pyranase, not a transporter, see PMID:15060078 or UniProt:RBSD_ECOLI (KEGG_correct)",D-ribose high-affinity transport system; membrane-associated protein (VIMSS),"Ribose ABC transport system, high affinity permease RbsD (TC 3.A.1.2.1)",D-ribose pyranase,MKKGTVLNSDISSVISRLGHTDTLVVCDAGLPIPKSTTRIDMALTQGVPSFMQVLGVVTNEMQVEAAIIAEEIKHHNPQLHETLLTHLEQLQKHQGNTIEIRYTTHEQFKQQTAESQAVIRSGECSPYANIILCAGVTF transporters,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1577,Ga0059261_1577,SKO01S_RS16465,WP_066580458.1,,L-glutamine and L-histidine transporter,"Specific phenotype on glutamine; also important for histidine utilization; detrimental to fitness on some other amino acids (proline, alanine) which may indicate that it likes these amino acids","amino acid/polyamine/organocation transporter, APC superfamily (TC 2.A.3)",amino acid transporter,"basic amino acid/polyamine antiporter, APA family",MAGGLFRTKRVKDAAEQAPEHRLAATLSWPHLVALGVGAIVGTGILTLIGVGAGKAGPAVIMSFVIAGAICACAALAYAEMATMMPASGSAYAYSYAVLGEIIAWVVGWSLILEYSLVVSTVAVGWSGYAAPLLHAWTGMPLELMAGPHANGIVNLPAIFIIAVVAGLLCLGTKESATLNAALVVVKIIALAVFVAVALPYFNGANLEPFAPFGFAKTISPDGVERGVMAAAAIIFFAFYGFDAISTAAEETKNPGRDLAIGIVGSMIACVAIYMLVAVAAVGATPFTHFANSPEPLALILRDLGRPGFATFLAVSAIIALPTVLLGFLFGQSRIFFTMARDGMLPIGLAKVSKRGSPVRITLFTAAIVAVIAGLLPIDEIAALANAGTLAAFTAVAVCMMVLRVRAPDMPRMFRTPLWWLVGAIAVLGCIYLFFSLPVKTQLWFLAWNALGVVIYFAYARPRVSAKGIE catabolism,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1581,Ga0059261_1581,SKO01S_RS16485,WP_066580460.1,,Maltose or maltodextrin glucosidase (EC 3.2.1.20),"Specifically important for: D-Maltose monohydrate; a-Cyclodextrin. The first step in catabolism of maltose or longer maltodextrin polymers. The phenotype on maltose implies that maltose is also a substrate, which was ambiguous with the original SEED annotation (SEED_correct)",Glycosidases,Maltodextrin glucosidase (EC 3.2.1.20),,MRSLAFALTLLAGTVASAQTAAPAELRARAAETEVLYFLLPDRFENGDPSNDRGGLRGDRLTTGFDPAHKGFFHGGDLKGLIRRLDYIQSLGASAIWLGPIYKNKPVQGGPGQETAGYHGYWITDFTRVDPHFGTNDEMKAFVDAAHARGIKVYLDIITNHTADVIQYRECPKSACDYRSRADYPYQRKGGVKGAAINPGFAGDGVQTPENFAKLSDPNYAYTTVVPEAERTVKVPAWLNDPIHYHNRGNTTFRNESSTMGDFVGLDDLMTESPRVLQGFIDIYGAWIDQFGIDGFRIDTARHVNPEFWQGFSKAMIERAKARGIPNFHIFGEVANEGDVGDLALHTRVHQLPSVLDFGFRAAVQHTVAGEKGPDMLAAMFDRDALYEGGAEAALRLPTFISNHDHGRFSTDVRKAFPKASDDEVLARVKLAHAMLLLLRGVPTIYSGDEQGFVSDGNDQDAREDMFPSKVAIYNDNRLLGTDKTTADSNFDTGHPLYMEISKLSAIRKATPALSRGRQVTRAYDEKPGLFAVSRFDPASGAEVLVAFNTSAAPITRQVEVGVSSTAFAPLVGQCAASASAPGSVTVTLPAFGYVACVAAGGAK transporters,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1777,Ga0059261_1777,SKO01S_RS20290,WP_083954491.1,,"D-fructose transporter, sugar porter family","Specific phenotype on fructose and raffinose; during growth on raffinose, it is probably cleaved to sucrose in the periplasm (by Ga0059261_1166), so this probably is important on raffinose because of the fructose uptake","MFS transporter, sugar porter (SP) family",,,MHSVSASFAGAPDEEARATVAIILSAAGAALGGLLFGFDTAVISGATQALQLQFGLTDAMLGFTVASALIGTVLGSLIAGAPADRFGRKGVMLTVAIAYVVSSLGTGLAPDLNAFLVFRFMGGLAIGAASVVTPIYIAEVSPARFRGRLVAMNQLNIVLGILIAFLSNYIIAGLVQYDVAWRWMFGIVAVPSTIFLLVTLLLPESPRWLAIHGQADRARDVMQRLGFADPRAELARIELAEAREEAAGKPRLFQRSHFTPVACAIAIAMFNQLSGINALLYYAPRIFELAGAGADSALLQSIAVGGTNLVFTVAALFLIDRFGRRPLLFVGSVICAATLLLVGWQLESAKPDGTLILFGLLGFIAAFAMSQGAVIWVFISEVFPSAVRGKGQALGSTTHWVMAAAITWAFPVFAASVGGWVFAFFGAMMLLQLLWTWKFMPETNGIALEDMNLGSARA catabolism,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1778,Ga0059261_1778,SKO01S_RS20285,WP_066581977.1,,beta-fructosidase for D-raffinose catabolism (EC 3.2.1.26),"Specifically important for: D-Raffinose pentahydrate. Uncertain if it acts on raffinose or sucrose (raffinose could be cleaved to galactose + sucrose first). Alternatively, SEED suggests that it hydrolyzes sucrose-6-phosphate, but the basis for this is unclear.",Beta-fructosidases (levanase/invertase),Sucrose-6-phosphate hydrolase (EC 3.2.1.B3),,MKKLAILLTAAAVLASAASAQQADVRPGYHYGPARNWMNDPNGLVYYDGEYHLFYQYNPHGDRWGHMNWGHAVSRDLVNWEELPVAIPETDVMAFSGTAVIDWNNTTGFGKNGKPPMIAIYTGHDPKTERQSQYLAYSNDRGRTFTIHGEVLNAGNEKHFRDPKVFWHAPTRRWVMVVLKAAANTAEIYTSPNLKDWTHRSSFGPAGGRGKFWECPDLFELPVEGGAPGETRWVLSINLGDNAIGGGSGGQYFVGDFDGEKFTLVPGWPAAPQWMDYGADFYATISWNDMPKGDPRRVWMGWANDWRYAEAIPTWPARGIMTVARTVALRKTAEGYHLLQAPVRELATLRGTPQRAPALALSETPVPLPIEGGKADIELELDTGTADQVSIALTDGQGWQTRIGVNPTVNEVFVDRTRSGPHFHDGFANRHVAPVDLKSRKVKLRVLADESIVEVFVNDGRQTITDRFYRGGGALTWSATARGGKATMNLTAWPMRSMESRK catabolism,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1893,Ga0059261_1893,SKO01S_RS12770,WP_066578889.1,,Xylonolactonase (EC 3.1.1.68),Specifically important for: D-Xylose. xylonolactone is an intermediate in the oxidation of xylose (SEED_correct),Gluconolactonase,Xylonolactonase (EC 3.1.1.68),,MGVVLTAPEPVWALGAPLLEGPVWVQRDAALWFVDIKSHRIHRFDPASGERRSWDAPAQVGFCLPAANGKFVAGLQTGLAIFDPADRSFTPLTDPEPALPGNRLNDGTVDPAGRLWFGTMDDGESEATGRIYRLGGDGRCVAETAAVSISNGPAVSPDGRTLYHVDTLGGVIHSAAIGDDGILGDSRVFATIPNSEGFPDGPAVDAEGCVWIGLYNGAAVRRYSPAGELLDVVAFPVGAITKVAFGGPDLRTVYATTASKHLDADGRAEEPHAGDLFAFRVSVPGMPGTEVSVGL catabolism,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1894,Ga0059261_1894,SKO01S_RS12775,WP_066578892.1,,D-xylose 1-dehydrogenase (EC 1.1.1.175),Specifically important for: D-Xylose. the first step in xylose oxidation (SEED_correct),Dehydrogenases with different specificities (related to short-chain alcohol dehydrogenases),D-xylose 1-dehydrogenase (EC 1.1.1.175),,MTQPAAASAAAVYPSLKGKRVLVTGGGSGIGAGIVEGFARQGADVTFFDIAGAESQLLVERLSADGHKACFERVDLTDVASLQAVIARLIKGAGGFDILVNNAANDDRHAIDEITEAYWDERLSVNLKHIFFCAQAVVPAMRARGGGAIVNLGSISWHLGLSDLVLYQTCKAAIEGLTRSLARDLGRDGIRATCVIPGNVRTPRQLKWYSPEGEAEIVAAQCLDGRLAPEDVAAMVLFLASDDARLVTGHSYFVDAGWR catabolism,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1895,Ga0059261_1895,SKO01S_RS12780,WP_083629275.1,,2-keto-3-deoxyxylonate dehydratase,"# Specifically important in carbon source D-Xylose. 31% identical to HVO_B0027, which is 2-keto-3-deoxyxylonate dehydratase (see PMC2785657). Ga0059261_1895 is also 61% identical to xylX from Caulobacter crescentus, which is also required for xylose utilization (PMC1855722) and is believed to have this activity as well (ibid.).",Fumarylacetoacetate (FAA) hydrolase family protein,Fumarylacetoacetate hydrolase family protein,,VPEAFDVSALPEDAGQATLAGRVMLAEGPVPVIVREGRVHDVSTVAATTADLFDLPDPASAAGKYLCDVTDLTIGGGAYALLAPIDLQCVKACGVTFAVSALERVIEERARGDAGQAAAIRAELEEKIGGGIRSVVPGSADAAKLKAVLIDAGMWSQYLEVAIGPDAEVFTKAPVLSSVGWGAEIGIRSDSSWNNPEPEVVVIANAAGKVIGATLGNDVNLRDFEGRSALLLGKAKDNNAACAIGPFVRLFDAQFGIDDVRNAELDLLIEGPEGYRLEGHSSMNQISRDPLELVRQTLSEHQYPDGFALFLGTLFAPVQDRDDPGRGFTHKIGDTVRIRSRKLGTLTNRVTTSKDAAPWTFGIRELYRSIAARTAA catabolism,Sphingomonas koreensis DSMZ 15582,Korea,Ga0059261_1896,Ga0059261_1896,SKO01S_RS12785,WP_066578896.1,,Ketoglutarate semialdehyde dehydrogenase (EC 1.2.1.26),Specifically important for: D-Xylose. The KEGG annotation is vague; xylose is oxidized to alpha-ketoglutarate semialdehyde (SEED_correct),NAD-dependent aldehyde dehydrogenases,Ketoglutarate semialdehyde dehydrogenase (EC 1.2.1.26),NADP-dependent aldehyde dehydrogenase,MELTGGLFIGGERRQSDARFHAYDPAAGADIADAGFASASEQDVADACAAAEAAFLPYSTKPLEARARFLETIADEIEALGDVLIERACRESGLPAARITGERGRTVGQLRLFAKEVRDGAWQKLRIDHADRERTPPRPDLRLRMVPLGPVAVFGASNFPLAFSTAGGDTASAFAAGCPVVVKGHRAHPGTAELIATAIIRAVETCGMPAGTFGMVNGTSRKVGETLVADPRIQAVGFTGSRGGGEALMRIAAARPRPIPVYAEMAAINPVILMPQALKARGPALAEAFVASLAMGAGQFCTNPGLVMGIDGPELDAFVARAGEVLSGQAAQVMLTDGIWEAFESGKAKLAGSAFVTKVAEGVEADGPNRGRAALFSVAGKDFLADPVHLHEVFGVSSVVVRCASLEELKAVLGELEGQLTATLQVDEGDYPEAQALLPVLERTVGRVIANGWPTGVEVTHAMVHGGPYPSTSDPRSTSVGTLAIDRFLRPVSYQDLPEALLPAALRENAQAGTVARIDGSWTI catabolism,Marinobacter adhaerens HP15,Marino,GFF2534,HP15_2478,HP15_RS12360,WP_041645374.1,tr|E4PHV4|E4PHV4_MARAH,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Tween 20. tween 20 hydrolyzes to a mix of C12, C14, and C16 fatty acids; this is probably part of beta oxidation (SEED_correct)",acyl-CoA dehydrogenase family protein,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MRFVLNEVFDAPALWASLPKVAEHVDPETADAILEEAGKISSGVLAPLNREGDEQGCKWNDGEVTSPEGFKEAYQTIVEGGWNGLGGNPDFGGMGMPKTLVAQFEEMMQGANMAFGLAPMLTAGACLALDAHGSDELKEKYLPNMYSGVWSGAMDLTEPHAGTDLGIIRTKAEPNDDGSFNVTGTKIFITWGEHDMAENIIHLVLAKLPDAPKGPKGISMFLVPKFLVNDDGSLGERNSFSCGSLEKKMGIKGSATCVMNFDGAKGWLVGEENKGLAAMFTMMNYERLGVGIQGIGAAEASLQSAREYALDRIQSRAPTGAQQPEKAADPILVHPDVRRMLLTMKSYVEGGRTFSTYVAQWLDIAKFAEDDERRKHAEGMVALLTPVAKAFLTDRGLDTCIMGQQVFGGHGFIREWGQEQLVRDCRITQIYEGTNGIQALDLMGRKVVGTQGKLYELFAQDVANFLEENSGNDHLRPFLEPLAAAVERLDDVTEHVIKQAGDNPNAIGAASVDYLDLFGLTALGYMWAKIVKAAAPQADSDTSGFYSGKLKTARFYFDRLLPKTVSLAEGIRSGSDSMMALTAEEF catabolism,Marinobacter adhaerens HP15,Marino,GFF2749,HP15_2693,HP15_RS13380,WP_014577963.1,tr|E4PKI1|E4PKI1_MARAH,3-hydroxyacyl-CoA dehydrogenase PaaC (EC 1.1.1.-),Specifically important for: L-Phenylalanine. Part of phenylalanine catabolism via phenylacetate (SEED_correct),3-hydroxybutyryl-CoA dehydrogenase,3-hydroxyacyl-CoA dehydrogenase PaaC (EC 1.1.1.-),3-hydroxybutyryl-CoA dehydrogenase,MPALDTQTKVAVVGAGAMGSGIAQVAAQAGHQVYLHDQREGAAEAGRDGIAKQLQRRVDKGKMQQQELDDVIGRIHPVAKLDDVADAGLVIEAIIEDLQIKRQLLASLEDLCTADAILATNTSSISVTALGADMSKPERLVGMHFFNPAPLMALVEVVMGLATSKTVADTVHATATAWGKKPVYATSTPGFIVNRVARPFYAESLRLLQEQATDAATLDAIIREAGQFRMGAFELTDLIGHDVNYAVTSSVFNSYYQDPRFLPSLIQKELVEAGRLGRKSGQGFYPYGESAEKPQPKTEPAHQSDESVIIAEGNPGVAAPLLERLKAAGLTIIERDGPGQIRFGDAVLALTDGRMATERAACEGVANLVLFDLAFDYSKASRLALAPADQASDAAVSCACALLQKAGIEVSLIADRPGLVIMRTVAMLANEAADAALHGVATVADIDLAMKAGLNYPDGPLSWSDRLGAGHVFKVLTNIQTSYAEDRYRPALLLRKNAFAQKGFYS catabolism,Marinobacter adhaerens HP15,Marino,GFF2751,HP15_2695,HP15_RS13390,WP_041646301.1,tr|E4PKI3|E4PKI3_MARAH,Beta-ketoadipyl CoA thiolase (EC 2.3.1.-),"Specifically important for: L-Phenylalanine. The second SEED annotation (beta-ketoadipyl-CoA thiolase) is part of phenylalanine catabolism via phenylacetate. Not clear if the acetyl-CoA acetyltransferase annotation is correct, so it was removed, but it remains plausible (SEED_correct)",acetyl-CoA acetyltransferase,Acetyl-CoA acetyltransferase (EC 2.3.1.9) @ Beta-ketoadipyl CoA thiolase (EC 2.3.1.-),,MSADNILKDAYIVDAIRTPIGRYGGALSAVRADDLGAIPIKALAERYPDLDWSKIDDVLYGCANQAGEDNRDVARMSLLLAGLPVDVPGSTINRLCGSGMDAVGSAARAIRTGETQLMIAGGVESMSRAPFVMGKADSAFSRKAEIFDTTIGWRFVNPVLKKQYGIDSMPETAENVAADFGISREDQDAFALRSQQRTAAAQKEGRLAAEITPVTIPRRKQDPLVVDTDEHPRETSLEKLASLPTPFRENGTVTAGNASGVNDGACALLLAGADALKQYNLKPRARVVAMATAGVEPRIMGFGPAPATRKVLATAGLELADMDVIELNEAFAAQALAVTRDLGLPDDAEHVNPNGGAIALGHPLGMSGARLVTTALNELERRHAAGQKARYALCTMCIGVGQGIALIIERMDAVA catabolism,Marinobacter adhaerens HP15,Marino,GFF3193,HP15_3135,HP15_RS15560,WP_014578345.1,tr|E4PPM8|E4PPM8_MARAH,ethanol oxidation regulatory protein ercA,Specifically important for: Ethanol; D-Trehalose dihydrate. Similar to ercA (PA1991) which apparently has a regulatory role (PMCID: PMC3754586) rather than being directly involved in catabolism. Also this gene is adjacent to an apparent ercS (HP15_3134). Do not see why SEED annotates it as aldehyde dehydrogenase. HP15_3164 is probably the ethanol dehydrogenase and HP15_3144 the acetaldehyde dehydrogenase.,iron-containing alcohol dehydrogenase,Alcohol dehydrogenase (EC 1.1.1.1); Acetaldehyde dehydrogenase (EC 1.2.1.10),,MSHDISSLRKFVSPEIVFGAGSRKSVANFASNFGARHVFLVSDPGVAAAGWVDEIVTLLTDAGIRSTVYTGVSPNPKVDEVMTGAELYKSNECDVIVAIGGGSPMDCAKGIGIVATHGRSILEFEGVDTITNPSPPLILIPTTAGTSADVSQFAIISDPNRRFKFSIISKAVVPDVSLIDPEVTETMDAYLTACTGVDALVHAIEAYVSTGSGPLTDTHALEAIRLINRNLEPLVDNTADPYLREQIMLASMQAGLAFSNAILGAVHAMSHSLGGFLDLPHGLCNALLLEHVVAYNFQSAEDRFRRVAEAMDIDTRGMAKPEIKKRLMNRIVELKRRVGLEARLAQLGVSVSDIPHLSGFALQDPCILTNPRKSSLRDVQVVYEEAL catabolism,Marinobacter adhaerens HP15,Marino,GFF879,HP15_858,HP15_RS04300,WP_014576347.1,tr|E4PR87|E4PR87_MARAH,phenylalanine aminotransferase (EC 2.6.1.57),"Specifically important for: L-Phenylalanine. The donor for the amine group is uncertain, but this gene is specifically involved in catabolism of phenylalanine via phenylpyruvate (the product of this enzyme)",aspartate aminotransferase,Aspartate aminotransferase (EC 2.6.1.1),aspartate aminotransferase,MFEALQPLPQDPILQLMQTFLEDDRPDKVDLGIGVYKDDAGNTPIMAAVHEAERRLLEGETTKSYVGPAGSAGFNSAMAELILGSNSPLVRDGRVSVIQTPGGCGALRMAAEFLRLCKADTKVWVSTPTWANHLPLLGGAGLTIREYPYLNPETLQVDFGAMLETLERADAGDVVLLHGCCHNPSGADLSLAQWQEVTSLIQRKGLLPFVDMAYQGLGEGLDADAAGLRHLASAVPEMLIAASCSKNFGLYRERTGALALVSETATVNAAATSQLLSVIRSHYSMPPAHGASIVETILGDDDLRAQWQQELNGMCKRILHLRHAFADALSPVGDFDFIARQRGMFSFLGISPEQVGRLRKEHGIHMLESSRVNVAGLNDHVLPQVASALREVLGK catabolism,Marinobacter adhaerens HP15,Marino,GFF880,HP15_859,HP15_RS04305,WP_014576348.1,tr|E4PR88|E4PR88_MARAH,phenylpyruvate ferredoxin oxidoreductase (EC 1.2.7.8),"Specifically important for: L-Phenylalanine. This is the second step in phenylalanine catabolism (to phenylacetyl-CoA). Alternatively, could be a decarboxylase (forming phenylacetaldehyde), but we did not identify a separate aldehyde dehydrogenase. Annotated as indolepyruvate ferredoxin oxidoreductase, which has the same EC number but is part of tryptophan catabolism..",pyruvate ferredoxin/flavodoxin oxidoreductase,"Indolepyruvate ferredoxin oxidoreductase, alpha and beta subunits",,MSADTPQLDDYKLEDRYLRESGRVFLTGTQALVRIPLMQAALDRKQGLNTAGLVSGYRGSPLGAVDQALWQAKDLLDENRIDFVPAINEDLAATILLGTQQVETDEDRQVEGVFGLWYGKGPGVDRAGDALKHGTTYGSSPHGGVLVVAGDDHGCVSSSMPHQSDVAFMSFFMPTINPANIAEYLEFGLWGYALSRYSGCWVGFKAISETVESAASVEIPPAPDFVTPDDFTAPESGLHYRWPDLPGPQLETRIEHKLAAVQAFARANRIDRCLFDNKEARFGIVTTGKGHLDLLEALDLLGIDEDKARDMGLDIYKVGMVWPLERRGILDFVHGKEEVLVIEEKRGIIESQIKEYMSEPDRPGEVLITGKQDELGRPLIPYVGELSPKLVAGFLAARLGRFFEVDFSERMAEISAMTTAQDPGGVKRMPYFCSGCPHNTSTKVPEGSKALAGIGCHFMASWMGRNTESLIQMGGEGVNWIGKSRYTGNPHVFQNLGEGTYFHSGSMAIRQAVAAGINITYKILFNDAVAMTGGQPVDGQITVDRIAQQMAAEGVNRVVVLSDEPEKYDGHHDLFPKDVTFHDRSELDQVQRELRDIPGCTVLIYDQTCAAEKRRRRKRKQFPDPAKRAFINHHVCEGCGDCSVQSNCLSVVPRKTELGRKRKIDQSSCNKDFSCVNGFCPSFVTIEGGQLRKSRGVDTGSVLTRKLADIPAPKLPEMTGSYDLLVGGVGGTGVVTVGQLITMAAHLESRGASVLDFMGFAQKGGTVLSYVRMAPSPDKLHQVRISNGQADAVIACDLVVASSQKALSVLRPNHTRIVANEAELPTADYVLFRDADMKADKRLGLLKNAVGEDHFDQLDANGIAEKLMGDTVFSNVMMLGFAWQKGLLPLSEAALMKAIELNGVAIDRNKEAFGWGRLSAVDPSAVTDLLDDSNAQVVEVKPEPTLDELINTRHKHLVNYQNQRWADQYRDAVAGVRKAEESLGETNLLLTRAVAQQLYRFMAYKDEYEVARLFAETDFMKEVNETFEGDFKVHFHLAPPLLSGETDAQGRPKKRRFGPWMFRAFRLLAKLRGLRGTAIDPFRYSADRKLDRAMLKDYQSLVDRIGRELNASNYETFLQLAELPADVRGYGPVREQAAESIREKQTQLIKALDTGRPTLIRTQQANEEANHV catabolism,Shewanella oneidensis MR-1,MR1,200453,SO1275,SO_1275,NP_716898.1,tr|Q8EHE8|Q8EHE8_SHEON,succinate-semialdehyde dehydrogenase (NADP+) [EC: 1.2.1.16],"Specifically important for: L-Isoleucine. Also very important with putrescine as the N source. The more specific KEGG annotation is consistent its role on N putrescine (which is converted to 4-aminobutyrate and then to succinate semialdehyde). Its role on isoleucine is unclear but a close homolog, Shewana3_3092, also has this phenotype (albeit milder). Also note that isoleucine is catabolized to propionyl-CoA and Shewana3_3092 is very important for propionate utilization. The annotation of son:SO_1275 in KEGG has been updated to list glutarate-semialdehyde as an additional substrate. (KEGG_correct)",succinate-semialdehyde dehydrogenase (NCBI ptt file),Aldehyde dehydrogenase B (EC 1.2.1.22),succinate-semialdehyde dehydrogenase (NADP+),MLLNDPSLLRQQCYINGQWCDANSKETVAITNPATGAVIACVPVMGQAETQAAIAAAEAALPAWRALTAKERGAKLRRWFELLNENSDDLALLMTSEQGKPLTEAKGEVTYAASFIEWFAEEAKRIYGDTIPGHQGDKRIMVIKQPVGVTAAITPWNFPAAMITRKAAPALAAGCTMVVKPAPQTPFTALALAVLAERAGIPAGVFSVITGDAIAIGNEMCTNPIVRKLSFTGSTNVGIKLMAQCAPTLKKLSLELGGNAPFIVFDDANIDAAVEGAMIAKYRNAGQTCVCANRIYVQAGVYDEFAEKLSMAVAKLKVGEGIIAGVTTGPLINAAAVEKVQSHLEDAIKKGATVLAGGKVHELGGNFFEPTVLTNADKSMRVAREETFGPLAPLFKFNDVDDVIKQANDTEFGLAAYFYGRDISLVWKVAESLEYGMVGVNTGLISTEVAPFGGMKSSGLGREGSKYGIEEYLEIKYICMSV catabolism,Shewanella oneidensis MR-1,MR1,201633,SO2492,SO_2492,NP_718079.1,tr|Q8EE95|Q8EE95_SHEON,acyl-CoA dehydrogenase (1.3.8.-),"Specifically important for: Tween 20. Tween 20 hydrolyzes to a mix of C12, C14, and C16 fatty acids; this is probably part of beta oxidation. Similar to fadE from E. coli. The specific SEED annotation as butyryl-CoA dehydrogenase seems questionable but could still be correct","oxidoreductase, acyl-CoA dehydrogenase family (NCBI ptt file)",Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MLTIIIIALIAIIALFAVKSLRMQFITQPVFHFFKKVLPPLSDTEREAMEAGDVWWEGELFRGNPNWNTLHSYGKPTLTAEEKDFIDNQVMTALTMIDDFDIVHNRKDLPPELWDYFKKEGFFALIIPKKFGGKAFSAYANSTIVSKLASRSVSAAVTVMVPNSLGPGELLTHYGTEEQKERWLPALAKGDEIPCFALTGPEAGSDAGAIPDVGIVCRGEFNGEEVLGLKLTWNKRYITLAPVATVLGLAFQMRDPDGLLGEKKNLGITCALIPTDHPGVVIGRRHNPLNMAFMNGTTQGDEVFIPLDWIIGGPEFAGRGWRMLVECLSAGRGISLPALATASGHMATKTTTAYSYVRHQFGMAIGQFEGVQEALARIIANTYQLEAARRLTTTGIDLKVKPSVVTAIAKFHMTELGRAVMNDAMDIQSGKGIQLGPKNYLGHPYMSNPISITVEGANILTRSLMIFGQGATRCHPYVLAEMEAAAMENQHDALTRFDSLLMGHMGYATRNAFSALFNALTASRFGNAPVSGETKQYYKDMSRLSSALAFMTDISMLIMGGDLKRKEMLSARMGDVLSQLYLGSATLKLFEDNGRQQDDLPAVRYVMANRLHLAAKALEDVIRNFPNRPVAWLLRALIFPIGNHFNAPSDKMATELVSGMLKPSPARERITFLCPEFEGDVGGIAEVEQAFVAQYACKEIYKKLKKAQRSGELPAKVPNLVLFAKALEQGTISNDEHQTLLHADKLRLAAINVNDFEAL transporters,Shewanella oneidensis MR-1,MR1,202564,SO3461,SO_3461,NP_719010.1,tr|Q8EBP8|Q8EBP8_SHEON,Fatty acid transporter,"Specific phenotype on Tween-20, which is hydrolyzed extracellularly to fatty acids (typically around 12 carbons). This protein is distantly related (PF03547) to auxin efflux transporters in plants and to the malate permease mleT of Lactobacillus casei (PMID: 23835171)","transporter, putative (NCBI ptt file)",TRANSPORTER,,MTILTPLFAVFGIMLLGTLVQKLRFLPVETDQVLNQYVYYIAFPAVLLIALAQQPIEEILQWRFIAGYSAAMLVIYLMCIGISLLVNPKQHAIAAVRALNATFGNTAFIGIPLLVIIFPEQQSALVAAIASLLSVLMFAVALVSLELTTNKHRQHHAVVIMCLAITKNPIVIGCFIGISISALGITLPSGVALMIQQIGNTSSPCALFAVGMVLAKAMRYQKDSKLFSLTNFVELCLINLFKLILQPALVFFMLKSIGVTGDYLVMGVILSALPTAASVYLLAQRYNTQASTCAQGILFGTIVTFFSLPILEQLVKTYS transporters,Shewanella oneidensis MR-1,MR1,202604,SO3503,SO_3503,NP_719050.1,tr|Q8EBL0|Q8EBL0_SHEON,"N-acetyl glucosamine transporter, NagP",Specific phenotype on NAG. Also see PMID:16857666 (SEED_correct),glucose/galactose transporter (NCBI ptt file),"N-acetyl glucosamine transporter, NagP",,MTLDKSQQKSSFLPMAIVAALFFILGFATWLNGSLMPYLKQILQLNPFQASLILFSFYIAVTFTALPSAWVIRKVGYKNGMALGMGVMMIAGLLFIPAAKTQVFALFLFAQLVMGAGQTLLQTAVNPYVVRLGPEESAAARVSVMGILNKGAGVIAPLVFSALILDSFKDRIGTTLTQVQIDEMANGLVLPYLGMAVFIGILALAVKKSPLPELSNEDEVADHTDKSQIKAALSHPNLALGVLALFVYVAVEVIAGDTIGTFALSLGIDHYGVMTSYTMVCMVLGYILGILLIPRVISQPTALMISAILGLLLTLGILFGDNNSYAIANLLLVPFGGVALPDTLLLIAFLGLANAIVWPAVWPLALSGMGKLTSTGSALLIMGIAGGAFGPVSWGLMSSATDMGQQGGYMVMLPCYLFILFYAVKGHKMRSWSAK catabolism,Shewanella oneidensis MR-1,MR1,202608,SO3507,SO_3507,NP_719054.1,tr|Q8EBK6|Q8EBK6_SHEON,N-acetylglucosamine kinase (EC 2.7.1.59),Specifically important for: N-Acetyl-D-Glucosamine. First step in NAG catabolism; (SEED_correct); see PMID 16857666,conserved hypothetical protein (NCBI ptt file),N-acetylglucosamine kinase of eukaryotic type (EC 2.7.1.59),,MGLVQTNDQQLFIGVDGGGSKCRATIYTADGTVLGTGVAGRANPLHGLAQTFESIEASAHQALLDAGMKATDSHLLVAGLGLAGVNVPRLYQDVVNWQHPFAAMYVTTDLHTACIGAHRGADGAVIITGTGSCGYAHVGDASLSIGGHGFALGDKGSGAWLGLKAAEHVLLALDGFATPTALTEMLLSHLGVKDALGIVEHLAGKSSSCYAQLARNVLDCANAGDQVAIAIVQEGADYISEMARKLFMLNPVRFSMIGGLAEPLQAWLGSDVVAKISETLAPPELGAMYYAQQQFNSATV transporters,Phaeobacter inhibens BS107,Phaeo,GFF1248,PGA1_c12640,PGA1_RS06295,WP_014874390.1,tr|I7DPJ2|I7DPJ2_PHAIB,"D-lactate transporter, ATP-binding component","Specific phenotype on D-lactate and D,L-lactate and cofit with other components",putative high-affinity branched-chain amino acid transport ATP-binding protein,InterPro IPR001687:IPR003439:IPR003593 COGs COG0411,,MGILEVKNVGKRFGGLQALSDVNLSVRENTVHAIIGPNGAGKSTLLNCLVGKLIPDTGSVMFDGKSVLGRAPYEINQMGISRVFQTPEIFGDLSVLENMMIPCFAKRDGAFEMNAISAVSGQRDILEKAEHMLEEMNMADKRHMNAASMSRGDKRRLEIGMCLSQEPRLLLLDEPTAGMARADTNNTIDLLKQIKSERDITIAIIEHDMHVVFSLADRITVLAQGTPLVEDDPQNIKGNPKVREAYLGESA transporters,Phaeobacter inhibens BS107,Phaeo,GFF1249,PGA1_c12650,PGA1_RS06300,WP_014874391.1,tr|I7EW25|I7EW25_PHAIB,"D-lactate transporter, permease component 1","Specific phenotype on D-lactate and D,L-lactate and cofit with other components",putative high-affinity branched-chain amino acid transport system permease protein,Branched-chain amino acid transport system permease protein LivM (TC 3.A.1.4.1),,MFTLNKKDKTLLLVVAILTLFAPFILNPFPTGSALAQFNAGYPDLMQRFVIFGIFAIGFNILFGLTGYLSFGHAAFLGVGSYSAVWMFKLLSMNVVPAIVLSVIVAGLFALVIGYVSLRRSGIYFSILTLAFAQMSFNLAYSVLTPITNGETGLQLTLDDPRVLGVSATADGSIPVTSLFGLEMRSTFEMVVGPWAFQFNAGYYLCALILLAAFYLSIRIFRSPFGLMLKAVKSNQQRMNYTGLNTRPYTLAAFVISGMYAGLAGGLMASMDPLAGAERMQWTASGEVVLMTILGGAGTLIGPVLGAGFIKYFENIFSKINDNVLHSWFSFMPDGIEDAMVFIVHPFIGKGWHLTLGILFMLVVIFLPGGLVEGGQKLRGWIQGRKAKKDGPSGKTEPAE transporters,Phaeobacter inhibens BS107,Phaeo,GFF1250,PGA1_c12660,PGA1_RS06305,WP_014874392.1,tr|I7EL66|I7EL66_PHAIB,"D-lactate transporter, permease component 2","Specific phenotype on D-lactate and D,L-lactate and cofit with other components",putative high-affinity branched-chain amino acid transport system permease protein,Possible ABC transporter subunit,,MDAILLQILNGLDKGSAYALIALGLTLIFGTLGVVNFAHGALFMIGAFCAVTVQRVLSLSFETVDETQKDFLGNPLKVKTPYVESWFGPEVGGAIIDWAVPLAILFAIPIMIGVGYVMERGLIKHFYKRPHADQILVTFGLAIVLQEVVKYFYGANPIQTPAPDALNGVVNLGSIIGMDIVYPVWRVVYFFFAVVIIGGIFSFLQFTTFGMVVRAGMADRETVGLLGINIDRRFTIMFGIAAAVAGLAGVMYTPINSPNYHMGMDFLVLSFVVVVVGGMGSLPGAVLAGFLLGVLESFASMNEIKSLIPGIDQIIIYVVAIIILLTRPRGLMGRKGVMED transporters,Phaeobacter inhibens BS107,Phaeo,GFF1251,PGA1_c12670,PGA1_RS06310,WP_014874393.1,tr|I7DZT5|I7DZT5_PHAIB,"D-lactate transporter, substrate binding component","Cofit with the other components and important for growth in various D-lactate and D,L-lactate media","ABC-type branched-chain amino acid transport systems, periplasmic component","Branched-chain amino acid ABC transporter, periplasmic substrate- binding protein",,MSKTDVSRRGVLKTGAIAGAGVALPTIFTASSAAAFTNEPTGSTVTLGFNVPQTGPYADEGADELRAYQLAVEHLNGGGDGGMMNTFSSKALQGNGIMGKEVKFVTGDTQTKSDAARASAKSMIEKDGAVMITGGSSSGVAIAVQGLCQEAGVIFMAGLTHSNDTTGKDKKANGFRHFFNGYMSGAALAPVLKNLYGTDRNAYHLTADYTWGWTQEESIAAATEALGWNTVNKVRTPLAATDFSSYIAPVLNSGADVLVLNHYGGNMVNSLTNAVQFGLREKVVNGKNFEIVVPLYSRLMAKGAGANVKGIHGSTNWHWSLQDEGSQAFVRSFGSKYGFPPSQAAHTVYCQTLLYADAVERAGSFNPCAVVEALEGFEFDGLGNGKTLYRAEDHQCFKDVLVVRGKENPTSEFDLLEVVEVTPAEQVTYAPDHPMFAGGALGTCNSGA catabolism,Phaeobacter inhibens BS107,Phaeo,GFF1301,PGA1_c13170,PGA1_RS06560,WP_014879812.1,tr|I7ELB3|I7ELB3_PHAIB,Sorbitol dehydrogenase (EC 1.1.1.14),Specifically important for: D-Sorbitol. First step is sorbitol catabolism; (SEED_correct),sorbitol dehydrogenase PolS,Sorbitol dehydrogenase (EC 1.1.1.14),,MKRLSGKRALITGAARGIGAAFAEAYANEGARVVIADIDTARAEATAAQIGAAAIAVELDVTDQASIDRALSRTVECFGGLDILINNAAVFTAAPLVEVTREAYQRTFDINVSGTLFMMQAAAQQMITQGTGGKIINMASQAGRRGEPLVSVYCATKAAVISLTQSAGLNLISHGINVNAIAPGVVDGEHWDGVDAFFAKYEGKAPGQKKAEVAQSVPYGRMGTAADLTGMAVFLASEDADYVVAQTYNVDGGQWMS transporters,Phaeobacter inhibens BS107,Phaeo,GFF1302,PGA1_c13180,PGA1_RS06565,WP_014879813.1,tr|I7DZW6|I7DZW6_PHAIB,"ABC transporter for D-Sorbitol, ATPase component",Specific phenotype on D-Sorbitol.,"ABC transporter, ATP binding protein","Various polyols ABC transporter, ATP-binding component",sorbitol/mannitol transport system ATP-binding protein,MGQIKLESVTKNFGPVEVIPPLDLTIEDGEFTVFVGPSGCGKSTLLRLIAGLEDITSGTIRIDGEDATNIPPAKRGLAMVFQSYALYPHMSVRKNIAFPMKMAGIPADEQKRRIDNAAAALNLTDYLDRRPGQLSGGQRQRVAIGRAIVREPAAFLFDEPLSNLDAALRVGMRLEISELHKRLATTMIYVTHDQVEAMTMADKIVVLQAGVIEQVGSPMELYRAPRNVFVAGFIGSPKMNLLTGPQAAQHNAATIGIRPEHLSISETEGMWAGTIGVSEHLGSDTFFHVQCDAFDDPLTVRASGELDLGYGERVFLTPDMTHLHRFGSDGLRIE transporters,Phaeobacter inhibens BS107,Phaeo,GFF1303,PGA1_c13190,PGA1_RS06570,WP_014874441.1,tr|I7EYP2|I7EYP2_PHAIB,"ABC transporter for D-Sorbitol, permease component 1",Specific phenotypes on D-Sorbitol; D-Sorbitol. Not important on mannitol.,ABC transporter permease protein,"Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MARAVTPRRKAINTALAWAVGLLIFFPILWTILTSFKTEAQAISDPPVFLFFDWTLENYSVVQERSDYMRFLWNSVIIAGGSTILGIIIAVPAAWSMAFVPSKRTKDILLWMLSTKMLPAVGVLYPIYILFIKMGLLDNRFGLVVVLMLINLPIIVWMLYTYFKEIPGEILEAARMDGATLKEEILYVLTPMAIPGIASTLLLNIILAWNEAFWTLNLTAAKAAPLTAFIASYSSPEGLFYAKLSAASTMAIAPILILGWFSQKQLVSGLTFGAVK transporters,Phaeobacter inhibens BS107,Phaeo,GFF1304,PGA1_c13200,PGA1_RS06575,WP_014874442.1,tr|I7DPP3|I7DPP3_PHAIB,"ABC transporter for D-Sorbitol, permease component 2",Specific phenotypes on D-Sorbitol; D-Sorbitol. Not important on mannitol.,ABC transporter permease protein,"Various polyols ABC transporter, permease component 1",sorbitol/mannitol transport system permease protein,MATQHSRSAARIMMAPAVILLLGWMLVPLTMTLYFSFKKYLPLRGGDLGWVGFDNYARFLSSSAFWPSVQATLVIVGGVLAITVILGVFLALLLNQPMWGQGIVRILVIAPFFVMPTVSALVWKNMFMDPVNGLFAHLWKAFGAEPVSWLSEASLQSIILIVSWQWLPFATLILLTAIQSLDSEQLEAAEMDGAPPVARFGYITLPHLSRAITVVVLIQTIFLLSIFAEIFVTTQGSFGTKTLTYLIYQRVLESQNVGLGSAGGVYAIILANIVAIFLMRIVGKNLDA transporters,Phaeobacter inhibens BS107,Phaeo,GFF1305,PGA1_c13210,PGA1_RS06580,WP_014879814.1,tr|I7EW74|I7EW74_PHAIB,"ABC transporter for D-Sorbitol, periplasmic substrate-binding component",Specific phenotype on D-Sorbitol.,extracellular solute-binding protein,"Various polyols ABC transporter, periplasmic substrate-binding protein",sorbitol/mannitol transport system substrate-binding protein,MYLKSALRAATALTVFASTAAFADTITIATVNNGDMIRMQGLTEDFTAKTGHEVEWVTLEENVLRQRVTTDISAKGGQFDIMTIGMYETPIWGKNGWLVPLNDLPADYDVDDILPAMRGGLSHDGTLFAAPFYGESSMIMYRTDLMEKAGLEMPAAPTWDFVADAARQMTDKDNETYGICLRGKAGWGENAAFITAMSNSFGARWFDENWAPQFDSEAWSNTLNFYINLLNDAGPPGASNNGFNENLSLFQQGKCGMWIDATVAASFVTNPNDSTVADKVGFALAPDTGLGKRSNWLWAWALAIPAGTQKEAAAKEFIQWATSKDYIELVAENEGWANVPPGARISLYENANYKDIPFAKMTLESILSADPNNPTVDPVPYVGIQFAAIPEFAGIATQVGQEFSAALAGQQTAEEALAKAQALTADEMEAAGY catabolism,Phaeobacter inhibens BS107,Phaeo,GFF1382,PGA1_c13990,PGA1_RS06965,WP_014879881.1,tr|I7EWE4|I7EWE4_PHAIB,Novel Xylose regulator from LacI family,# Specifically important in carbon source D-Xylose. Also see expression evidence (PMC4148808). (SEED_correct),"putative transcriptional regulator, iacl family",Novel Xylose regulator from LacI family,LacI family transcriptional regulator,MGKPTLHDVAAAAGVSYATADRVLNNRGGVAKKSQDRVRAAIADLGYQRDITAANLSRRRRYRFAVLMPAAQEGFFATLHADLEQEVAARSQARQQITLTQVPPFDAAALTCALEACAAEGYDGVCLVAVQDPSVDAALTGLRAQGVAVVTLVADSAAQARDTYVGIDNRRAGRTAGDLMRLAHRGSAASLGQILPITGSLNARDHADRYAGFCDVVSADLHILPPLETGDDPEILEQALRRTLRANPNITGIYNLGAGIPGLISALAAIQPDDRPVVISHELCPATRDAVAHGLIDAVIDQKPAQEITAALAALVALSDGQSVDPLAGQITPAVHFKHNMPPQSAVGPEEGA catabolism,Phaeobacter inhibens BS107,Phaeo,GFF1469,PGA1_c14880,PGA1_RS07390,WP_014879948.1,tr|I7E0E8|I7E0E8_PHAIB,2-hydroxy-3-oxopropionate reductase (EC 1.1.1.60),"Specifically important for: L-Serine. probably important for serine utilization because L-serine can be transaminated to 2-hydroxy-3-oxopropionate, which then needs to be dealt with somehow (SEED_correct)",putative 3-hydroxyisobutyrate dehydrogenase,2-hydroxy-3-oxopropionate reductase (EC 1.1.1.60),3-hydroxyisobutyrate dehydrogenase,MAKVAFLGLGVMGYPMAGHLAAAGHEVTVYNRTAAKAEAWAKEHGGTAAATPREAAAGAEFVMACVGNDDDLRSVCLGDTGAFGGMGAGAIFVDHTTVSAKVTRELNAAAADLGLSFVDAPISGGQAGAENGVLSVMCGGDQAAYDKAEPVIGSYARICRRIGDSGAGQMTKMCNQIAIAGLVQGLSEALHFAEKAGLDGRAVVEVISQGAAGSWQMANRHETMLDDHFDHGFAVDWMRKDLGICLDAAEETGASLPVTALVDQFYKDVQKLGGGRWDTSSLFKRLKAFE catabolism,Phaeobacter inhibens BS107,Phaeo,GFF1616,PGA1_c16380,PGA1_RS08130,WP_014880051.1,tr|I7EX15|I7EX15_PHAIB,arginine deiminase (EC 3.5.3.6),Specifically important for: L-Citrulline. Citrulline is catabolized via arginine by this reaction in reverse; this is distantly related to characterized arginine deiminases (see PF02274),Uncharacterized protein conserved in bacteria containing a pentein-type domain,Amidinotransferase family protein,,MSQLQAPGAVVMIRPHHFCSNPETRDDNAFQTLADDTADVTSAQAQAEFDGAVTALRGAGVSVHVFDDTGTETPDSVFPNNWFSTHAGGHVAVYPMYAANRRKERRWDVIELLKRDYRVQDVIDYSGLEQDGLALEGTGAMVLDHIGRIAYTVKSNRADPVLLERFCTHFNFEPMVFEARDAQGRDVYHTNVLMGIGTDYALICLDMITDPTRRAEVAARLEETGRRVIDLTPEQIAGFAGNALELTGHSRLLALSSRAAEVLRADQITIIERSATLLPLSIPTIETAGGSVRCMLAAIHLSPRERTQS catabolism,Phaeobacter inhibens BS107,Phaeo,GFF1644,PGA1_c16670,PGA1_RS08270,WP_014880077.1,tr|I7E0Z4|I7E0Z4_PHAIB,D-mannose isomerase (EC 5.3.1.7),"Specifically important for: D-Mannitol; D-Mannose. often annotated as N-acylglucosamine 2-epimerase, but the phenotypes confirm that it is mannose isomerase. (Mannitol is catabolized via mannose.)",putative N-acylglucosamine 2-epimerase,N-acylglucosamine 2-epimerase (EC 5.1.3.8),,MSTHSAVGDLSAPGVWLEDTGHQAYLIADARRQLDFFAGSLRAEPGFHTLDAAGRPLPDDKQELHTTTRLVHSYALAHICGYAGAEQMIDHGMAYLNSHHRDQIHGGYLWALSGDAIADDRKLAYGHVFVLLAAASAKLAGHPGADALLSDVAEVLDQRFWETGPKRFADEWNRDWTPFSTYRGMNANMHGVEALLTAYEATGETVFLDRAGHILDFFVDEVAAAESWRLPEHYTETWQIDRTYAGDPMFRPAGTTPGHSFELGRLMLQHWDLAGRRDTGAPTRARSLIEQALADAWLVDGGFAYTLDFEGKVAMRNRFWWPVTEAIGAVATLVKLDGRVEDEQWYRRLWGFAQAHFIDEDRGGWYPEIDGNGQVTRTIFTGKPDIYHALQACLLPLGALPLGHVAGLKKLSAPLLS catabolism,Phaeobacter inhibens BS107,Phaeo,GFF2748,PGA1_c27910,PGA1_RS13875,WP_014880980.1,tr|I7EZT8|I7EZT8_PHAIB,N-acetylglucosamine kinase (EC 2.7.1.59),"Specifically important for: D-Glucose; N-Acetyl-D-Glucosamine. This is the first step in NAG catabolism. The next step is expected to be N-acetylglucosamine-6-phosphate deacetylase (PGA1_c27880, no data). The phenotype on D-glucose is consistent across replicates and is not explained. It does not seem to be due to polar effects. There is a glucokinase that is important for glucose utilization (PGA1_c05420 ) so we don't expect that this is glucokinase. (SEED_correct)","putative ATPase, BadF/BadG/BcrA/BcrD type",N-acetylglucosamine kinase of eukaryotic type (EC 2.7.1.59),,MTDSRFPYLIAVDGGGTSCRFALLKSGATPPQQLVVTGGSANVYTAPDQAVETLSAGLADLQRQSGLTDEVFHQIPVYAGLAGVIDGESAARVAEALPQAHVRVEDDRMPAVVGALGEDTASLIGVGTGSFLGRQVAGQVTLIGGHGTVLGDEASGGWLGRRALQLTLQAAEGIEPMTPLLRSCLRDFSNETAKIVRFAQTARPVAFGAYAPRVAKAAVEGDAAGRRLMAEGAEYLCNGLQALGRRPEEPVYHIGGVAAQYAAYLPADVADYLRGAEGSPLDGALELARRFAREIAQEVV transporters,Phaeobacter inhibens BS107,Phaeo,GFF2750,PGA1_c27930,PGA1_RS13885,WP_014880982.1,tr|I7E3W0|I7E3W0_PHAIB,"N-Acetyl-D-glucosamine ABC transport system, periplasmic substrate-binding component",Specific phenotype on N-Acetyl-D-Glucosamine. (SEED_correct),"putative sugar ABC transporter, periplasmic binding protein","N-Acetyl-D-glucosamine ABC transport system, sugar-binding protein",multiple sugar transport system substrate-binding protein,MTRKLFTLALLGSTMIGGAAAAEDVTLTIESWRNDDLALWQDKIIPAFEAANPGIKLKFTPSAPAEYNAVLNSKLDAGSAGDLITCRPFDASLGLYDKGQLADLSDLDAMGSFSNVAKSAWQTDDGSATFCVPIASVIHGFIYNKDAFAELGVNVPETEDEFFAALEKIKEDGNYVPLAMGTNDQWEAATMGYNNIGPNYWKGEEGRLALIAGEQKLTDDQWVAPYETLSKWGAYMGDGYEAQTYPDSQNIFTLGRAAIYPAGSWEISGFNTQADFAMGAFKPPVKAAGDTCYISDHTDIAVGLNAASPNAEAAKTFLNWVGSAEFASIYANALPGFFSLSNHEVAMEDPLAQEFVSWRGECESTIRSTYQILSRGTPNLENETWGASVAAIKGTKAPADLGAELQEGLASWYEPQK transporters,Phaeobacter inhibens BS107,Phaeo,GFF2751,PGA1_c27940,PGA1_RS13890,WP_014875690.1,tr|I7F282|I7F282_PHAIB,"N-Acetyl-D-glucosamine ABC transport system, permease component 1",Specific phenotypes on N-Acetyl-D-Glucosamine. (SEED_correct),ABC transporter permease protein,"N-Acetyl-D-glucosamine ABC transport system, permease protein 1",multiple sugar transport system permease protein,MQNTPHKPRRRWHIAVFLAPAVLVYTAIMIFPLFNTLRLALYSESDQIRQFVGLANFETLFGNPNWSEQFWNALGNNFWFFFVHMLVQNPIGVALAAILSHPRLRFAALYRTAIFVPTILSFVIVGFAWKLILSPIWGITPDLLDAIGLKWLFAPWLGKEDYALTTLALISVWQFVGIPMMLIYAALLSIPEEVIEAGEVDGITGMSAFWKIKLPLILPSIGIISILTFVGNFNAFDLIYTTQGALAGPDFSTDILGTFMYRTFFGFQLQLGDPHMGSAIATAMFAIILIGVCIYLFGIQTRLRRYQF transporters,Phaeobacter inhibens BS107,Phaeo,GFF2752,PGA1_c27950,PGA1_RS13895,WP_044040979.1,tr|I7DTQ0|I7DTQ0_PHAIB,"N-Acetyl-D-glucosamine ABC transport system, permease component 2",Specific phenotypes on N-Acetyl-D-Glucosamine. (SEED_correct),ABC transporter permease protein,"N-Acetyl-D-glucosamine ABC transport system, permease protein 2",multiple sugar transport system permease protein,MMHKSRSNPFNSILAHGALITYTLIALFPVFVILVNSFKTRKAIFRDPLGLPTSDTFSLVGYQTVLKQGDFFLYFQNSMIVTVVSLALVLLFGAMAAFALAEYRFKGNMLLGLYLALGIMIPIRIGTVAILELMVDTGLVNTLTALILVYTAQGLPLAVFILSEFMKQVSDDLKNAGRIDGLSEYTIFFRLVLPLVRPAMATVAVFNMIPIWNDLWFPLILAPAEETKTLTLGSQVFIGQFVTDWNAVLSALSMAILPVMVLYVIFSRQLIRGITSGAVK transporters,Phaeobacter inhibens BS107,Phaeo,GFF2754,PGA1_c27970,PGA1_RS13905,WP_014880984.1,tr|I7EQ92|I7EQ92_PHAIB,"N-Acetyl-D-glucosamine ABC transport system, ATPase component",Specific phenotype on N-Acetyl-D-Glucosamine. (SEED_correct),ATP-binding transport protein SmoK,N-Acetyl-D-glucosamine ABC transport system ATP-binding protein,sorbitol/mannitol transport system ATP-binding protein,MTALQLTNVCKSFGPVEVLKDINLTVEDGEFVVFVGPSGCGKSTLLRVISGLEDATAGEISIGGQTVTTTPPAKRGIAMVFQSYALYPHLSVRENMALALKQERQPKEEIAARVAEASRMLSLEDYLDRRPSELSGGQRQRVAIGRAVVREPKLFLFDEPLSNLDAALRMNTRLEIARLHRQLSASMIYVTHDQIEAMTLADKIVVLRDGRIEQVGTPMELYNNPANRFVAEFIGAPAMNFVPAQRLGGNPGQFIGIRPEYARISPVGPLAGEVIHVEKLGGDTNILVDMGEDLTFTARLFGQHDTNVGETLQFDFDPANCLSFDEAGQRI catabolism,Phaeobacter inhibens BS107,Phaeo,GFF2923,PGA1_c29700,PGA1_RS14755,WP_014881125.1,tr|I7DU71|I7DU71_PHAIB,"D-lactate oxidase, iron-sulfur subunit (EC 1.1.3.15)","Specifically important for: Sodium D,L-Lactate; Sodium D-Lactate. KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase)",glycolate oxidase iron-sulfur subunit,"Glycolate dehydrogenase (EC 1.1.99.14), iron-sulfur subunit GlcF",glycolate oxidase iron-sulfur subunit,MQTTFSEKQLRDPGTQRANEILRSCVHCGFCTATCPTYQVLGDELDSPRGRIYLIKDMLENERVPDAKTVKHIDRCLSCLACMTTCPSGVHYMHLVDHARAYIDKHYNRPWSDRALRWLLARILPYPGRFRLALIGAKLAQPFKRLVPDARLRAMLDMAPRHIPPVSRNDDPQSFAAKAPRRKRVALMTGCAQKALNTDINDATIRLLTRLGCEVVVAAGAGCCGALTHHMGREEESHATAAKNIRAWTDEIDGQGLDAIVINTSGCGTTVKDYGHMFRNDALAEDAARVSAIAMDISELLMQLDLPEGEDKETTVAYHAACSLQHGQQIKTHPKTLLKRAGFTVVEPADSHLCCGSAGTYNLLQPEISAELKARKVTSLEARQPDLIAAGNIGCMMQIGSATDIPILHTVELLDWATGGPKPRALVAGGPGADPRAGEIPILR catabolism,Phaeobacter inhibens BS107,Phaeo,GFF2924,PGA1_c29710,PGA1_RS14760,WP_014881126.1,tr|I7F093|I7F093_PHAIB,"D-lactate oxidase, FAD binding subunit (EC 1.1.3.15)","Specifically important for: Sodium D-Lactate; Sodium D,L-Lactate. KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase)",glycolate oxidase subunit GlcE,"Glycolate dehydrogenase (EC 1.1.99.14), FAD-binding subunit GlcE",glycolate oxidase FAD binding subunit,MTPQSEAELAQIIVGATAPLAVSGGGTRGLSTGGETLSVAGLNGVTLYEPGALTLVVQAGTSVEEVQALLAGENQRLAFEPMDHRGLLGTKGTPTIGGVFAANVSGPRRIQCGAARDFLLGVRFVDGRGDVLSNGGRVMKNVTGYDLVKLMAGSHGTLGVLSEVSLKVLPCSEACATVTVHVADLTSAVAAMSTALGSPYDVTGAAYDPEAGAVYIRVEGFEASVTYRAEALKMALGKFGEVSLALGAGDALWEGIRNVAAFHDRPGDVWRISVKPSDAVALAPALEAEGLLFDWGGGLIWALVPAGRDLRFRLTVPGHATLVRASAQTRAELGQFQPQPGPLAAISGGLRRQFDPRGILNPGLMG catabolism,Phaeobacter inhibens BS107,Phaeo,GFF2925,PGA1_c29720,PGA1_RS14765,WP_014881127.1,tr|I7EQM9|I7EQM9_PHAIB,"D-lactate oxidase, FAD-linked subunit (EC 1.1.3.15)","Specifically important for: Sodium D,L-Lactate; Sodium D-Lactate. KEGG has the correct EC # but describes it as glycolate oxidase; SEED has it as 1.1.99.14 (glycolate dehydrogenase)",glycolate oxidase subunit GlcD,"Glycolate dehydrogenase (EC 1.1.99.14), subunit GlcD",glycolate oxidase,MEMPIPDQTVLSQKTELALRLAAVLPDDALVQDPAETRAYECDALTAYKCPPMLVVLPRTTKEVSDVLRICHAAGVPVVPRGAGTSLAGGALPTADCVILGVARMNAVLETDYDNRIIRVQTGRTNLSVSGAVEEEEFFYAPDPSSQLACAIAGNIAMNSGGAHCLKYGVTTNNLMGVTMVMMDGTVVEIGGAHLDAGGLDLLGVICGSEGQLGVVTEATLRILRKPEGARPVLIGYDSNEVAGACVSDIIKAGVLPVAIEFMDRPCIEACEAFAKAGYPMCEALLIVEVEGSDAEIDHQLRLITEIARSHNPVELREARDSDEAARIWLGRKSAFGAMGQINDYMCLDGTIPVTSLPHVLRRIGEMSKEFGLDVANVFHAGDGNMHPLILFDANKPGDLETCEAFGAEILKLCVEVGGCLTGEHGVGIEKRDLMLDQYGVADIEAQLRVKDVFDPKWLLNPAKVFPLSTTQSRRTPAVTPL catabolism,Phaeobacter inhibens BS107,Phaeo,GFF357,PGA1_c03680,PGA1_RS01815,WP_014879056.1,tr|I7ETR9|I7ETR9_PHAIB,(3S)-malyl-CoA thioesterase [EC:3.1.2.30],"Specifically important for: L-Threonine; Potassium acetate. This is part of the ethylmalonyl-CoA cycle for acetate assimilation, and hence is important on threonine (cleaved to glycine and acetate via the PGA1_c34330 or kbl) and acetate (where it has a milder phenotype). The annotation of sit:TM1040_3520 has been updated to (3S)-malyl-CoA thioesterase [EC:3.1.2.30]. 69% similar to MCTE_RHOS4 (consistent with the KEGG annotation). SEED suggests that this is the malyl-CoA/methylmalyl-CoA lyase but that activity is probably provided by PGA1_c30490. (KEGG_correct)",putative citrate lyase subunit beta,L-malyl-CoA/beta-methylmalyl-CoA lyase (EC 4.1.3.-),(3S)-malyl-CoA thioesterase,MDPRIRPYRSVLYIPGSKLRALEKARSLPVDAIIFDLEDAVSAEEKVNARQTLEEALRAGGYGARMKIVRINGLDTVWGRADAAAAARMDCDAILLPKVSSPEDLDALAVITGDKPLWAMMETPRGMLNAAAIAAHPLLQGMVMGTNDLAKELQTRFRPDRLPLMAGLGQCLLAAKAKGIIIVDGVYNAFKDAEGLEAECDQGRDMGFDGKTLIHPAQVDVANSAFAPSEDEIDLARRQIAAFDAAEAEGQGVAVVDGKIVENLHVATAREILAKADAIAAVSA transporters,Phaeobacter inhibens BS107,Phaeo,GFF3639,PGA1_262p00430,PGA1_RS18345,WP_014881674.1,tr|I7F4G1|I7F4G1_PHAIB,"glucose transporter, periplasmic substrate-binding component","specific phenotype on glucose and cofit with other nearby components. This is similar (65% identity) to gxyS (Atu3576) of Agrobacterium tumefaciens, which is involved in the transport of glucose, glucosamine, and xylose (PMCID: PMC4135649). It is not clear if the Phaeobacter system transports xylose, as it is not required for utilizing xylose, but no other xylose transport system was identified either.",D-xylose-binding periplasmic protein XylF,"Xylose ABC transporter, periplasmic xylose-binding protein XylF",D-xylose transport system substrate-binding protein,MKFLSGVSALAFAATASMAFAEDVTVGVSWSNFQEERWKTDEAAIKAALEAKGATYVSADAQSSSAKQLSDIESLIAQGVDALIVLAQDAQAIGPAVQAAADEGIPVVAYDRLIEDGRAFYLTFDNVEVGRMQARAVLEAQPSGNYVMIKGSPTDPNADFLRGGQQEIIQAAIDSGDIKIVGEAYTDGWLPANAQRNMEQILTANDNKVDAVVASNDGTAGGVVAALTAQGMEGIAVSGQDGDHAALNRVAKGTQTVSVWKDARDLGKAAANIAVEMAEGAVMGDVAGGAAWTSPAGTELTARFLEPIPVTADNLSVVVDAGWITKEALCQGVTNGPAPCN transporters,Phaeobacter inhibens BS107,Phaeo,GFF3640,PGA1_262p00440,PGA1_RS18350,WP_014881675.1,tr|I7DWB0|I7DWB0_PHAIB,"glucose transporter, permease component","specific phenotype on glucose and cofit with nearby xylF (PGA1_262p00430) and xylG (PGA1_262p00450). This is similar to the gxyB (Atu3575) of Agrobacterium tumefaciens, which is involved in the transport of glucose, glucosamine, and xylose (PMCID: PMC4135649). It is not clear if the Phaeobacter system transports xylose, as it is not required for utilizing xylose, but no other xylose transport system was identified either.",xylose transport system permease protein XylH,"Xylose ABC transporter, permease protein XylH",D-xylose transport system permease protein,MTETTSQPIPEHSKRGLFQQLELDVRLLGMIGAFVILCIGFNILTDGRFLTPRNIFNLTIQTVSVAIMATGMVFVIVTRHIDLSVGALLATCSAVMAVVQTDVLPDMFGLGLNHPATWIITVAVGLAIGTLIGAFQGWMVGFLTIPAFIVTLGGFLVWRNVAWYLTDGQTIGPLDSTFLVFGGTSGTLGTTLSWVVGIVATLLALAALWNSRRAKQGHGFPVKPAWAEAVIAGSIAASILGFVAILNAYQIPARRLKRMMEAQGETMPEGLVVGYGLPISVLILIATAVVMTIIARRTRLGRYIFATGGNPDAAELSGINTRLLTVKIFALMGFLCALSAVVASARLANHSNDIGTLDELRVIAAAVIGGTALSGGFGTIYGAILGALIMQSLQSGMAMVGVDAPFQNIVVGTVLVAAVWIDILYRKRVGARI transporters,Phaeobacter inhibens BS107,Phaeo,GFF3641,PGA1_262p00450,PGA1_RS18355,WP_014881676.1,tr|I7F276|I7F276_PHAIB,"glucose transporter, ATPase component","Important for utilizing glucose and cofit with the other components. This is similar (68% identity) to gxyA (Atu3574) of Agrobacterium tumefaciens, which is involved in the transport of glucose, glucosamine, and xylose (PMCID: PMC4135649). It is not clear if the Phaeobacter system transports xylose, as it is not required for utilizing xylose, but no other xylose transport system was identified either.","sugar ABC transporter, ATP-binding protein",D-xylose transport ATP-binding protein XylG,D-xylose transport system ATP-binding protein,MSTAKELRAAGATPLVEMKDISISFGGIKAVDHVSVDLYPGEVVGLLGHNGAGKSTLIKVLSGAYQMDAGEIRVNGDKVEITNPRDARSHNIETIYQTLALADNLDAASNLFLGRELVTPFGLVDDSAMEAECRKIMNRLNPNFQKFSEPVSALSGGQRQSVAIARAVYFNAKILIMDEPTAALGPHETQMVAELIQQLKAQGIGIFLIDHDVNAVMELCDRASVMKNGQLVGTVDIDDVTDDDLLSMIILGKRPGEAAA catabolism,Phaeobacter inhibens BS107,Phaeo,GFF707,PGA1_c07220,PGA1_RS03580,WP_014879336.1,tr|I7EX64|I7EX64_PHAIB,5-keto-2-deoxy-D-gluconate-6 phosphate aldolase (EC 4.1.2.29),Specifically important for: m-Inositol. Part of inositol catabolism via 5-deoxyglucuronate (SEED_correct),putative 6-phospho-5-dehydro-2-deoxy-D-gluconate aldolase,5-keto-2-deoxy-D-gluconate-6 phosphate aldolase (EC 4.1.2.29),"fructose-bisphosphate aldolase, class II",MTIATLAEVLQPALRDGYAVAGLVTLGWEDMRAYVAAAEAEGVPVILQAGPSCRVHTPLPILGKMFRHLAEGASVPVVAHLDHGYTAEDCRIAIDSGFTSVMFDGSRNALDDNIAETAAIAGMAHAAGVSCEGEIGFVGYSGGEGSAGTDPEEARRFAQETGVDAMAISVGNVHLQQDKEGGLDIDRIRAIEAITEVPLVIHGGSGVPVAQRRMLARESRICKFNIGTELRMVFGAAMRDAVNSDPDRFDRVSILSETHDPVVAAARSVLRAFKGETQC catabolism,Phaeobacter inhibens BS107,Phaeo,GFF711,PGA1_c07260,PGA1_RS03600,WP_014879339.1,tr|I7DYC7|I7DYC7_PHAIB,Myo-inositol 2-dehydrogenase (EC 1.1.1.18),Specifically important for: m-Inositol. The first step in inositol catabolism (SEED_correct),putative inositol 2-dehydrogenase,Myo-inositol 2-dehydrogenase (EC 1.1.1.18),,MVCGRVDLILRDVESQDTSFATGCKLNLMVWEARMTEIGIGILGGGYMGKAHAVAMAAVGAVFDTALRPRLEMVCATSPVSAERYRKAYGFQRATDDWQVLVNDPKVEAIVIASPQQTHRVVAEAAFALGKPVFCEKPLGASLADSRAMVAAAQSSGCPNMVGFNYIRTPASQYARQLIADGLIGEVTWFRGEHTEDFYADPDAPATWRTEGEANGTMGDLAPHMINGAMALIGPIAELIAEVETVHTDRPGGRVTNDDQGQLMCRFANGAMGQMFFSRIATGRKMGYAYEITGTKGAIRFDQEDQNALWLYLSEGDEATRGFRKILTGPAHPDYLPFCQGPGHGTGYQDQILIEARDFLRAIETQTPVWPTFEDGMAVNQVIAAAMAASEARSWQTVSTF transporters,Phaeobacter inhibens BS107,Phaeo,GFF715,PGA1_c07300,PGA1_RS03620,WP_014873819.1,tr|I7EJU1|I7EJU1_PHAIB,Inositol transport system sugar-binding protein,Specific phenotype on inositol and cofit with other nearby components (SEED_correct),"ABC-type sugar transport system, periplasmic component",Inositol transport system sugar-binding protein,simple sugar transport system substrate-binding protein,MTSIFKTFMLATTVAAAPMMLATTASAEGEKYILVSHAPDSDSWWNTIKNGIALAGEQMNVEVEYRNPPTGDLADMARIIEQAAASGPNGIITTLSDYDVLSGPIKAAVDSGVDVIIMNSGTPDQAREVGALMYVGQPEYDAGHAAGMRAKADGVGSFLCVNHYISSPSSTERCQGFADGLGVDLGDQMIDSGQDPAEIKNRVLAYLNTNPETDAILTLGPTSADPTLLALDENGMAGDIYFGTFDLGEEIVKGLKSGVINWGIDQQPFLQAYLPVVVLTNYHRYGVLPGNNINSGPGFVTKDGLEKVEEFAGEYR transporters,Phaeobacter inhibens BS107,Phaeo,GFF716,PGA1_c07310,PGA1_RS03625,WP_014873820.1,tr|I7DYD2|I7DYD2_PHAIB,Inositol transport system permease protein,Specific phenotype on inositol and cofit with other nearby components (SEED_correct),binding protein dependent transport system permease,Inositol transport system permease protein,simple sugar transport system permease protein,MSDAPTQFAEDERIKTRSKFREAMIRPELGGIIGTITVFAMFLIFAGDSGMFNSQGVMNWSQISAQFMIIAVGACLLMIAGEFDLSVGSMIGFAGMLIAIFSVTLGWPVWLAILVTFAIATAIGALNGFIVVRTGLPSFIVTLAFLFILRGFAIYLPQTIERKTIIGGVADAAEGDWLAALFGGKILTGLFQWFGDNGWIAVFERGTRKGQPVVEGLPMLIVWAILLVIIGHVILTKTRFGNWIFAAGGDAEAARNSGVPVNRVKILMFMFTAFCATVFATCQVMEFGGAGSDRGLLKEFEAIIAVVIGGALLTGGYGSVLGAALGALIFGVVQQGLFFAGVESSLFRVFLGLILLFAVILNTYIRRVITGER transporters,Phaeobacter inhibens BS107,Phaeo,GFF717,PGA1_c07320,PGA1_RS03630,WP_014873821.1,tr|I7EX72|I7EX72_PHAIB,Inositol transport system ATP-binding protein,Specific phenotype on inositol and cofit with other nearby components (SEED_correct),"sugar ABC transporter, ATP-binding protein",Inositol transport system ATP-binding protein,simple sugar transport system ATP-binding protein,MSMSQPLIRMQGIEKHFGSVIALAGVSVDVFPGECHCLLGDNGAGKSTFIKTMSGVHKPTKGDILFEGQPLHFADPRDAIAAGIATVHQHLAMIPLMSVSRNFFMGNEPIRKIGPLKLFDHDYANRITMEEMRKMGINLRGPDQAVGTLSGGERQTVAIARAVHFGAKVLILDEPTSALGVRQTANVLATIDKVRKQGVAVVFITHNVRHALAVGDRFTVLNRGKTLGTAQRGDISAEELQDMMAGGQELATLEGSLGGTV catabolism,Dechlorosoma suillum PS,PS,Dsui_0512,Dsui_0512,DSUI_RS02475,WP_014235627.1,tr|G8QFB6|G8QFB6_DECSP,Methylmalonyl-CoA epimerase (EC 5.1.99.1),Important on proprionate; this makes sense because of the utilization of the (S)-methylmalonyl-CoA formed by propionyl-CoA carboxylase (SEED_correct),hypothetical protein,Methylmalonyl-CoA epimerase (EC 5.1.99.1),lactoylglutathione lyase,MSQRPFKVLGIQQIAIGGPSKDKLKTLWVDMLGLEVTGNFVSERENVDEDICAMGKGPFKVEVDLMQPLDPEKKPAVHTTPLNHVGLWIDDLPKAVEWLTANGVRFAPGGIRKGAAGFDICFLHPKGNEESPIGGEGVLIELVQAPAEVVDAFAKLAG catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_1109,,A1D17_RS10255,WP_063341556.1,tr|A0A166N099|A0A166N099_PSEFL,2-ketoglutaric semialdehyde dehydrogenase (EC 1.2.1.26),Specifically important for: D-Galacturonic Acid monohydrate. ketoglutarate semialdehyde is an intermedate in the oxidation of galacturonate (SEED_correct),2-ketoglutaric semialdehyde dehydrogenase (EC 1.2.1.26),2-ketoglutaric semialdehyde dehydrogenase (EC 1.2.1.26),aldehyde dehydrogenase (NAD+),VADAKRYDNYINGEWVSGADYSANINPSELTDTIGDYAKADLAQVHAAIDAARAAFPAWSTSGIQARHDSLDKVGTEILARREELGTLLAREEGKTLPEAIGEVTRAGNIFKFFAGECLRLSGDYLPSVRPGVNVEVTREALGVVGLITPWNFPIAIPAWKIAPALAYGNCVVLKPADLVPGCAWALAEIISRAGFPAGVFNLVMGSGRVVGDALVQSPKVDGISFTGSVGVGRQIAVSCVSRQAKVQLEMGGKNPQIILDDADLKQAVELSVQSAFYSTGQRCTASSRFIVTAGIHDKFVEAMAERMKSIKVGHALKTGTDIGPVVSQAQLEQDLKYIDIGQSEGARLVSGGGLVACDTEGYFLAPTLFADSTAAMRISREEIFGPVANIVRVADYEAALAMANDTEFGLSAGIATTSLKYANHFKRHSQAGMVMVNLPTAGVDYHVPFGGRKGSSYGSREQGRYAQEFYTVVKTSYIGS catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_2254,,A1D17_RS15975,WP_007897683.1,tr|A0A166NMU2|A0A166NMU2_PSEFL,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),"Specifically important for: Putrescine Dihydrochloride. This is the first step in putrescine catabolism. Some close homologs are annotated as this by SEED, so not sure why it was missed. Also, another gene is annotated as this activity (Pf1N1B4_4354) but has no phenotype.",Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),,glutamine synthetase,MNGVVRGKRIERTSLHKVYEKGINLPASLFALDINGSTVESTGLGLDIGDADRICYPIPDTLCNEPWQKRPTAQLLMTMHELEGDPFFADPREVLRQVVAKFDEMGLTICAAFELEFYLIDQENVNGRPQPPRSPISGKRPHSTQVYLIDDLDEYVDCLQDILEGAKEQGIPADAIVKESAPAQFEVNLHHVADPIKACDYAVLLKRLIKNIAYDHEMDTTFMAKPYPGQAGNGLHVHISILDKDGKNIFASEDPEQNAALRHAIGGVLETLPAQMAFLCPNVNSYRRFGAQFYVPNSPCWGLDNRTVAIRVPTGSSDAVRIEHRVAGADANPYLLMASVLAGVHHGLTNKIEPGAPVEGNSYEQNEQSLPNNLRDALRELDDSEVMAKYIDPKYIDIFVACKESELEEFEHSISDLEYNWYLHTV transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_3431,,A1D17_RS21835,WP_063342827.1,tr|A0A166PAW1|A0A166PAW1_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, periplasmic substrate-binding component",Specific phenotype on L-Arginine; L-Citrulline.,"Arginine/ornithine ABC transporter, periplasmic arginine/ornithine binding protein","Arginine/ornithine ABC transporter, periplasmic arginine/ornithine binding protein",,MKKLVLLGALALSVLSLPTFADEKPLKIGIEAAYPPFASKAPDGSIVGFDYDIGNALCEEMKVKCVWVEQEFDGLIPALKVRKIDAILSSMSITEDRKKSVDFTNKYYNTPARLVMKAGTAVSENLAELKGKNIGVQRGSIHERFAREVLAPLGAEIKPYGSQNEIYLDVAAGRLDGTVADATLLDDGFLKTDAGKGFAFVGPAFTDVKYFGDGVGIAVRKGDALKDKINTAIAAIRENGKYKQIQDKYFAFDIYGK transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_3432,,A1D17_RS21840,WP_063342828.1,tr|A0A162AX92|A0A162AX92_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, permease component 1",Specific phenotypes on L-Arginine; L-Citrulline.,"Arginine/ornithine ABC transporter, permease protein AotQ","Arginine/ornithine ABC transporter, permease protein AotQ",arginine/ornithine transport system permease protein,MLKGYGAVILDGVWLTLQLALSSMVLAIVLGLIGVALRLSPIRWLAWLGDLYSTVIRGIPDLVLILLIFYGGQDLLNRVAPMFGYDDYIDLNPLAAGIGTLGFIFGAYLSETFRGAFMAIPKGQAEAGMAYGMSSFQVFFRVLVPQMIRLAIPGFTNNWLVLTKATALISVVGLQDMMFKAKQAADATREPFTFFLAVAAMYLVITSVSLLALRHLEKRYSVGVRAADL transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_3433,,A1D17_RS21845,WP_063342829.1,tr|A0A161Z5M0|A0A161Z5M0_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, permease component 2",Specific phenotypes on L-Arginine; L-Citrulline.,"Arginine/ornithine ABC transporter, permease protein AotM","Arginine/ornithine ABC transporter, permease protein AotM",arginine/ornithine transport system permease protein,MIFDYNVIWEALPLYLGGLVTTLKLLALSLFFGLLAALPLGLMRVSKQPVVNMSAWLFTYVIRGTPMLVQLFLIYYGLAQFEAVRESFLWPWLSSATFCACLAFAINTSAYTAEIIAGSLRATPNGEIEAAKAMGMSRIKMYKRILLPSALRRALPQYSNEVIMMLQTTSLASIVTLIDITGAARTVNAQFYLPFEAYITAGVFYLCLTFILVRLFKMAEHRWLGYLAPRKH transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_3435,,A1D17_RS21855,WP_007904680.1,tr|A0A166PAY4|A0A166PAY4_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, ATPase component",Very important for utilization of L-Arginine or L-Citrulline. Detrimental to fitness in some other minimal media experiments.,"Arginine/ornithine ABC transporter, ATP-binding protein AotP","Arginine/ornithine ABC transporter, ATP-binding protein AotP",,MYKLEVQDLHKRYGSHEVLKGVSLKAAAGDVISIIGSSGSGKSTFLRCINLLEQPHAGKILLNNEELKLVANKDGALKAADPKQLQRMRSRLSMVFQHFNLWSHMTAMENIMEAPVHVLGMSKTEAREKAEHYLNKVGVAHRKDAYPGHMSGGEQQRVAIARALAMEPEVMLFDEPTSALDPELVGDVLKVMQALAQEGRTMVVVTHEMGFAREVSNQLVFLHKGVVEESGNPREVLVNPQSERLQQFLSGSLK catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_412,,A1D17_RS06770,WP_063341172.1,tr|A0A166MLN9|A0A166MLN9_PSEFL,L-arabinose 1-dehydrogenase / D-galactose 1-dehydrogenase (EC 1.1.1.46; EC 1.1.1.48),"Both of these sugars are catabolized via a 1-dehydrogenase followed by lactonase and dehydratase reactions. This is the only dehydrogenase is specifically important for either of these carbon sources in several Pseudomonas, apart from a alpha-ketoglutarate semialdehyde dehydrogenase that is expected to be the last dedicated step in L-arabinose catabolism (i.e., Pf6N2E2_612). L-arabinose and D-galactose are chemically similar and some dehydrogenases are already known to act on both substrates.",3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),,MAEPLSLPPVPEPPKGERLKNKVVLLTGAAQGIGEAIVATFASQQARLVISDIQGEKVEKVAAHWREQGADVVAIKADVSRQQDLHAMARLAIELHGRIDVLVNCAGVNVFRDPLQMTEEDWHRCFAIDLDGAWYGCKAVLPQMIEQGIGSIINIASTHSTHIIPGCFPYPVAKHGLLGLTRALGIEYAPKGIRVNAIAPGYIETQLNVDYWNGFADPHAERQRAFDLHPPRRIGQPIEVAMTAVFLASDEAPFINASCITIDGGRSVMYHD catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_4511,,A1D17_RS27145,WP_063343423.1,tr|A0A166Q345|A0A166Q345_PSEFL,D-galacturonate dehydrogenase (EC 1.1.1.203),Important on D-galacturonate; KEGG now has pfo:Pfl01_3452 as uronate dehydrogenase which is correct but less specific (KEGG_correct),UDP-glucose 4-epimerase (EC 5.1.3.2),UDP-glucose 4-epimerase (EC 5.1.3.2),,MTNTSPFNRLLLTGAAGGLGKVLRERLRPYANVLRLSDIANMAPAIDDREEVVPCDLADKQAVHQLVEGVDAILHFGGVSVERPFEEILGANICGVFHIYEAARRHGVKRVIFASSNHVIGFYKQDKTLDAHSPRRPDSYYGLSKSYGEDMASFYFDRYGIETVSIRIGSSFPEPQNRRMMSTWLSFDDMTQLLERALYTPNVGHTVVYGMSANKSVWWDNRFAAHLGFAPQDTSEVFREKVEAQPMPAEDDPARVYQGGAFVAAGPFGD catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_4623,,A1D17_RS27665,WP_063343491.1,tr|A0A161Z7Y9|A0A161Z7Y9_PSEFL,2-dehydro-3-deoxy-L-arabinonate dehydratase (EC 4.2.1.43),"# Specifically important in carbon source L-Arabinose. Similar to PA2216 from Pseudomonas aeruginosa (see PMC:PMC4038344) and to gguC or araD1 (Atu2345) from Agrobacterium tumefaciens (see PMC: PMC3232879), which also have this activity. In Agrobacterium, this reaction is proposed to be a step in L-arabinose oxidation. (Note that 2-keto-3-deoxy-L-lyxonate and 2-keto-3-deoxy-L-arabinonate are the same compound.)",SUGAR TRANSPORTER,SUGAR TRANSPORTER,,MRLVQFELSHGERRVGVVEDGLVREVRDARSVRDLALAAIEAGVNLEQQVQTLGLGISHDYSALLANLQILPPLDHPDPAHMLVSGTGLTHLGSASARDKMHQQAGDETAMTDTMRIFKWGVEGGKPATGQVGVQPEWFYKGDGSIVVRPGKPFPLPPFAEDAGEEPELSGLYVIGHDGKPYRLGFAVGNEFSDHVMERKNYLYLAHSKLRSCSYGPELRVGELPQHLAGTSRILRDGEVLWQNEFLSGEANMCHSLENLEYHHFKYSQFLRPGDVHIHFFGTATLSFADGIRTQPGDVFEISQAEFGAPLINGIEPVEAAFEPGTVGTL catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_597,,A1D17_RS07755,WP_063341295.1,tr|A0A161YYW9|A0A161YYW9_PSEFL,Isomerase for D-mannose and D-mannitol catabolism (probably D-mannose-6-phosphate or D-mannose isomerase),"Specifically important for: D-Mannitol; D-Mannose. Often annotated as N-acylglucosamine 2-epimerase, but some homologs are annotated as D-mannose isomerase or D-mannose-6-phosphate isomerase, which would fit its phenotypes. (Mannitol is probably converted to mannose or mannose-6-phosphate.) The organism has a fructokinase (Pf1N1B4_4844, no data) which probably utilizes the resulting fructose if this is D-mannose isomerase. However data for close homologs suggests a phosphorylated substrate instead, so the substrate is uncertain.",N-acylglucosamine 2-epimerase (EC 5.1.3.8),N-acylglucosamine 2-epimerase (EC 5.1.3.8),,MDTFQPAFSSWLNAPAHQQWLAAEGLRLLAFAKAAKLAEGFGNLDEKGRLAANAQAETMNTARMTHSFAMAHIQGLPGFAELVDHGIQALSGPLRDAEHGGWFATPEHRDGNTGKAAYLHAFVALAASSAVVAQRPGAQALLDDAIHIIDSHFWSEEEGAMRESFNRDWSVEEAYRGANSNMHATEAFLALADVTEDNRWLIRAQRIVERVIHDHAAVNDYLVVEHFDRDWQPLRDYNYDNPADGFRPYGTTPGHGFEWARLLLHLEAARVQAGILTPGWLATDAQKLFDHNCRHGWDVDGAPGIVYTLDWDNRAVVRHRLHWTHAEASAAASALLKRTGDEQYERWYRLFWEFCDSHFIDRCDGSWHHELDPLNRPSADIWAGKPDLYHAWQAVLIPRLPLAPSMAIALAQLSQSVAV catabolism,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_6029,,A1D17_RS02225,WP_063340417.1,tr|A0A166R3Y5|A0A166R3Y5_PSEFL,uridine nucleosidase (EC 3.2.2.3),Important on uridine; annotated by SEED as for uridine and inosine; close homologs are important for utilizing uridine (SEED_correct),Inosine-uridine preferring nucleoside hydrolase (EC 3.2.2.1),Inosine-uridine preferring nucleoside hydrolase (EC 3.2.2.1),,MQRYAQKLHHLLRSLLLLSLITATSAQAAEKIDLIIDTDPGADDVVALLFALASPDELNIRALTTVAGNVRLDKTSRNARLAREWAGREEVPVYAGAPKPMMRTPIYAENIHGKEGLSGVTVHEPKKGLAEGNAVNYLIDTLKTAKPHSITIAMLGPQTNLALALIQEPEIVQGIKEVVIMGGAHFNGGNITPVAEFNLFADPQAAEVVLKSGVKLTYLPLDVTHKILTSEARLKQIAALNNNASKLVGDILNEYVKGDMEHYGIPGGPVHDATVIAYLLKPELFTGRSVNVVVDSREGPTFGQTIVDWYDGLKAPKNAFWVESGDAQGFFDLLTQRLSRLK transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_771,,A1D17_RS08645,WP_063341404.1,tr|A0A162BIP5|A0A162BIP5_PSEFL,"ABC transporter for L-asparagine and L-glutamate, periplasmic substrate-binding component",# Specifically important in carbon source L-Asparagine. Also important for glutamate utilization.,Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),glutamate/aspartate transport system substrate-binding protein,MRIVPHILGAAIAAALISTPVFAAELTGTLKKIKESGTITLGHRDASIPFSYIADASGKPVGYSHDIQLKIVEAIKKDLDMPNLQVKYNLVTSQTRIPLVQNGTVDVECGSTTNNVERQQQVDFSVGIFEIGTKLLSKKDSAYKDFADLKGKNVVTTAGTTSERILKSMNADKQMGMNVISAKDHGESFQMLETGRAVAFMMDDALLAGEMAKAKKPTDWAVTGTAQSNEIYGCMVRKGDAPFKKAVDDAIIATYKSGEINKIYEKWFMQPIPPKGLNLMFPMSEELKALIANPTDKAADEKKS transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_772,,A1D17_RS08650,WP_008069477.1,tr|A0A166MU26|A0A166MU26_PSEFL,"ABC transporter for L-asparagine and L-glutamate, permease component 1","# Specifically important in carbon source L-Asparagine. Also important for glutamate utilization. We do not have fitness data for aspartate utilization - it may well transport aspartate as well. Also note that the aspariganase (Pf1N1B4_2023) is predicted to be cytoplasmic and is important for asparagine utilization, so we do not think that asparagine is converted to aspartate before uptake.",Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),glutamate/aspartate transport system permease protein,MNYNWDWGVFFKSTGVGSEIYFDWYLSGLGWTIAIAVAAWIIALLLGSILGVMRTVPNRIVSGIATCYVELFRNVPLLVQLFIWYFLVPDLLPADIQEWYKQDLNPTTSAFLSVVVCLGLFTTARVCEQVRTGIQALPRGQEAAARAMGFKLPQIYWNVLLPQAYRIIIPPLTSEFLNVFKNSSVASLIGLMELLAQTKQTAEFSANLFEAFTLATLIYFTLNMSLMLLMRSVEKKVAVPGLISVGGK transporters,Pseudomonas fluorescens FW300-N1B4,pseudo1_N1B4,Pf1N1B4_773,,A1D17_RS08655,WP_063341405.1,tr|A0A166MU41|A0A166MU41_PSEFL,"ABC transporter for L-asparagine and L-glutamate, permease subunit 2",# Important for glutamate and asparagine utilization and cofit with other components,Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),glutamate/aspartate transport system permease protein,MEFDFSGIIPSLPGLWNGMIMTLKLMALGVIGGIILGTILALMRLSHNKLLSNIAGAYVNYFRSIPLLLVITWFYLAVPFVLRWITGEDTPIGAFASCIVAFMMFEAAYFCEIVRAGVQSIPKGQMGAAQALGMEYGQMMRLIILPQAFRKMTPLLLQQSIILFQDTSLVYAVGLVDFLNASRASGDIIGRSNEFLIFAGLTYFTISFAASLLVKRLQKRFAV transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_1894,,AO361_RS09435,WP_057713549.1,,"ABC transporter for D-Galactose and D-Glucose, periplasmic substrate-binding component",Specific phenotype on D-Fructose; L-Aspartic Acid; D-Galactose; L-Lysine. The nitrogen source phenotypes for aspartate and lysine are with glucose as the carbon source. May also transport fructose.,"Glucose ABC transport system, periplasmic sugar-binding protein","Glucose ABC transport system, periplasmic sugar-binding protein",multiple sugar transport system substrate-binding protein,MNAISRLATVISLASLSALPLSVLAAESKGSVEVVHWWTSGGEKAAVDVLKAQVEKDGFTWKDGAVAGGGGSTAMTVLKSRAVAGNPPGVAQIKGPDIQEWGSTGLLSTDALKDVSKAENWDGLLSKKVSDTVKYEGDYVAVPVNIHRVNWLWINPEVFKKAGIEKAPTTLEEFYAAGDKLKAAGFIALAHGGQPWQDSTVFEDVVLSVMGADGYKKALVDLDQKTLSGPEMTKSFAELKKITGYMDPNRAGRDWNIAAADVISGKAGMQMMGDWAKSEWTAAKKIAGKDYQCVAFPGTEKAFTYNIDSMAVFKLKADRKGDIAAQQDLAKVALGTDFQKVFSMNKGSIPVRNDMLNEMDKLGFDECAQKSAKDFIADDKTGGLQPSMAHNMATSLAVQGAIFDVVTNFMNDKDADPAKASAQLASAVKAAQ transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_1895,,AO361_RS09440,WP_057713550.1,,"ABC transporter for D-Galactose and D-Glucose, permease component 1",Specific phenotypes on D-Galactose; N L-Lysine. glucose is the C source for the N source experiments with a phenotype (i.e. lysine); may also transport fructose.,"Glucose ABC transport system, inner membrane component 1","Glucose ABC transport system, inner membrane component 1",multiple sugar transport system permease protein,MSSVAVFSKASPFDALQRWLPKLVLAPSMLIVLVGFYGYIIWTFILSFTNSSFMPSYKWVGLQQYMRLMDNDRWWVASKNLALFGGMFISISLVLGVFLAVLLDQRIRKEGFIRTVYLYPMALSMIVTGTAWKWLLNPGLGLDKMLRDWGWEGFRLDWLVDQDRVVYCLVIAAVWQASGFVMAMFLAGLRGVDQSIIRAAQVDGASLPTIYLKIVLPSLRPVFFSAFMILAHIAIKSFDLVAAMTAGGPGYSSDLPAMFMYSFTFSRGQMGIGSASAMLMLGAVLTILVPYLYSELRGKRHD transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_1896,,AO361_RS09445,WP_007974434.1,,"ABC transporter for D-Galactose and D-Glucose, permease component 2",Specific phenotypes on D-Galactose; N L-Lysine. glucose is the C source for the N source experiments with a phenotype (i.e. lysine); may also transport fructose.,"Glucose ABC transport system, inner membrane component 2","Glucose ABC transport system, inner membrane component 2",multiple sugar transport system permease protein,MTNQLGKSGISFSRIAIYATLLLAAAVYLIPLVVMLLTSFKSPEDIRTGNLLSWPTVIDGIGWIKAWDVVGGYFWNSVKITVPAVLISTFIGAMNGYVLSMWRFRGSQLFFGLLLFGCFLPFQTVLLPASFTLGKFGLANTTTGLVLVHVVYGLAFTTLFFRNYYVSIPDALVKAARLDGAGFFTIFLKILLPMSIPIVMVCLIWQFTQIWNDFLFGVVFASGDAQPITVALNNLVNTSTGAKEYNVDMAAAMIAGLPTLLVYIFAGKYFLRGLTSGAVKG transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_1897,,AO361_RS09450,WP_057713551.1,,"ABC transporter for D-Galactose and D-Glucose, ATPase component",Specific phenotype on D-Galactose; L-Lysine; L-Aspartic Acid. The nitrogen source phenotypes for aspartate and lysine are with glucose as the carbon source. May also transport fructose.,"Glucose ABC transporter, ATP-binding subunit (EC 3.6.3.-)","Glucose ABC transporter, ATP-binding subunit (EC 3.6.3.-)",multiple sugar transport system ATP-binding protein,MATLELRNVNKTYGPGLPDTLKNIELKIDDGEFLILVGPSGCGKSTLMNCIAGLETISGGAILVDDADISGMSPKDRDIAMVFQSYALYPTMSVRDNIAFGLKIRKMPTAEIDEEVARVSKLLQIEHLLSRKPGQLSGGQQQRVAMGRALARRPKIYLFDEPLSNLDAKLRVEMRTEMKLMHQRLKTTTVYVTHDQIEAMTLGDKVAVMKDGIIQQFGTPKDIYNNPANLFVASFIGSPPMNFIPLRLQRKDGRLLALLDSGQARCELPLGMQDAGLEDREVILGIRPEQIILANGEANGLPTIRAEVQVTEPTGPDTLVFVNLNDTKVCCRLAPDVAPAVGETLTLQFDPAKVLLFDAKTGERLGVAGVPKAEAHADNVAQFKGR catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_2119,,AO361_RS10555,WP_057713727.1,,L-arabinose 1-dehydrogenase / D-galactose 1-dehydrogenase (EC 1.1.1.46; EC 1.1.1.48),"Specifically important for: D-Galactose; L-Arabinose. Both of these sugars are catabolized via a 1-dehydrogenase followed by lactonase and dehydratase reactions. This is the only dehydrogenase is specifically important for either of these carbon sources in several Pseudomonas, except for an alpha-ketoglutarate semialdehyde dehydrogenase that is expected to be the last dedicated step in L-arabinose catabolism (i.e., Pf6N2E2_612). L-arabinose and D-galactose are chemically similar and some dehydrogenases are already known to act on both substrates.",3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),,MAEPLSLPPVPEPPRGERLKNKVVLLTGAAQGIGEAIVATFASQQARLIISDIQGEKVEKVAAHWRDQGADVVAIKADVSRQQDLHAMARLAIDLHGRIDVLVNCAGVNVFRDPLEMTEEDWRRCFAIDLDGAWYGCKAVLPQMIEQGIGSIINIASTHSTHIIPGCFPYPVAKHGLLGLTRALGIEYAPKGVRVNAIAPGYIETQLNVDYWNGFADPHAERQRAFDLHPPRRIGQPIEVAMTAVFLASDEAPFINASCITIDGGRSVMYHD catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_2867,,AO361_RS14290,WP_057714593.1,,D-glucosaminate dehydratase (EC 4.3.1.9),"Specifically important for: D-Glucosamine Hydrochloride. This enzyme had not previously been linked to a gene. This is the second step in catabolism of glucosamine, and the 'beta' form of the enzyme was expected to be PLP-dependent and about this size. Iwamoto et al (2003) purified a non-specific 'alpha' enzyme for this reaction (PMID: 12686150)",D-serine deaminase (EC 4.3.1.18),D-serine deaminase (EC 4.3.1.18),,MSSAPNTAAVEKGTAPKGASLVRDVSLPALVLHREALEHNIRWMQAFVSDSGAELAPHGKTSMTPALFRRQLDAGAWGITLASATQTRAAYAHGVRRVLMANQLVGTPNMALIADLLADPTFDFYCMVDHPDNVADLGAYFASRGVRLNVMIEYGVVGGRCGCRSEQQVLDLAKAIAAQPALALTGIEGYEGVIHGDHAVTGIRDFAASLVRLAVQLQDSGAFAIPKPIITASGSAWYDLIAESFEAQNAAGRFLSVLRPGSYVAHDHGIYKEAQCCVLDRRSDLHEGLRPALEVWAHVQSLPEPGFAVIALGKRDVAYDAGLPVPLLRYKAGVVPAEGDDVSVCKVTAVMDQHAFMTVAPGVDLRVGDIISFGTSHPCLTFDKWRVGCLVDEQLNVIETMETCF catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_2985,,AO361_RS14885,WP_057714718.1,,isobutyrl-CoA dehydrogenase (EC 1.3.8.1),"Specifically important for: L-Valine. Isobutyryl-CoA is an intermediate in valine degradation. SEED annotates it as butyryl-CoA dehydrogenase, which is also expected to perform this reaction",Butyryl-CoA dehydrogenase (EC 1.3.99.2),Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MHDIELSEEQVMIRDMARDFARGEIAPHAQAWEKAGWIDDGLVAKMGELGLLGMVVPEEWGGTYVDYVAYALAVEEISAGDGATGAFMSIHNSVGCGPVLNYGSEEQKQTWLADLASGQVIGCFCLTEPQAGSEAHNLRTRAELRDGQWVINGAKQFVSNGKRAKLAIVFAVTDPDLGKRGISAFLVPTDTAGFIVDRTEHKMGIRASDTCAVTLNNCTIPEANLLGERGKGLAIALSNLEGGRIGIAAQALGIARAAFEAALAYSRDRVQFGKAINEHQSIANLLADMHMQLNAARLMILHAARLRTAGKPCLSEASQAKLFASEMAEKVCSSAIQIHGGYGYLEDYPVEKYYRDARITQIYEGSSEIQRMVIARELKNYLI transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_3039,,AO361_RS15130,WP_057714764.1,,"ABC transporter for D-Mannitol, D-Mannose, and D-Mannose, ATPase component",Specific phenotype on D-Mannitol; D-Mannose.,"Various polyols ABC transporter, ATP-binding component","Various polyols ABC transporter, ATP-binding component",sorbitol/mannitol transport system ATP-binding protein,MANLKIKNLQKGFEGFSIIKGIDLEVNDKEFVVFVGPSGCGKSTLLRLIAGLEEVSGGTIELDGRDITEVSPAKRDLAMVFQTYALYPHMSVRKNMSFALDLAGVAKAEVEKKVSEAARILELGPMLERKPKQLSGGQRQRVAIGRAIVRNPKIFLFDEPLSNLDAALRVQMRLELLRLHKELQATMIYVTHDQVEAMTMADKVVVLNGGKIEQVGSPLDLYHQPANLFVAGFLGTPKMGFLKGKITRVDSQGCEVQLDAGTRVSLPLGGRHLSVGSAVTLGIRPEHLELAKPGDCALQVTADVSERLGSDTFCHVRTASGEALTMRVRGDLASRYGETLSLHLDAQHCHLFDADGVALTRPLRVAA transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_3040,,AO361_RS15135,WP_057714765.1,,"ABC transporter for D-Mannitol, D-Mannose, and D-Mannose, permease component 1",Specific phenotypes on D-Mannitol; D-Mannose; D-Mannose,"Various polyols ABC transporter, permease component 2","Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MTLQQSRRLQSLLLGTLAWAIAILIFFPIFWMVLTSFKTEIDAFATPPQFIFMPTLENYLHINERSDYFSFAWNSVVISFSATALCLLIAVPAAYSMAFYETQRTRGTLLWMLSTKMLPPVGVLMPIYLLAKSFGLLDTRIALIVIYTLINLPIVVWMIYTYFKDIPRDILEAARLDGATLAQEMLRVLLPIAKGGLASTVLLSLILCWNEAFWSLNLTSSKAAPLTALIASYSSPEGLFWAKLSAVSTLACAPILIFGWISQKQLVRGLSFGAVK transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_3041,,AO361_RS15140,WP_046042047.1,,"ABC transporter for D-Mannitol, D-Mannose, and D-Mannose, permease component 2",Specific phenotypes on D-Mannitol; D-Mannose; D-Mannose,"Various polyols ABC transporter, permease component 1","Various polyols ABC transporter, permease component 1",sorbitol/mannitol transport system permease protein,MDIAQPQRKSRVANPGWFLVSPSVALLLLWMIVPLGMTVYFSTIRYNLLNPGENEFVGLENFTYFLTDSGFLPGATNTLLLVGSVLLISVVFGVLISALLEASEFFGRGIVRVMLISPFFIMPTVGALIWKNLIFHPVSGILAYVWKLFGAQPVDWLAHYPLLSIIIIVSWQWLPFAILILMTAMQSLDQEQKEAARLDGAGPIAIFWHLTLPHLARPIAVVVMIETIFLLSVFAEIFTTTNGGPGYASTNLAYLIYNQALVQFDVGMASAGGLIAVVIANIAAIILVRMIGKNLTDKA transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_3042,,AO361_RS15145,WP_057714766.1,,"ABC transporter for D-Mannitol, D-Mannose, and D-Mannose, periplasmic substrate-binding component",Specific phenotype on D-Mannose; D-Mannitol.,"Various polyols ABC transporter, periplasmic substrate-binding protein","Various polyols ABC transporter, periplasmic substrate-binding protein",sorbitol/mannitol transport system substrate-binding protein,MQPSVKALLALTCMTLSSVSLGAQTLTIATVNNSDMIRMQKLSKTFEAEHPDIKLNWVVLEENVLRQRLTTDIATQGGQFDVLTIGMYEAALWGAKGWLEPMKDLPAGYALDDVFASVREGLSVKGQLYALPFYAESSITYYRTDLFKDAGLTMPERPTWEQIGEFAAKLNKPEQEQYGICLRGKAGWGENIALISTVANAYGARWFDEKWQPEFNGPEWKNALNFYVDTMKKSGPPGASSNGFNENLALFNSGKCAIWVDASVAGSFVTDKSQSKVTDHVGFTYAPHQVTDKGSAWLYSWALAIPTSSKAKDAAKTFSAWATSKEYGALVAEKDGIANVPPGTRASTYSEAYMSAAPFARVTLESLKAADPSKPGTRPVPYIGIQLVTIPEFQGIGTQVGKSFSAALIGQTTVDQALAAAQQTTEREMKRAGYPK catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_3314,,AO361_RS16495,WP_057715013.1,,D-galactono-lactonase (EC 3.1.1.-),"Specifically important for: D-Galactose. A 1,5-lactone is probably the product of D-galactose dehydrogenase (PfGW456L13_2119), althought it might rearrange to a 1,4-lactone. Related to E. coli ybhE or pgl, which is 6-phospho-D-glucono-1,5-lactonase.",3-carboxymuconate cyclase,3-carboxymuconate cyclase,,MRNLWPLLMAGSIGAMGVQVASAEDYQLLVGSYTAGQSQGIYRLAFDSRTGQIDASPLQVIKSANPSWLTLSKDQRHLFVVNENGPGQTDPVGRVSSFAIDPKTHALSLISQVQSLGNEPTHSSLSIDGSHLFVSNYSVAEDPGGTLAVLPVAADGKLKAVVQMSSHPASRVNPERQASAHVHSTIPSPDGRYVFANDLGADKVFAYRFDPKANPELPLTPATPAFVQLPPGSGPRHLLFSADGKHAWLTMEMSAQVAVFDYHDGQLEQTQMVDLAAGQPVSDKAAAALHASADGKFLYVSNRGTANQLLVFAIDPATGHLSELQRRAVEGDHPREFSLDPSGKFLLIANQKSNQIVVVERDARTGLLGKTVQKLPMDAPSDLRFLLRQ catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_3317,,AO361_RS16510,WP_057715016.1,,2-dehydro-3-deoxy-L-arabinonate dehydratase (EC 4.2.1.43),"# Specifically important in carbon source L-Arabinose. Similar to PA2216 from Pseudomonas aeruginosa (see PMC:PMC4038344) and to gguC or araD1 (Atu2345) from Agrobacterium tumefaciens (see PMC: PMC3232879), which also have this activity. In Agrobacterium, this reaction is proposed to be a step in L-arabinose oxidation. (Note that 2-keto-3-deoxy-L-lyxonate and 2-keto-3-deoxy-L-arabinonate are the same compound.)",SUGAR TRANSPORTER,SUGAR TRANSPORTER,,MRLVQFELSNGERRVGVVEAGLVREVQDTRTVRDLALAAIEASASLEQQVQSLGLGISHDYAELLTQRRILPPLDHADPAHMLVSGTGLTHLGSASARDKMHQQSGDETAMTDTMRIFKWGVEGGKPAVDEAGVQPEWFYKGDGGIVVRPGQPFPLPPFAEDAGEEPEIAGLYVIGHDGKPYRLGFAVGNEFSDHVMERKNYLYLAHSKLRSCSYGPELRVGELPQHLAGTSRILRDGEVLWQNEFLSGEANMCHSLANLEFHHFKYSQFLRPGDVHIHFFGTATLSFADGIRTQPGDVFEITQAEFGAPLINGIAPVAAAFEPGTVGTL catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_4396,,AO361_RS21975,WP_057715924.1,,tyrosine aminotransferase (EC 2.6.1.57),Specifically important for: L-Phenylalanine. Phenylalanine is hydroxylated to tyrosine (by PfGW456L13_4394) before deamination. KEGG has same EC# but less specific,Aspartate aminotransferase (EC 2.6.1.1),Aspartate aminotransferase (EC 2.6.1.1),aromatic-amino-acid transaminase,MHFDAIGRVPGDPILGLMEAYAQDPNPRKFDLGVGVYKDAQGLTPILESVKLAEQRLVDHQSTKTYIGGHGEPAFGKAINELVLGADSKLISAQRAGATQTPGGTGALRLSADFIAQCLPGRGVWLSNPTWPIHETIFAAAKVKVSHYPYVGSDNRLDVDGMLAALNEAPKGDVVLLHACCHNPTGFDLSQDDWQRVLEVVRKRELLPLIDFAYQGFGDGLEQDAWSTRLFAAELPELLITSSCSKNFGLYRDRTGALIVCAKTADKLVDIRSQLANIARNLWSTPPDHGAAVVATILGDPELKRLWADEVEAMRLRIAQLRSGLVEALEPHGLRERFAHIGVQRGMFSYTGLSPEQVKNLRDHHSVYMVSSGRANVAGIDATRLDLLAEAIASVCK transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_4770,,AO361_RS23845,WP_046041277.1,,"ABC transporter for L-Asparagine and possibly other L-amino acids, periplasmic substrate-binding component",Specific phenotype on L-Asparagine; Sodium octanoate. Detrimental on glutamine and might leak it. The mild phenotype on octanoate is not explained.,Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),glutamate/aspartate transport system substrate-binding protein,MRIVPHILGAAIAAALISTPVFAAELTGTLKKIKESGTITLGHRDASIPFSYIADASGKPVGYSHDIQLKVVEALKKDLDMPNLQVKYNLVTSQTRIPLVQNGTVDLECGSTTNNVERQQQVDFSVGIFEIGTRLLSKADSKYKDFPDLAGKNVVTTAGTTSERILKAMNADKQMGMNVISAKDHGESFQMLETGRAVAFMMDDALLAGEAAKAKKASDWAVTGTPQSYEIYGCMVRKGDEPFKKAVDDAIKATYASGEINKIYEKWFMQPIPPKGLNLNFPMSDELKALIAKPTDKAADDKKS transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_4771,,AO361_RS23850,WP_057716225.1,,"ABC transporter for L-Asparagine and possibly other L-amino acids, permease component 1","Specific phenotypes on L-Asparagine; L-Asparagine. also has weak phenotypes on other a.a., but not on aspartate; weak phenotype on glutamate; also is detrimental during growth on glutamine (along with a downstream HK-RR), which could imply that it leaks glutamate; ko:K10003 : glutamate/aspartate transport system permease protein",Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),glutamate/aspartate transport system permease protein,MNYNWDWGVFFKSTGVGSETYFDWYVTGLAWTIGIAIAAWIIALTLGSILGVMRTVPNRIVSGIATCYVELFRNVPLLVQLFIWYFLVPDLLPADLQEWYKQDLNPTTSAFLSVVVCLGLFTTARVCEQVRTGIQALPKGQESAARAMGFKLPQIYWNVLLPQAYRIIIPPLTSEFLNVFKNTSVASLIGLMELLAQTKQTAEFSANLFEAFTLATLIYFTLNMSLMLLMRVVEKKVAVPGLISVGGK transporters,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_4772,,AO361_RS23855,WP_057716226.1,,"ABC transporter for L-Asparagine and possibly other L-amino acids, permease component 2","Specific phenotypes on L-Asparagine; L-Asparagine. also has weak phenotypes on other a.a., but not on aspartate; weak phenotype on glutamate; also is detrimental during growth on glutamine (along with a downstream HK-RR), which could imply that it leaks glutamate; K10002 : glutamate/aspartate transport system permease protein",Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),glutamate/aspartate transport system permease protein,MEFDFSGIIPSLPGLWNGMIMTLKLMAMGVIGGIILGTILALMRLSHNKVLSNIAGAYVNYFRSIPLLLVITWFYLAVPFVLRWITGEDTPIGAFASCIVAFMMFEAAYFCEIVRAGVQSIPKGQMGAAQALGMSYGQMMRLIILPQAFRKMTPLLLQQSIILFQDTSLVYAVGLVDFLNASRASGDIIGRSNEFLIFAGLVYFIISFAASQLVKRLQKRFAV catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_805,,AO361_RS03965,WP_057712751.1,,gamma-glutamyl-gamma-aminobutyraldehyde dehydrogenase (EC 1.2.1.54),Specifically important for: Putrescine Dihydrochloride. Part of the gamma-glutamyl-putrescine pathway. Annotated as aldehyde dehydrogenase in SEED and as the 4-guanidino enzyme in KEGG (based on studies of PA5312 = kauB). Since this enzyme is closely related to kauB it is probably flexible.,Aldehyde dehydrogenase (EC 1.2.1.3),Aldehyde dehydrogenase (EC 1.2.1.3),4-guanidinobutyraldehyde dehydrogenase / NAD-dependent aldehyde dehydrogenase,MTTLTRADWEQRARDLKIEGRAYINGEYTDAVSGETFECISPVDGRLLGKIASCDAADAQRAVENARATFNSGVWSRLAPTKRKSTMIRFAGLLKQHAEELALLETLDMGKPISDSLYIDVPGAAQALSWSGEAIDKIYDEVAATPHDQLGLVTREPVGVVGAIVPWNFPLMMACWKLGPALSTGNSVILKPSEKSPLTAIRIAELAVEAGIPKGVLNVLPGYGHTVGKALALHNDVDTLVFTGSTKIAKQLLIYSGESNMKRVWLEAGGKSPNIVFADAPNLQDAAEAAAGAIAFNQGEVCTAGSRLLVERSIKDKFLPLVIEALKAWKPGNPLDPATNVGALVDTQQMNTVLSYIESGHADGARLVAGGKRTLQETGGTYVEPTIFDGVSNAMKIAQEEIFGPVLSVIEFDSAEEAIAIANDTPYGLAAAVWTADISKAHLTARALRAGSVWVNQYDGGDMTAPFGGFKQSGNGRDKSLHAFDKYTELKATWIKL catabolism,Pseudomonas fluorescens GW456-L13,pseudo13_GW456_L13,PfGW456L13_925,,AO361_RS04565,WP_053163627.1,,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Specifically important for: Putrescine Dihydrochloride. The first step in putrescine catabolism. (SEED_correct),Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),glutamine synthetase,MSVPPRAVQLNEANAFLKEHPEVLYVDLLIADMNGVVRGKRIERTSLHKVYEKGINLPASLFALDINGSTVESTGLGLDIGDADRICYPIPDTLCNEPWQKRPTAQLLMTMHELEGEPFFADPREVLANVVRKFDEMGLTICAAFELEFYLIDQENVNGRPQPPRSPVSGKRPHSTQVYLIDDLDEYVDCLQDILEGAKEQGIPADAIVKESAPAQFEVNLHHVADPIKACDYAVLLKRLIKNIAYDHEMDTTFMAKPYPGQAGNGLHVHISILDKDGKNIFASEDPEQNAALRHAIGGVLETLPAQMAFLCPNVNSYRRFGAQFYVPNSPCWGLDNRTVAIRVPTGSADAVRIEHRVAGADANPYLLMASVLAGVHHGLTNKIEPGAPVEGNSYEQNEQSLPNNLRDALRELDDSEVMAKYIDPKYIDIFVACKESELEEFEHSISDLEYNWYLHTV transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_03040,AO353_03040,AO353_RS03040,WP_019693166.1,tr|A0A0N9W776|A0A0N9W776_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, ATPase component",Specific phenotype on L-Arginine. Also important on citrulline.,amino acid transporter,"Arginine/ornithine ABC transporter, ATP-binding protein AotP",,MYKLEVQDLHKRYGSHEVLKGVSLKAAAGDVISIIGSSGSGKSTFLRCINLLEQPHAGKILLNNEELKLVANKDGALKAADPKQLQRMRSRLSMVFQHFNLWSHMTAMENIMEAPVHVLGMSKAEAREKAELYLAKVGVSHRKDAYPGHMSGGEQQRVAIARALAMEPEVMLFDEPTSALDPELVGDVLKVMQALAQEGRTMVVVTHEMGFAREVSNQLVFLHKGVVEESGNPREVLVNPQSERLQQFLSGSLK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_03045,AO353_03045,AO353_RS03045,WP_019693167.1,tr|A0A0N9W128|A0A0N9W128_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, permease component 1",Specific phenotypes on L-Arginine; L-Arginine. also important for citrulline,amino acid ABC transporter permease,"Arginine/ornithine ABC transporter, permease protein AotM",arginine/ornithine transport system permease protein,MIFDYNVIWEALPLYFGGLVTTLKLLALSLLFGLLAALPLGLMRVSKQPIVNMSAWLYTYVIRGTPMLVQLFLIYYGLAQFEAVRESFLWPWLSSATFCACLAFAINTSAYTAEIIAGSLRATPNGEIEAAKAMGMSRFKMYKRILLPSALRRALPQYSNEVIMMLQTTSLASIVTLIDITGAARTVNAQYYLPFEAYITAGVFYLCMTFILVRLFKMAEHRWLGYLAPRKH transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_03050,AO353_03050,AO353_RS03050,WP_019693168.1,tr|A0A0N9WAY7|A0A0N9WAY7_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, permease component 2",Specific phenotypes on L-Arginine; L-Arginine; L-Arginine. possibly citrulline as well,ABC transporter,"Arginine/ornithine ABC transporter, permease protein AotQ",,MLKGYGAVILDGAWLTLQLALSSMALAIVLGLIGVALRLSPIRWLARLGDLYSTVIRGIPDLVLILLIFYGGQDLLNRVAPLLGYDDYIDLNPLVAGIGTLGFIFGAYLSETFRGAFMAIPKGQAEAGAAYGMSSFQVFFRVLVPQMIRLAIPGFTNNWLVLTKATALISVVGLQDMMFKAKQAADATREPFTFFLAVAAMYLVITSVSLLALRHLEKRYSVGVRAADL transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_03055,AO353_03055,AO353_RS03055,WP_054593623.1,tr|A0A0N7GZD9|A0A0N7GZD9_PSEFL,"ABC transporter for L-Arginine and L-Citrulline, periplasmic substrate-binding component",Specific phenotype on L-Arginine; L-Citrulline.,ABC transporter substrate-binding protein,"Arginine/ornithine ABC transporter, periplasmic arginine/ornithine binding protein",arginine/ornithine transport system substrate-binding protein,MKKLVLLGALALSVLSLPTFADEKPLKIGIEAAYPPFASKAPDGSIVGFDYDIGNALCEEMKVKCVWVEQEFDGLIPALKVRKIDAILSSMSITDDRKKSVDFTNKYYNTPARLVMKAGTQVSDNLAELKGKKIGVQRGSIHNRFAEEVLKPLGAEIKPYGSQNEIYLDVAAGRLDGTVADATLLDDGFLKTDSGKGFAFVGPAFTDEKYFGDGIGIAVRKGDKAELDKINAAIVAIRANGKYKQIQDKYFNFDIYGK catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_03400,AO353_03400,AO353_RS03400,WP_054593685.1,tr|A0A0N9WB79|A0A0N9WB79_PSEFL,Isomerase for D-mannose and D-mannitol catabolism (probably D-mannose-6-phosphate isomerase or D-mannose isomerase),"Specifically important for: D-Mannitol; D-Mannose. This has a conserved phenotype on mannose and mannitol (which is probably catabolized via mannose or mannose-6-P, although we did not identify the dehydrogenase). Some homologs are annotated as D-mannose isomerase or D-mannose-6-P isomerase. We suspect that the putative fructokinase (AO353_25910) is actually mannose kinase, which would complete the pathway for mannose utilization if this is D-mannose-6-P isomerase (forming fructose-6-phosphate). Also, some close homologs of the 'fructokinase' are detrimental during growth on fructose, which suggests that it is not actually fructokinase. However, data for close homologs suggests D-mannose as the substrate of this enzyme, so the exact role is uncertain. (Also, another gene is annotated as a potential mannose-6-P isomerase (AO353_13390) but is not important for utilizing mannose or mannitol.)",sugar isomerase,N-acylglucosamine 2-epimerase (EC 5.1.3.8),,MNTAPPAFSSWLNAPAHQQWLADEGLRLLAFAKASKLPEGFGNLDEHGQLPADAQAHTMNTARMTHSFAMAHIQGLPGFAELVDHGINALNGLLRDAEFGGWFAAPDHRDGDTGKAAYLHAFVALAASSAVIAQRPGAEALLNDAIAIIEQHFWSEEEGAMRESFNRDWSEEEPYRGANSNMHATEAFLALADVTQDSRWLNRALRIVERVIHRHAASNDHLVVEHFDRDWQPLRDYNQANPADHFRPYGTTPGHGFEWSRLLLHLEAARGQAGMLTPGWLLSDAQQLFANNCLHGWDVDGAPGIVYTLDWDNRPVVRQRLHWVHCEASAAASVLLKRTGEAQYETWYRRFWEFSDRCLIDRIHGSWHHELDPQNRPSTEIWGGKPDLYHAWQAVLIPRLPLAPSMASALAQGSVSVLM transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_03810,AO353_03810,AO353_RS03810,WP_054593749.1,tr|A0A0N9WF03|A0A0N9WF03_PSEFL,"Alpha-ketoglutarate permease, MFS superfamily","Specific phenotype on a-ketoglutarate. 86% identical to the a-ketoglutarate transporter in P. aeruginosa (PA5530, see PMCID: PMC4097582) and 56% similar to E. coli kgtP. (KEGG_correct)",MFS transporter,Dicarboxylate MFS transporter,"MFS transporter, MHS family, alpha-ketoglutarate permease",MDNSQTLPLGSAAVPAKEKTTASRIKSIFSGSVGNMVEWYDWYVYAAFSLYFAKAFFPKGDTTAQLLNTAAIFAVGFLMRPIGGWLMGLYADRAGRKAALMASVYLMCFGSLIIALSPGYETIGVGAPILLVFARLLQGLSVGGEYGTSATYLSEMATKERRGFFSSFQYVTLISGQLIALGVLIVLQQTLTTEQLYDWGWRIPFAIGALCAIVALYLRRGMEETESFAKKEKSKESAMRTLLRHPKELMTVVGLTMGGTLAFYTYTTYMQKYLVNTVGMSISDSTTISAATLFLFMCLQPIIGGLSDKVGRRPILIAFGILGTLFTVPILTTLHTIQTWWGAFFLIMAALIIVSGYTSINAVVKAELFPTEIRALGVGLPYALTVSIFGGTAEYIALWFKSIGMETGYYWYVTACIAVSLLVYVTMKDTRKHSRIETD transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_04460,AO353_04460,AO353_RS04460,WP_054593864.1,tr|A0A0N9WFD8|A0A0N9WFD8_PSEFL,"N-acetylglucosamine-specific PTS system, I, HPr, and IIA components (nagF)",Specifically important for NAG utilization. (SEED_correct),PTS N-acetyl-D-glucosamine transporter,"PTS system, glucose-specific IIA component / Phosphotransferase system, phosphocarrier protein HPr / Phosphoenolpyruvate-protein phosphotransferase of PTS system (EC 2.7.3.9)",phosphocarrier protein,MHNNNKDLTLSAPLSGPVLTLAKVPDPVFASGAMGDGIAIDPLNNTLHAPCAGVVVHVARTGHAVTLRADNGAELLLHLGLDTVELQGEGFSMLVKEGTRVSNGQALLRFDLDQVAQGCKSLVSLLVLTNSEDFQVLPITLKSVKVGEPLLHIVPRTTHSAQVEADSSGAEVHGHIRIIHRGGLHARPAALIRQTAHLFNSKSQLHFAGKSASCDSLIGLMGLGIGEQDEVQVSCKGADAKAALQALLNALSTAVNDDSHAAAPTPIAQRTRTAEAGVLNGVCAAPGLVGGPLFQLAAIPLPEDTGKHNAEEQLQALDRALEQVRSEIRETLSHAKKHKHTEEEQIFAAHLALLEDPALLEAAIQSIDQGSAATHAWSQSIEAQCEVLQQLGNPLLAERANDLRDLRQRVLRALLGQDWHYDVPAGAIVAAHELTPSDLLQLSQQGVAGLCMAEGGATSHVAILARGKGLPCLVALSASLLQQPQGQSVVLDADGGRLELTPDSQRLEQVAQAQREHLQRRERQQAQAHTPAHTRDGLRIEVAANVASSNEAADALKGGADGVGLLRTEFLFVDRQTAPDEQEQRQAYQAVLDAMGDKSVIIRTIDVGGDKQLDYLPLPAEANPVLGLRGIRMAQVRPELLDQQLRALLQVSPLQRCRILLPMVTEVDELLYIRQRLDALCAELALTQRLELGVMIEVPAAALLAEQLAEHADFLSIGTNDLSQYTLAMDRDHAGLAARVDALHPALLRLIAQTCIGAAKHQRWVGVCGALASDPLATPVLIGLGISELSVSPPQVGEIKERVRQLDAADCRRFSATLLNLSSATAVRHACHQHWPLS transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_04465,AO353_04465,AO353_RS04465,WP_054593865.1,tr|A0A0N9VKH1|A0A0N9VKH1_PSEFL,"N-acetylglucosamine-specific PTS system, IIBC components (nagE)",Specifically important for NAG utilization. SEED incorrectly implies that this contains the IIA component as well. (KEGG_correct),PTS N-acetyl-D-glucosamine transporter,"PTS system, N-acetylglucosamine-specific IIA component (EC 2.7.1.69) / PTS system, N-acetylglucosamine-specific IIB component (EC 2.7.1.69) / PTS system, N-acetylglucosamine-specific IIC component (EC 2.7.1.69)","PTS system, N-acetylglucosamine-specific IIB component ; PTS system, N-acetylglucosamine-specific IIC component",MYQLFIEGLQRLGRALMLPIAILPIAGLLLRLGDTDLLNIAIIHDAGQVIFANLALIFAIGIAVGFARDNNGTAGLAGAIGYLVMVSTLKVLDASINMGMLAGIISGLMAGALYNRFKDIKLPEYLAFFGGRRFVPIATGFSAVGLGVIFGLIWPPIQHGINSFGQLLLESGSIGAFVFGVFNRLLIVTGLHHILNNMAWFIFGSFTDPTTGAIVTGDLARYFAGDPKGGQFMTGMFPMMIFGLPAACLAMYRNALPERRKVMGGIFLSMALTSFLTGVTEPIEFAFMFLAPLLYLLHVLLTGMAMAITNALNIHLGFTFSGGAIDMALGWGKSTNGWLVFPVGLAYAVIYYVVFDFCIRRFNLKTPGREGVVVGEKVVLSENQRAGAYIQALGGAENLITVGACTTRLRLEMVDRNKASDSELKALGAMAVVRPGKGGSLQVVVGPLADSIADEIRQAMPTAGSALVAAVVVTEEAPKAAPVETPEAQKWLNAVGGSDNVLQLDCVAMTRIRLQLADGKALSECQLKDLGCQGVSALDGGVWHLLIGDKALSLSEALEGLVNRSEVSAKV transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_05485,AO353_05485,AO353_RS05485,WP_054594049.1,tr|A0A0N9W2F2|A0A0N9W2F2_PSEFL,"Fructose-specific PTS system, I, HPr, and IIA components","Specifically important for fructose utilization. The enzyme II B/C components of this PTS system are in AO353_05475 which is cofit and is correctly annotated. AO353_05485 is 73% identical to PA3562, which is also part of a fructose-specific system (PMC:PMC2542419)",PTS fructose transporter subunit IIA,Phosphoenolpyruvate-protein phosphotransferase of PTS system (EC 2.7.3.9),phosphocarrier protein,MLELTIEQISMGQSAVDKAAALHLLADTLVNDGLVAEGYLSGLQAREAQGSTFLGQGIAIPHGTPDTRDLVHTTGVRLLQFPEGVDWGDGHIVYLAIGIAAKSDEHLRLLQLLTRALGETDLGQALRRASSAEALLKLLQGAPQELALDAQMIGLGVSADDFEELVWRGARLLRQADCVSNGFSAVLQQVEALPLGDGLWWLHSEQTVKRPGLAFVTPDKPIRYLGQPLSGLFCLASLGEAHQALLERLCALLIEGRGHELGRATSRRAVLEVLGGELPADWPSARIALANAHGLHARPAKILAQLAKSFEGEIRIRIVDGQDSAVSVKSLSKLLSLGARRGQVLELIAEPSIAADALPALLRAIEEGLGEDIEPLPTVSAQSEVIDEITDVVVAPASGCVIQAVAAAPGIAIGPAHIQVLQAIDYPLRGESTAIERERLKTSLADVRRDIEGLIQRSKAKAIREIFITHQEMLDDPELTDEVDTRLKQGESAEAAWMAVIDAAARQQESLQDALLAERAADLRDIGRRVLAQLCGIETPSEPDQPYILVMDEVGPSDVARLDPTRVAGILTARGGATAHSAIVARALGIPALVGAGAAVLRLASGTPLLLDGQRGRLHVDADAATLQRAAEERDNREQRLQAAAAQRHQPALTTDGHAVEVFANIGESAGVVSAVEQGAEGIGLLRTELIFMAHQQAPDEATQEVEYRRVLDGLAGRPLVVRTLDVGGDKPLPYWPIAKEENPFLGVRGIRLTLQRPQIMEAQLRALLRAADNRPLRIMFPMVGSVDEWRQARDMTERLRLEIPVADLQLGIMIEVPSAALLAPVLAKEVDFFSVGTNDLTQYTLAIDRGHPTLSAQADGLHPAVLQLIDITVRAAHAHGKWVGVCGELAADPLAVPVLVGLGVDELSVSARSIGEVKARVRELSLAQVKHLAQLALAVGSANEVRALVEAL catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07485,AO353_07485,AO353_RS07485,WP_054594340.1,tr|A0A0N9WH75|A0A0N9WH75_PSEFL,Acyl-CoA dehydrogenase (EC 1.3.8.7),Specifically important for: Sodium butyrate; Tween 20. The phenotype on Tween 20 suggests that it has activity on longer substrates as well as on butyryl-CoA (SEED_correct),acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MADYKAPLRDMRFVLNEVFEVAKLWAELPALADTVDAETVEAILEEAGKVTSKSIAPLSRAADEEGCHWSDGAVTTPAGFPQAYQTYAEGGWVGVGGDPSYGGMGMPKAVSAQVEEMVNSASLSFGLYPMLTAGACLSINAHASEELKAAYLPNMYAGVWAGSMCLTEPHAGTDLGIIRTKAEPQADGSYKVSGTKIFITGGEHDLTENIIHLVLAKLPDAPAGPKGISLFLVPKFMVNADGSLGARNPANCGSIEHKMGIQASATCVMNFDEAVGYLVGEPNKGLAAMFTMMNYERLGVGIQGLASGERSYQNAVEYARDRLQSRSPTGAQNKDKVADPIIVHPDVRRMLLTMKASNEGGRAFSTYVAMQLDTAKFSEDATIRKRAEDLVALLTPVAKAFLTDLGLETTIHGQQVFGGHGYIREWGQEQLVRDVRITQIYEGTNGIQALDLVGRKIVGSGGAYYKLFADEIRHFTATASADLAEFTKPLNDAVDTLDELTAWLLDRVKNNPNEIGAASVEYLQVFGYVSYAYMWALMAKAAFGKEAQDDFYASKLGTARFYFARLLPRFHSLSASVKAGSESLFLLDAAQF catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07705,AO353_07705,AO353_RS07705,WP_054594379.1,tr|A0A0N7GZP8|A0A0N7GZP8_PSEFL,DgcB Dimethylglycine demethylase subunit B (EC 1.5.3.-),Specifically important for: Carnitine Hydrochloride. Dimethylglycine is an intermediate in L-carnitine metabolism; this gene is similar to PA5399 or dgcB (PMCID: PMC2293255),(Fe-S)-binding protein,"Predicted L-lactate dehydrogenase, Iron-sulfur cluster-binding subunit YkgF",,MLNTLLPILLFAALGLAVLGALRRVAMWRRGRASKVDLIGGLLAMPKRYMVDLHHVVARDKYIANTHVATAGGAVASIVLAILVHGFGLHNRFLGYALLLMTAVMFVGAIFVYLRRRNPPARLSKGPWMRLPKSLLAFSASFFLVTLPVAGILPENFGGWLLAAILGVGVLWGVSELFFGMTWGGPMKHAFAGALHLAWHRRAERFGGGRSTGLKPLDLNDPQAPLGVEKPKDFTWNQLLGFDACVQCGKCEAACPAFAAGQPLNPKKLIQDMVVGLAGGTDAKFAGSPYPGKAIGEHAGNPHQPIVNGLVDAETLWSCTTCRACVEECPMMIEHVDAIVDMRRHLTLEKGATPNKGAEVLENLIATDNPGGFAPGGRMNWAADLNLTLLSEKKSTDVLFWVGDGAFDMRNQRTLRAFVKVLKAAKVDFAVLGLEERDSGDVARRLGDEATFQLLAKRNIQTLAKYSFNRIVTCDPHSFHVLKNEYGAFDGNYLVQHHSTYMAEIIDAGALNLGQHKGDSVTYHDPCYLGRYNGEYEAPRQVLRALGIEVKEMQRSGFRSRCCGGGGGAPITDIPGKQRIPDMRMDDIRETGAELVAVGCPQCTAMLEGVVEPRPLIKDIAELVADALLEDAAPSKSPAPTKRQPAEAH catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07710,AO353_07710,AO353_RS07710,WP_054594380.1,tr|A0A0N9WSS7|A0A0N9WSS7_PSEFL,DgcA Dimethylglycine demethylase subunit A,Specifically important for: Carnitine Hydrochloride. Carnitine is catabolized via glycine betaine (trimethylglycine) and then demethylated twice to sarcosine (N-methylglycine) (PMCID: PMC2293255) (SEED_correct),N-methylproline demethylase,DgcA Dimethylglycine demethylase subunit A,,MAFEAMFQPIQIGKLTIRNRVLSTAHAEVYATDGGMTTDRYVKYYEEKAKGGIGLAICGGSSSVAIDSPQGWWKSVNLADDRIIPHFQNLADAMHKHGAKIMIQITHMGRRSRWDGEHWPTLLSPSGIREPVHRATCKTIEPEEIWRVIGNYASAAARAKAGGLDGVELSAVHQHMIDQFWSPRVNKRTDEWGGSFENRMRFGLEVIKAVRKEVGPDFCVGIRICGDEFHPDGLSHEDMKQIAKYYDDTGMIDFIGVVGSGCDTHNTLANVIPNMSYPPEPFLHLAAGIKEVVKAPVLHAQNIKDPNQATRILEGGYVDMVGMTRAHIADPHLIAKIKMGQIDQIKQCVGANYCIDRQYQGLDVLCIQNAATSREYMGVPHIIEKSTGVKRKVVVVGAGPAGMEAARVAAERGHDVTLFEKKEFIGGQITTASKAPQRDQIAGITRWFQLELARLKVDLRLGVAADAATILDLRPDIVVLAVGGHPFLEQNEHWGAAEGLVVSSWDVLDGKVLPGKNVLVYDTICEFTGMSVADYLADKGSQVEIVTDDIKPGVAIGGTSFPTYYRSMYPKEVIMTGDMMLDKVYREGDKLVAVLENEYTGAKEERVVDQVVVENGVRPDEEIYYALKEGSRNKGQMDIEALFAIKPQPSLSQAGDGYLLFRIGDCVAQRNTHAAIYDALRLCKDF transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07740,AO353_07740,AO353_RS07740,WP_054594384.1,tr|A0A0N9WDQ6|A0A0N9WDQ6_PSEFL,"ABC transporter for Carnitine, substrate-binding component","Specific phenotypes on Carnitine Hydrochloride. Similar to CaiX (see PMID:19919675). This transporter may also have a second SBP, AO353_07780, similar to CbcX, which is reported to be an alternate SBP for choline or glycine utilization (ibid). However _07780 is also important for carnitine utilization - it might be an additional necessary subunit of this transporter.",glycine/betaine ABC transporter substrate-binding protein,L-proline glycine betaine binding ABC transporter protein ProX (TC 3.A.1.12.1),glycine betaine/proline transport system substrate-binding protein,MKRLISSCVLALSGTAFLSSGAMAADPAACQNVRMGVVNWTDVIATSAMTQVLLDGLGYKTKQTSASQQIIFAGIRDQRLDMFLGYWNPLMTQTITPFVAGKQVTVLSEPSLKDARATLAVPTYLADKGLKTFADIAKFEKELGGKIYGIEPGSGANTQIKEMIAKNQFGLGKFQLVESSEAGMLAAVDRAVRRNEAVVFFGWAPHPMNVNVKMTYLTGSQDALGPNEGSATVWTVTAPNYASQCPNVSRLLSNLTFTAEDESRMMQPLLDHKDAFESAKQWLKDHPQDKQRWLEGVTTFDGKPAAENLQLSSQ catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07750,AO353_07750,AO353_RS07750,WP_054594385.1,tr|A0A0N9VS13|A0A0N9VS13_PSEFL,carnitine 3-dehydrogenase [EC:1.1.1.108],Specifically important for: Carnitine Hydrochloride. Not sure why SEED missed this: some very similar proteins are annotated correctly. pba:PSEBR_a5237 has been updated in KEGG and is now annotated as the carnitine dehydrogenase. (KEGG_correct),3-hydroxybutyryl-CoA dehydrogenase,,3-hydroxybutyryl-CoA dehydrogenase,MSFITEIKTFAALGSGVIGSGWVSRALAHGLDVVAWDPAPGAEVALRKRVANAWGALEKQGLAPGASQDRLRFVATIEECVRDADFIQESAPERLELKLELHSKISAAAKPNALIGSSTSGLLPSEFYEGSTHPERCVVGHPFNPVYLLPLVEVVGGKNTAPEAVQAAMKVYESLGMRPLHVRKEVPGFIADRLLEALWREALHLVNDGVATTGEIDDAIRFGAGLRWSFMGTFLTYTLAGGDAGMRHFMAQFGPALQLPWTYLPAPELTDKLIDDVVDGTSDQLGKHSISALERYRDDCLLAVLEAVKTTKAKHGMTFSE catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07755,AO353_07755,AO353_RS07755,WP_054594386.1,tr|A0A0N9WGA2|A0A0N9WGA2_PSEFL,betainyl-CoA thiolase (EC 3.1.2.-),"Specifically important for: Carnitine Hydrochloride. This is part of the degradation of carnitine (K. Bastard et al, Nature Chem Biol. 2014). Since KEGG had this EC number you could argue that it is correct, but it made a very vague prediction.",4-hydroxybenzoyl-CoA thioesterase,,acyl-CoA thioester hydrolase,MPALTTYTTKIIPDWVDYNGHLRDAFYLLIFSYATDALMDQLGMDSNNREASGNSLFTLELHLNYLHEVKLGAEVEVHTQIIGHDRKRLHLYHSLHLVGEEQELAGNEQMLLHVDLAGPRSAPFSESVLNKLRAMSALQSDLPTPAYIGRVIALPPEK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07785,AO353_07785,AO353_RS07785,WP_054594390.1,tr|A0A0N7GZQ0|A0A0N7GZQ0_PSEFL,"ABC transporter for Carnitine, permease component",Specific phenotypes on Carnitine Hydrochloride. no phenotype in choline stress; annotated as glycine betaine or proline transport. Another issue is that carnitine might be oxidized to choline which is then excreted (MetaCyc Pathway: L-carnitine degradation II) but this would not support growth with carnitine as either C or N source (no C or N extracted). Note that both carnitine and choline are catabolized via glycine betaine and this is clustered with glycine betaine degradation genes. This ABC transporter might also transport choline or glycine betaine with a different SBP (AO353_07780).,choline ABC transporter permease subunit,L-proline glycine betaine ABC transport system permease protein ProW (TC 3.A.1.12.1),glycine betaine/proline transport system permease protein,MLIDQKIPLGQYIAAFVEWLTQHGASTFDAIATTLETMIHGVTFALTWFNPLALIGLIALLAHFIQRKWGLTAFVIASFLLILNLGYWQETMETLAQVLFATFVCVIIGVPLGIVAAHKPMFYTMMRPVLDLMQTVPTFVYLIPTLTLFGLGVVPGLISTVVFAIAAPIRLTYLGIRDVPQELMDAGKAFGCSRRQLLSRIELPHAMPSIAAGITQCIMLSLSMVVIAALVGADGLGKPVVNALNTADIALGFEAGLAIVLLAIMLDRICKQPDAKVGGDA transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_07790,AO353_07790,AO353_RS07790,WP_054594391.1,tr|A0A0N9WA42|A0A0N9WA42_PSEFL,"ABC transporter for Carnitine, ATPase component",Specific phenotypes on Carnitine Hydrochloride. Note that both carnitine and choline are catabolized via glycine betaine and this is clustered with glycine betaine degradation genes. This ABC transporter might also transport choline or glycine betaine with a different SBP (AO353_07780).,choline ABC transporter ATP-binding protein,L-proline glycine betaine ABC transport system permease protein ProV (TC 3.A.1.12.1),glycine betaine/proline transport system ATP-binding protein,MSIIRFDNVDVIFSKDPREALKLLDQGMTRDQILKKTGQIVGVEKASLDIEKGEICVLMGLSGSGKSSLLRCINGLNTVSRGKLFVEHEGRQIDIASCTPAELKMMRTKRIAMVFQKFALMPWLTVRENISFGLEMQGRPEKERRQLVDEKLELVGLTQWRNKKPDELSGGMQQRVGLARALAMDADILLMDEPFSALDPLIRQGLQDELLELQRKLHKTIVFVSHDLDEALKLGSRIAIMKDGRIIQYSKPEEIVLNPADDYVRTFVAHTNPLNVLCGRSLMRTLDNCKRINGSVCLDPGGDSWLDLAEGNTIKGARQNGSSLDLQNWVPGQAVEGLGRRPTLVDSNIGMRDALQIRYQTGNKLVLHDNNKVVGILGDSELYHALLGKNLG catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_08585,AO353_08585,AO353_RS08585,WP_054594536.1,tr|A0A0N9VTA1|A0A0N9VTA1_PSEFL,Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),"Specifically important for: Putrescine Dihydrochloride. KEGG annotates this as putrescine aminotransferase, probably based on similarity to spuC (PA0299) of P. aeruginosa, but it is believed that only the g-glutamyl- pathway operates in P. aeruginosa (X. Yao et al 2011, PMC:3147493)",aminotransferase,Omega-amino acid--pyruvate aminotransferase (EC 2.6.1.18),putrescine aminotransferase,MTSKNPQTREWQTLSSEHHLAPFSDFKQLKEKGPRIITNAKGVYLWDSEGNKILDGMAGLWCVAIGYGRDELADAASKQMRELPYYNLFFQTAHPPVLELAKAISDIAPEGMNHVFFTGSGSEGNDTMLRMVRHYWAIKGQPNKKVIISRINGYHGSTVAGASLGGMTYMHEQGDLPIPGIVHIPQPYWFGEGGDMTPEEFGIWAANQLEEKILELGVDTVGAFIAEPIQGAGGVIIPPDSYWPRIKEILAKYDILFVADEVICGFGRTGEWFGSDFYGLKPDMMTIAKGLTSGYIPMGGLIVRDEVVEVLNEGGDFNHGFTYSGHPVAAAVALENIRILREEKIIEHVRAETAPYLQKRLRELNDHPLVGEVRGVGLLGAIELVQDKATRARYVGKGVGMICRQFCFDNGLIMRAVGDTMIIAPPLVITKAEIDELVTKARKCLDLTLSALQS catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_08595,AO353_08595,AO353_RS08595,WP_054594538.1,tr|A0A0N9VMN1|A0A0N9VMN1_PSEFL,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Specifically important for: Potassium acetate; L-Arginine; Putrescine Dihydrochloride; L-Citrulline. This is the first step in putrescine degradation and is important for the usage of putrescine as either C or N source. The phenotypes on citrulline and arginine might indicate a side pathway where they are converted to putrescine via ornithine decarboxylase (SEED_correct),gamma-glutamylputrescine synthetase,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),glutamine synthetase,MSVPPRAVQLNEANAFLKEHPEVLYVDLLIADMNGVVRGKRIERTSLHKVYEKGINLPASLFALDINGSTVESTGLGLDIGDADRICYPIPNTLCNEPWQKRPTAQLLMTMHELEGEPFFADPREVLANVVRKFDDMGLTICAAFELEFYLIDQENVNGRPQPPRSPVSGKRPHSTQVYLIDDLDEYVDCLQDILEGAKEQGIPADAIVKESAPAQFEVNLHHVADPIKACDYAVLLKRLIKNIAYDHEMDTTFMAKPYPGQAGNGLHVHISILDKDGKNIFASEDPEQNAALRHAIGGVLETLPAQMAFLCPNVNSYRRFGAQFYVPNSPCWGLDNRTVAIRVPTGSADAVRIEHRVAGADANPYLLMASVLAGVHHGLTNKIEPGAPVEGNSYEQNEQSLPNNLRDALRELDDSEVMAKYIDPKYIDIFVACKESELEEFEHSISDLEYNWYLHTV catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_11505,AO353_11505,AO353_RS11505,WP_054595050.1,tr|A0A0N9WI11|A0A0N9WI11_PSEFL,succinate-semialdehyde dehydrogenase (EC 1.2.1.16),"Specifically important for: L-Citrulline; L-Arginine; Putrescine Dihydrochloride. succinate semialdehyde is an intermediate in putrescine catabolism. KEGG has updated its annotation (K00135, see pman:OU5_3276) to include both succinate semialdehyde and glutarate semialdehyde as substrates. The other phenotypes are milder and might indicate a side pathway of conversion of citrulline and arginine to putrescine via ornithine decarboxylase. (KEGG_correct)",succinate-semialdehyde dehydrogenase,,glutarate semialdehyde dehydrogenase,MQLKDTLLFRQQAFIDGAWVDADNGQTINVTNPATGEILGTVPKMGAAETRRAIEAADKALPAWRALTAKERANKLRRWFELIIENQDDLARLMTLEQGKPLAEAKGEIVYAASFIEWFAEEAKRIYGDVIPGHQPDKRLIVIKQPIGVTAAITPWNFPAAMITRKAGPALAAGCTMVLKPASQTPYSAFALAELAQRAGIPKGVLSVVTGSAGDIGSELTSNPIVRKLSFTGSTEIGRQLMAECAKDIKKVSLELGGNAPFIVFDDADLDKAVEGAIISKYRNNGQTCVCANRLYIQDSVYDAFAEKLKVAVAKLKIGNGLEEGTTTGPLIDGKAVAKVQEHIADALSKGATLLAGGKVMEGNFFEPTILTNVPKSAAVAKEETFGPLAPLFRFKDEAEVIAMSNDTEFGLASYFYARDLGRVFRVAEALEYGMVGVNTGLISNEVAPFGGIKASGLGREGSKYGIEDYLEIKYLCLGI transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_12275,AO353_12275,AO353_RS12275,WP_054595190.1,tr|A0A0N9VTP5|A0A0N9VTP5_PSEFL,histidine permease,"Specific phenotype on histidine. 86% identical to PFLU0368, which is required for histidine utilization (PMCID: PMC1950622). SEED_correct",proline-specific permease,Histidine transport protein (permease),"amino acid transporter, AAT family",MQKPANGLKRGLSARHIRFMALGSAIGTGLFYGSASAIQMAGPAVLLAYLIGGAAVFMVMRALGEMAVHNPVAGSFGQYASTYLGPMAGFILGWTYAFEMVIVGMADVTAFGIYMGFWFPEVSRWIWVLGVVSIVGGLNLCNVKVFGEMEFWLSLLKVAAIVAMILGGFGIMLFGISTAPGQVTDISNLWTQGGFMPNGMGGLIASFAVVMFAFGGIEIIGVTAGEAKDPQHVLPRAINAVPLRILLFYVLTMLVLMSIFPWQQIGSQGSPFVQIFDKLGISSAATILNIVVITAAISAINSDIFGAGRMMFGLAQQGHAPKGFAHLSRNGVPWMTVVVMSVALLLGVLLNYLIPENVFLLIASIATFATVWVWLMILFTQVAMRRSMTAEQVAQLKFPVPFWPYAPMAAIAFMLFVFGVLGYFPDTQAALIVGVVWIVLLVLAYLMWVKPAAGQAALVARDPSFSNR catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_12285,AO353_12285,AO353_RS12285,WP_054595192.1,tr|A0A0N9WHL9|A0A0N9WHL9_PSEFL,N-formylglutamate deformylase (EC 3.5.1.68),"Specifically important for: L-Histidine. KEGG suggests formimidoyl-L-glutamate as the substrate, while SEED suggests N-formylglutamate. This gene is cofit with iminohydrolase (which produces N-formylglutamate) which suggests that the SEED annotation is correct. (SEED_correct)",N-formylglutamate deformylase,N-formylglutamate deformylase (EC 3.5.1.68),formiminoglutamase,VDKVLNFKQGRVPLLISMPHAGVRLTPAVEAGLIPEAKSLPDTDWHIPTLYEFAAELGASTLAAEYSRFVIDLNRPSDDKPMYVGATTGLYPATLFDGVPLFREGLEPSAEERASYLEKVWTPYHSTLQQELARLKAEFGYALLFDAHSIRSVIPHLFDGKLPDFNLGTFNGASCDPQLASQLEAICARHTDYSHVLNGRFKGGHITRHYGNPAENIHAVQLELGQCTYMEEVEPFRYRPDLAAPTQVVLKELLQGLLAWGQKHYA transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_15995,AO353_15995,AO353_RS15995,WP_054595825.1,tr|A0A0N9WH78|A0A0N9WH78_PSEFL,"trehalose-specific PTS system, I, HPr, and IIA components","Specific phenotype on trehalose. The IIB/IIC components are provided by AO353_15980. 45% similar to a NAG PTS system (PA3760, PMC:PMC2542419)",PTS mannose transporter subunit IIC,"PTS system, glucose-specific IIA component / Phosphotransferase system, phosphocarrier protein HPr / Phosphoenolpyruvate-protein phosphotransferase of PTS system (EC 2.7.3.9)","PTS system, fructose-specific IIA component ; phosphotransferase system, enzyme I, PtsI ; phosphocarrier protein FPr",MTLTQPLQLLAPLSGVLMPLDHVPDPVFASRVIGDGLCIDPTSQVLCAPLAGVVSNLQHSGHAISITDDSGVQVLLHIGLDTVNLKGQGFSALVEQGQRVEAGQPLIEFDADYVALHARSLLTLMLVVSGEPFSLLTPDSGLVACAQPVLRLSLGDPRTVVAQEEGEALFSKPVHLPNPNGLHARPAAVFAQAAKGFAASICLHKQQDSANAKSLVAIMALQTVHGDALQVSAVGEDAELAISTLAQLLADGCGEAVTPVAVVAPVVEAQEVSTKLLRGVCASAGSAFGYVVQVAERTLEMPEFAADQQLERESLERALMHATQALQRLRDNAAGEAQADIFKAHQELLEDPSLLEQAQALIAEGKSAAFAWNSATEATATLFKSLGSTLLAERALDLMDVGQRVLKLILGVPDGVWELPDQAILIAEQLTPSQTAALDTGKVLGFATVGGGATSHVAILARALGLPAVCGLPLQVLSLASGTRVLLDADKGELHLDPAVSVIEQLHAKRQQQRQRHQHELENAARAAVTRDGHHFEVTANVASLAETEQAMSLGAEGIGLLRSEFLYQQRSVAPSHDEQAGTYSAIARALGPQRNLVVRTLDVGGDKPLAYVPMDSEANPFLGMRGIRLCLERPQLLREQFRAILSSAGLARLHIMLPMVSQLSELRLARLMLEEEALALGLRELPKLGIMIEVPAAALMADLFAPEVDFFSIGTNDLTQYTLAMDRDHPRLASQADSFHPSVLRLIASTVKAAHAHGKWVGVCGALASETLAVPLLLGLGVDELSVSVPLIPAIKAAIREVELSDCQAIAHQVLGLESAEQVREALSVQQQAMVETSQVLES transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_16120,AO353_16120,AO353_RS16120,WP_054595847.1,tr|A0A0N9WH98|A0A0N9WH98_PSEFL,D-alanine and L-alanine transporter,"Specific phenotype on D-alanine as the carbon source. Also important for utilizing L-alanine as a carbon source. Both of these phenotypes are conserved. For D-alanine as a nitrogen source, there is a mild phenotype, and it may be partly redundant with another amino acid transporter (AO353_04615:04600). Sometimes annotated as a glycine transporter, but neither it nor its orthologs are important on glycine.",D-alanine/D-serine/glycine permease,D-serine/D-alanine/glycine transporter,"amino acid transporter, AAT family",MTVGNHLPHGETAQGGPLKRELGERHIRLMALGACIGVGLFLGSAKAIEMAGPAIMLSYIIGGLAILVIMRALGEMAVHNPVAGSFSRYAQDYLGPLAGFLTGWNYWFLWLVTCVAEITAVAVYMGIWFPEVPRWIWALAALVSMGSINLIAVKAFGEFEFWFALIKIVTIIAMVVGGVGVIAFGFGNDGVALGISNLWSHGGFMPNGVQGVLMSLQMVMFAYLGVEMIGLTAGEAKNPQKTIPNAIGSVFWRILLFYVGALFVILSIYPWNEIGTQGSPFVMTFERLGIKTAAGIINFVVITAALSSCNGGIFSTGRMLYSLAQNGQAPAGFAKTSNGVPRRALLLSIGALLLGVLLNYLVPEKVFVWVTAIATFGAIWTWVMILLAQLKFRKGLSPAERAALKYRMWLYPVSSYLALAFLVMVVGLMAYFPDTRVALYVGPAFLVLLTVLFYVFKLQPTGVPQAAVRTAS transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_16280,AO353_16280,AO353_RS16280,WP_054595877.1,tr|A0A0N7H0A0|A0A0N7H0A0_PSEFL,"ABC transporter for L-aspartate, L-asparagine, L-glutamate, and L-glutamine, permease component 1",# Specifically important in carbon source L-Aspartic Acid; pH ; carbon source L-Asparagine; carbon source L-Glutamic acid monopotassium salt monohydrate; carbon source L-Glutamine,amino acid ABC transporter permease,Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),glutamate/aspartate transport system permease protein,MDLDFSGVVQAVPGMWNGMVMTLQLTVLGVVGGIILGTLLALMRLSHSKLLSNIAGAYVNYFRSIPLLLVITWFYLAVPFVLRWITGEDTPIGAFTSCIVAFMMFEAAYFCEIVRAGVQSIPKGQMGAAKALGMGYGQMMRLIILPQAFRKMTPLLLQQSIILFQDTSLVYTVGLVDFLNATRASGDIIGRANEFLIIAGLVYFTISFAASRLVKRLQKRFAV transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_16285,AO353_16285,AO353_RS16285,WP_054595878.1,tr|A0A0N9WDU0|A0A0N9WDU0_PSEFL,"ABC transporter for L-aspartate, L-asparagine, L-glutamate, and L-glutamine, permease component 2",# Specifically important in carbon source L-Aspartic Acid; pH ; carbon source L-Asparagine; carbon source L-Glutamic acid monopotassium salt monohydrate; carbon source L-Glutamine,amino acid ABC transporter permease,Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),glutamate/aspartate transport system permease protein,MNYNWDWGVFFKSTGVGSETYLDWFITGLGWTIAIAIVAWIIALMLGSVLGVMRTVPNRLVSGIATCYVELFRNVPLLVQLFIWYFLVPDLLPQNLQDWYKQDLNPTTSAYLSVVVCLGLFTAARVCEQVRTGIQALPRGQESAARAMGFKLPQIYWNVLLPQAYRIVIPPLTSEFLNVFKNSSVASLIGLMELLAQTKQTAEFSANLFEAFTLATLIYFTLNMSLMLLMRMVEKKVAVPGLISVGGK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_16290,AO353_16290,AO353_RS16290,WP_054595879.1,tr|A0A0N9W726|A0A0N9W726_PSEFL,"ABC transporter for L-aspartate, L-asparagine, L-glutamate, and L-glutamine, periplasmic substrate-binding component",# Specifically important in carbon source L-Aspartic Acid; carbon source L-Asparagine; pH ; carbon source L-Glutamic acid monopotassium salt monohydrate; carbon source L-Glutamine,ABC transporter,Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),glutamate/aspartate transport system substrate-binding protein,MRIVPHILGAAITAALISTPVFAAELTGTLKKIKESGTITLGHRDASIPFSYIADASGVPVGYSHDIQLKIVEAIKKDLDMPNLKVKYNLVTSQTRIPLVQNGTVDVECGSTTNNTERQQQVDFSVGIFEIGTKLLSKKDSTYKDFADLKGKNVVTTAGTTSERILKSMNADKQMGMNVISAKDHGESFQMLESGRAVAFMMDDALLAGEMAKAKSPTDWAVTGTAQSYEIYGCMVRKGDAPFKKAVDDAIVATYKSGEINTIYGKWFTQPIPPKNLNLMFPMSDELKALISNPTDKAAEEKKS catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_18575,AO353_18575,AO353_RS18575,WP_054596267.1,tr|A0A0N9WXT8|A0A0N9WXT8_PSEFL,tyrosine aminotransferase (EC 2.6.1.57),"Specifically important for: L-Phenylalanine. Phenylalanine is catabolized via tyrosine and transamination to 4-hydroxyphenylpyruvate, so tyrosine is the relevant substrate. (KEGG_correct)",aromatic amino acid aminotransferase,Aspartate aminotransferase (EC 2.6.1.1),aromatic-amino-acid transaminase,MHFDAIGRVPGDPILGLMEAYAQDSNPCKFDLGVGVYKDAQGLTPIPQSVKLAELRLVDRQTTKTYIGGHGDPAFGKVINELVLGADSALIAEQRVGATQTPGGTGALRLSADFIAHCLPGRGIWLSNPTWPIHETIYATAGLKVSHYPYVGSDNRLDVEAMLATLNLIPKGDVVLLHACCHNPTGFDLSHDDWRRVLEVVRSRELLPLIDFAYQGFGDGLEQDAWAVRLFAAELPELLITSSCSKNFGLYRDRTGALIVCAKDAEKLVDIRSQLANIARNLWSTPPDHGAAVVATILGDPELKQLWADEVEAMRLRIAQLRSGLVEALEPHGLGERFAHIGVQRGMFSYTGLTPAQVKNLRDHHSVYMVSSGRANVAGIDATRLDLLAQAFADVCK catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_20845,AO353_20845,AO353_RS20845,WP_054596656.1,tr|A0A0N9WN92|A0A0N9WN92_PSEFL,uridine/adenosine nucleosidase (EC 3.2.2.3; EC 3.2.2.7),Important on adenosine and uridine; annotated by SEED as for uridine and inosine; fitness defect on uridine is subtle but is confirmed by observations for close homologs (SEED_correct),nucleoside hydrolase,Inosine-uridine preferring nucleoside hydrolase (EC 3.2.2.1),purine nucleosidase,MYRYAQRLHHLIRSLLLLSLLTATGAHAAEKIDLIIDTDPGADDVVALLFALASPEELNIRALTTVAGNVRLDKTSRNARLAREWAGREDVPVYAGAPKPLMRTPIYAENIHGKEGLSGVTVHEPKKGLAKGDAVSYLIDTLRAAKPHSITIAMLGPQTNLALALIQDPEITQGIKEVVIMGGAHFNGGNITPVAEFNLFADPHAAEVVLKSGVKLTYLPLDVTHKILTSDARLKQIAALNNNASKLVGDILNEYVKGDMEHYGIPGGPVHDATVIAYLLKPELFTGRAVNVVVDSREGPTFGQTVVDWYDGLKAPKNAFWVANGDAQGFFDLLTARLARLK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_21385,AO353_21385,AO353_RS21385,WP_054598296.1,tr|A0A0N9WNW1|A0A0N9WNW1_PSEFL,"m-Inositol ABC transporter, ATPase component (itaA)","Specific phenotype on m-Inositol and 63% identical to SMb20173 or itaA, which also has this function (see PMC2597717). The periplasmic substrate-binding component (AO353_21380) is not included in this table because it was annotated correclty in both resources. SEED_correct",D-ribose transporter ATP-binding protein,Inositol transport system ATP-binding protein,simple sugar transport system ATP-binding protein,MFASATASSIPLVGVQPNAIPVDEPYLLEIINVSKGFPGVVALSDVQLRVRPGSVLALMGENGAGKSTLMKIIAGIYQPDAGELRLRGKPVTFDTPLAALQAGIAMIHQELNLMPHMSIAENIWIGREQLNGFHMIDHREMHRCTAQLLERLRINLDPEEQVGNLSIAERQMVEIAKAVSYDSDILIMDEPTSAITDKEVAHLFSIIADLKAQGKGIIYITHKMNEVFSIADEVAVFRDGAYIGLQRADSMDGDSLISMMVGRELSQLFPVREKPIGDLLMSVRDLRLDGVFKGVSFDLHAGEILGIAGLMGSGRTNVAEAIFGITPSDGGEICLDGQPVRISDPHMAIEKGFALLTEDRKLSGLFPCLSVLENMEMAVLPHYAGNGFIQQKALRALCEDMCKKLRVKTPSLEQCIDTLSGGNQQKALLARWLMTNPRILILDEPTRGIDVGAKAEIYRLISYLASEGMAVIMISSELPEVLGMSDRVMVMHEGDLMGTLDRSEATQERVMQLASGMSVRH transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_21390,AO353_21390,AO353_RS21390,WP_054596742.1,tr|A0A0N7H0L7|A0A0N7H0L7_PSEFL,"m-Inositol ABC transporter, permease component (iatP)","Specific phenotype on m-Inositol and 71% identical to SMb20174 or itaP, which also has this function (see PMC2597717). The periplasmic substrate-binding component (AO353_21380) is not included in this table because it was annotated correclty in both resources. SEED_correct",ABC transporter,Inositol transport system permease protein,simple sugar transport system permease protein,MNAILENKPAMAPAKSRRRLPTELSIFLVLIGIGLVFEMFGWIVRDQSFLMNSQRLVLMILQVSIIGLLAIGVTQVIITTGIDLSSGSVLALSAMIAASLAQTSDFARAVFPSLTDLPVWIPVIAGLGVGLLAGAINGSIIAVTGIPPFIATLGMMVSARGLARYYTEGQPVSMLSDSYTAIGHGAMPVIIFLVVAVIFHIALRYTKYGKYTYAIGGNMQAARTSGINVKRHLVIVYSIAGLLAGLAGVVASARAATGQAGMGMSYELDAIAAAVIGGTSLAGGVGRITGTVIGALILGVMASGFTFVGVDAYIQDIIKGLIIVIAVVIDQYRNKRKLKR transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_21710,AO353_21710,AO353_RS21710,WP_054596801.1,tr|A0A0N9WMM3|A0A0N9WMM3_PSEFL,"ABC transporter for D-glucosamine, periplasmic substrate-binding component",# Specifically important in nitrogen source D-Glucosamine Hydrochloride; carbon source D-Glucosamine Hydrochloride,ABC transporter substrate-binding protein,"Glutamine ABC transporter, periplasmic glutamine-binding protein (TC 3.A.1.3.2)",,MQRRLSLFTACVFLFAATASAVGIAQAADSRLDNVLKRGHLIVGTGSTNAPWHFQGADGKLQGFDIDIGRMVAKGLFNDPSKVEFVVQSSDARIPNLLTDKVDMSCQFITVTASRAQQVAFTLPYYREGVGLLLPANSKYKEIEDLKAAGDSVTVAVLQNVYAEELVHQALPKAKVDQYDSVDLMYQAVNSGRADTAATDQSSVKYLMVQNPGRYRSPTYAWSPQTYACAVKRGDQDWLNFVNTVLHEAMTGVEFPTYAASFKQWFGVDLPSPAIGFPVEFK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_21715,AO353_21715,AO353_RS21715,WP_054596802.1,tr|A0A0N7H0M5|A0A0N7H0M5_PSEFL,"ABC transporter for D-glucosamine, permease component 1",# Specifically important in nitrogen source D-Glucosamine Hydrochloride; carbon source D-Glucosamine Hydrochloride,amino acid ABC transporter permease,"amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MNYQLNFAAVWRDFDTLLAGLGLGLELALVSIAIGCVIGLLMAFALLSKHRALRVLASVYVTVIRNTPILVLILLIYFALPSLGIRLDKLPSFIITLSLYAGAYLTEVFRGGLLSIPKGLREAGLAIGLGEWQVKAYVTVPVMLRNVLPALSNNFISLFKDTSLAAAIAVPELTYYARKINVESYRVIETWLVTTALYVAACYLIAMLLRYLEQRLAIRR transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_21720,AO353_21720,AO353_RS21720,WP_054596803.1,tr|A0A0N9WLR5|A0A0N9WLR5_PSEFL,"ABC transporter for D-glucosamine, permease component 2",# Specifically important in nitrogen source D-Glucosamine Hydrochloride; carbon source D-Glucosamine Hydrochloride,ABC transporter permease,"Amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MYESPSWLHELWVARDTLWSGFLTSVQCSVLAIMLGTLIGIVAGLVLTYGTLWMRAPFRFYVDLIRGTPVFVLVLACFYMAPALGWQIDAFQAGVLGLTLFCGSHVAEIVRGALQALPRGQMEASKAIGLTFYQALAYVLLPQALRQILPTWVNSSTEIVKASTLLSVIGVAELLLSTQQIIARTFMTLEFYLFAGFLFFIINYAIELLGRHIEKRVALP catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_21740,AO353_21740,AO353_RS21740,WP_054596807.1,tr|A0A0N9VSJ9|A0A0N9VSJ9_PSEFL,D-glucosaminate dehydratase (EC 4.3.1.9),"Specifically important for: D-Glucosamine Hydrochloride. This enzyme had not previously been linked to a gene. This is the second step in catabolism of glucosamine, and the 'beta' form of the enzyme was expected to be PLP-dependent and about this size. Iwamoto et al (2003) purified a non-specific 'alpha' enzyme for this reaction (PMID: 12686150)",amino acid deaminase,D-serine deaminase (EC 4.3.1.18),,MSSALNIAAVEKGAGQPGANLVRDVSLPALVLHREALEHNIHWMQAFVSHSGAELAPHGKTSMTPSLFRRQLEAGAWGITLATVVQTRAAYAHGVRRVLMANQLVGAPNMALIAELLADPSFDFYCMVDHPDNVADLGVFFAARGLRLNVMIEYGVVGGRCGCRSEAEVLALAEAIKAQPGLALTGIEGYEGVIHGDQAVSGIREFAASLVRLAVQLQDSGAFAISKPIITASGSAWYDLIAESFEAQNAGGRFLSVLRPGSYVAHDHGIYKEAQCCVLDRRSDLHEGLRPALEVWAHVQSLPEPGFAVIALGKRDVAYDAGLPVPLLRYRAGVLPAVGDDVSACTVTAVMDQHAFMTVAPGVQLRVGDIISFGTSHPCLTFDKWRTGCLVDEQLNVIESMETCF catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25635,AO353_25635,AO353_RS25635,WP_054597481.1,tr|A0A0N9VZU6|A0A0N9VZU6_PSEFL,arginine deiminase (EC 3.5.3.6),"Specifically important for: L-Citrulline. Distantly related to characterized arginine deiminases (PF02274); this reaction (in reverse) is the first step of citrulline catabolism, followed by the N2-succinyl-arginine pathway",amidinotransferase,,,MQTTNTVLMIRPTRFSFNQDTAANNRFQRPAAAAEDVQLKALQEFDGYVAALREHGVEVMVHNDSEAPHTPDSIFPNNWWSSHPDGTLVLYPMQGHNRRLERDKGVLDWLRDAYRIEQLLDLSDLEQQEVFLEGTGSMVLDREQRICYAGYSTRTHAKALDQVVEHLGYELCAFNAVDRHGVPIYHTNVMMSVGRQLAVVCLESVSDLDERNALRSRLECSGKQVLTLSFDQLESFAGNMLEVHNAAGEPLLVMSRTAWRSLHADQRRMVEAYAKPLPVNIDTIERIGGGSARCMLAEVYLPKRVSPQEQH catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25665,AO353_25665,AO353_RS25665,WP_054597487.1,tr|A0A0N9WLS4|A0A0N9WLS4_PSEFL,3-hydroxy-isobutyryl-CoA hydro-lyase (EC 4.2.1.17),"Specifically important for: L-Valine. Hydroxyisobutyryl-CoA is an intermediate in valine degradation. But is this the hydrolase (forming 3-hydroxyisobutyrate) as suggested by SEED, or is it the hydratase (or hydro-lyase in reverse) converting methylacrylyl-CoA to (S)-3-hydroxy-isobutanoyl-CoA, as suggested by KEGG? It belongs to the ECH_2 (PF16113) family, which should be a hydratase. No other genes for either step are apparent, but a CoA-transferase (AO353_11105) could replace the hydrolase. (KEGG_correct)",crotonase,3-hydroxyisobutyryl-CoA hydrolase (EC 3.1.2.4),enoyl-CoA hydratase,MTAQVSSQATHGIDASANDVLAEVRNHIGHLTLNRPAGLNAITLDMVRSLHRQLDAWSKDPHIHAVVLRGAGEKAFCAGGDIRSLYDSFKSGGTLHEDFFVEEYALDLAIHHYRKPVLALMDGFVLGGGMGLVQGADLRVVTERSRLAMPEVAIGYFPDVGGSYFLPRIPGELGIYLGVSGVQIRAADALYCGLADWYLESQKLAELDQHLDSLEWHDTPLKDLQGLLAKLALQQLPDAPLQALRPTIDHFFALPDVPSIVEQLRTVTVADSHDWAMTTADLLDSRSPLAMGVTLEMLRRGRQLSLENCFALELHLDRQWFERGDLIEGVRALLIDKDKSPRWNPPTVQALDAKHVASFFSGFDQSGS catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25670,AO353_25670,AO353_RS25670,WP_054597488.1,tr|A0A0N9VZC5|A0A0N9VZC5_PSEFL,isobutyrl-CoA dehydrogenase (EC 1.3.8.1),"Specifically important for: L-Valine. SEED has it as butyryl-CoA dehydrogenase, which is also expected to perform this reaction in valine catabolism",acyl-CoA dehydrogenase,Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MHDIELTEEQVMIRDMARDFARGEIAPHAQAWEKAGWIDDALVAKMGELGLLGMVVPEEWGGTYVDYVAYALAVEEISAGDGATGALMSIHNSVGCGPVLNYGTEEQKQTWLADLASGQAIGCFCLTEPQAGSEAHNLRTRAELRDGQWVINGAKQFVSNGRRAKLAIVFAVTDPDLGKKGLSAFLVPTDTPGFIVDRSEHKMGIRASDTCAVTLNNCTIPEANLLGERGKGLAIALSNLEGGRIGIAAQALGIARAAFEAALAYARDRVQFDKPIIEHQSVANMLADMHTRLNAARLLILHAARLRSAGKPCLSEASQAKLFASEMAEKVCSSAIQIHGGYGYLEDYPVERYYRDARITQIYEGSSEIQRMVIARELKNYLV catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25680,AO353_25680,AO353_RS25680,WP_054597490.1,tr|A0A0N9WNL1|A0A0N9WNL1_PSEFL,2-methylbutanoyl-CoA dehydrogenase / butanoyl-CoA dehydrogenase / isobutyryl-CoA dehydrogenase (EC 1.3.8.1),"Specifically important for: Sodium butyrate. SEED has it as butyryl-CoA dehydrogenase. Also important on isoleucine and valine, which implies that it acts on the other substrates as well",acyl-CoA dehydrogenase,Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MLPTDEQLQISDAARQFAQERLKPFAAEWDREHRFPKEAIGEMAELGFFGMLVPEQWGGCDTGYLAYAMALEEIAAGDGACSTIMSVHNSVGCVPILKFGNDDQKERFLKPLASGAMLGAFALTEPQAGSDASSLKTRARLNGDHYVLNGCKQFITSGQNAGVVIVFAVTDPSAGKRGISAFIVPTDSPGYKVARVEDKLGQHASDTCQILFEDVQVPVANRLGEEGEGYKIALANLEGGRVGIASQSVGMARAAFEAARDYARERESFGKPIIEHQAVAFRLADMATQIAVARQMVHYAAALRDSGKPALVEASMAKLFASEMAEKVCSTALQTLGGYGYLSDFPLERIYRDVRVCQIYEGTSDIQRMVISRNL catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25685,AO353_25685,AO353_RS25685,WP_054597491.1,tr|A0A0N9X119|A0A0N9X119_PSEFL,acetyl-CoA:acetyl-CoA C-acetyltransferase / acetyl-CoA:propanoyl-CoA 2-C-acetyltransferase (EC 2.3.1.9),Specifically important for: Sodium butyrate; L-Isoleucine. Degradation of acetoacetyl-CoA is consistent with a role in butyrate degradation. The role in Ile degradation suggests that 2-methylacetoacetyl-CoA is also a substrate (conversion to propionyl-CoA + acetyl-CoA). This is included in EC 2.3.1.9. The SEED annotation is more vague. (KEGG_correct),acetyl-CoA acetyltransferase,3-ketoacyl-CoA thiolase (EC 2.3.1.16),acetyl-CoA C-acetyltransferase,MTMSHDPIVIVSAVRTPMGGFQGELKSLSAPQLGAAAIRAAVERAGVAADAVEEVLFGCVLSAGLGQAPARQAALGAGLDKSTRCTTLNKMCGSGMEAAILAHDMLLAGSADVVVAGGMESMSNAPYLLDRARSGYRMGHGKVLDHMFLDGLEDAYDKGRLMGTFAEDCAEANGFTREAQDEFAIASTTRAQQAIKDGSFNAEIVPLQVIVGKEQKLITDDEQPPKAKLDKIASLKPAFRDGGTVTAANSSSISDGAAALLLMRRSEAEKRGLKPLAVIHGHAAFADTPGLFPVAPVGAIKKLLKKTGWSLDEVELFEVNEAFAVVSLVTMTKLEIPHSKVNVHGGACALGHPIGASGARILVTLLSALRQKGLKRGVAAICIGGGEATAMAVECLY transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25880,AO353_25880,AO353_RS25880,WP_054597528.1,tr|A0A0N9WQM3|A0A0N9WQM3_PSEFL,"ABC transporter for D-mannitol and D-mannose, periplasmic substrate-binding component",# Specifically important in carbon source D-Mannitol; carbon source D-Mannose,sugar ABC transporter substrate-binding protein,"Various polyols ABC transporter, periplasmic substrate-binding protein",sorbitol/mannitol transport system substrate-binding protein,MKPTVKALLACTCMTLSSVSLGAQTLTIATVNNSDMIRMQKLSKTFEAEHPEIKLNWVVLEENVLRQRLTTDIATQGGQFDVLTIGMYEAALWGAKGWLEPMKDLPAGYALDDVFPSVREGLSVKGTLYALPFYAESSMTYYRTDLFKDAGLAMPEHPTWEQIGEFAGKLNKPDQEQYGICLRGKAGWGENMALITTIANAYGARWFDEKWQPEFSGPEWKNALSFYADTMKKSGPPGASSNGFNENLALFNSGKCAIWVDASVAGSFVTDKTQSKVSEHVGFTYAPHQVTDKGSAWLYSWALAIPTSSKAKDAAKTFSAWATSKEYGALVAEKDGIANVPPGTRASTYSEAYMNAAPFAKVTLESLKVADPGKPTLKPVPYIGIQLVTIPEFQAVGTQVGKLFSAALIGQTTVDQALAAAQQTTEREMKRAGYPK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25885,AO353_25885,AO353_RS25885,WP_054597529.1,tr|A0A0N9VUK6|A0A0N9VUK6_PSEFL,"ABC transporter for D-mannitol and D-mannose, permease component 1",# Specifically important in carbon source D-Mannose; also important for using D-mannitol,sugar ABC transporter permease,"Various polyols ABC transporter, permease component 1",sorbitol/mannitol transport system permease protein,MNTSTLKAQMDIPAPLRKSRLANPGWFLVSPSVALLLLWMIVPLGMTLYFSLIRYNLLYPGDNAFVGLENFSYFLTDSGFLPGATNTLLLVGSVLLISVVFGVLISALLEASEFLGRGIVRVMLISPFFIMPTVGALIWKNLIFHPVSGILAAVWKLFGAQPVDWLAHYPLLSIIIIVSWQWLPFAILILMTAMQSLDQEQKEAARLDGAGPIAIFWHLTLPHLARPIAVVVMIETIFLLSVFAEIFTTTNGGPGYASTNLAYLIYNQALVQFDVGMASAGGLIAVVIANIAAIILVRMLGKNLTDKA transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25890,AO353_25890,AO353_RS25890,WP_054597530.1,tr|A0A0N9WHV6|A0A0N9WHV6_PSEFL,"ABC transporter for D-mannitol and D-mannose, permease component 2",# Specifically important in carbon source D-Mannose; also important for using D-mannitol,sugar ABC transporter permease,"Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MTLQQTRRLQSLLLGTLAWAIAILIFFPIFWMVLTSFKTEIDAFATPPQFIFTPTLENYLHVNERSGYFSFAWNSVVISFSATALCLLIAVPAAYSMAFYETQRTKGTLLWMLSTKMLPPVGVLMPIYLLAKSFGLLDTRIALIIIYTLINLPIVVWMIYTYFKDIPKDILEAARLDGATLWQEMVRVLLPIAKGGLASTVLLSLILCWNEAFWSLNLTSSKAAPLTALIASYSSPEGLFWAKLSAVSTLACAPILIFGWISQKQLVRGLSFGAVK transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_25895,AO353_25895,AO353_RS25895,WP_054597531.1,tr|A0A0N9WAL6|A0A0N9WAL6_PSEFL,"ABC transporter for D-mannitol and D-mannose, ATPase component",# Specifically important in carbon source D-Mannose; carbon source D-Mannitol,ABC transporter ATP-binding protein,"Various polyols ABC transporter, ATP-binding component",sorbitol/mannitol transport system ATP-binding protein,MAHLKIKNLQKGFEGFSIIKGIDLEVNDREFVVFVGPSGCGKSTLLRLIAGLEEVTAGTIELDGRDITEVSPAKRDLAMVFQTYALYPHMSVRKNMSFALDLAGVNKAEVEKKVNEAARILELGPMLERKPKQLSGGQRQRVAIGRAIVRNPKIFLFDEPLSNLDAALRVQMRLELARLHKELQATMIYVTHDQVEAMTLADKVVVLNGGRIEQVGSPLELYHQPANLFVAGFLGTPKMGFLKGKVTRVERQNCEVLLDAGTRITLPLSGANLSIGGAVTLGIRPEHLNLALPGDCTLQVTADVSERLGSDTFCHVLTASGEALTMRIRGDLASRYGEQLSLHLDAEHCHLFDANGVAVARPLRAAA transporters,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_26685,AO353_26685,AO353_RS26685,WP_054598350.1,tr|A0A0N9W1L7|A0A0N9W1L7_PSEFL,cobalt efflux pump,"Strong phenotypes on citrate (as a carbon source) but also on cobalt stress. The citrate phenotype may be due to iron deprivation (via chelation by the citrate) leading to heavy metal stress. The cobalt phenotype is more conserved. It is possible that nickel is also a substrate of this efflux pump, as a slight defect in nickel stress was observed, and this protein is 49% identical to the cobalt and nickel exporter dmeF (Atu0891) of Agrobacterium tumefaciens (PMID:27235438). AO353_26685 is annotated as performing zinc or cadium efflux as well (czcD), but unfortunately, we do not have data for those heavy metal stresses. However a role in zinc stress seems doubtful, as a close homolog in strain GW456-L13 is not important for resisting zinc.",cation transporter,Cobalt-zinc-cadmium resistance protein CzcD,,MRSTPQVSRYSHDHMFLGATHDENARRTLWVVALTFVMMVGEIIAGYITGSMALLADGFHMATHVGALGITAIAYGFARRNAGNARYSFGTGKVGDLAGFASAMVLGLVSIFIAVESVMRLFQPTTVAFTEATLIAVLGLAVNIVSALLLAGNSAHHDHGHGHGHGHGHGHGHHHDNNLRSAYVHVLADAMTSVLAIAALLAGRYLGWVWLDPVMGIVGAIVIANWAYGLMRDSAAVLLDTTDEHVAAEVRELLESTGDVSITDLHVWRVGPQARAAIVSVVTSAAVTADTIRERLAPVHELSHLTVEYRNA catabolism,Pseudomonas fluorescens FW300-N2E3,pseudo3_N2E3,AO353_29305,AO353_29305,AO353_RS29305,WP_054598124.1,tr|A0A0N9WJH3|A0A0N9WJH3_PSEFL,Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),Specifically important for: Putrescine Dihydrochloride. part of the gamma-glutamyl-putrescine pathway for degrading putrescine (SEED_correct),gamma-glutamyl-gamma-aminobutyrate hydrolase,Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),putative glutamine amidotransferase,MALKPLIGVTACVKQIGLHPYHVSGDKYLRAVSVAALGLPVVIPSLGELTEIDELLAHLDGLLLTGSPSNVEPFHYQGPASAPGTDHDPARDSTTLPLLRAAIAAGVPVLGICRGFQEMNVAFGGSLHQKVHELPGMLDHREADHPDLAVQYAPAHAVSVQPGGVFQALELPPVFQVNSIHSQGIDRLAPGLRAEAIAPDGLIEAISVEHSKAFALGVQWHPEWQVLANPPYLSIFQAFGDACRQRAALRNTR transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00010,AO356_00010,AO356_RS00010,WP_060738036.1,tr|A0A0N9WSQ8|A0A0N9WSQ8_PSEFL,"ABC transporter for D-Sorbitol, ATPase component",Specific phenotype on D-Sorbitol.,ABC transporter ATP-binding protein,"Various polyols ABC transporter, ATP-binding component",,MATLKIENLKKGFEGLSIIKGIDLEVKDKEFVVFVGPSGCGKSTLLRLIAGLEDVTSGTIELDGRDITEVTPAKRDLAMVFQTYALYPHMTVRKNLSFALDLAGEKKPDVERKVAEAARILELGSLLDRKPKQLSGGQRQRVAIGRAIVRNPKIFLFDEPLSNLDAALRVQTRLELSRLHKELQATMIYVTHDQVEAMTLATKVVVLNAGRIEQIGSPLELYHHPANLFVAGFLGTPKMGFLQATVHAVHASGVEVRFASGTTLLIPRDSSALSVGQSVTIGIRPEHLTLGAEGQVLVTTDVTERLGSDTFCHVNVDSGESLTVRVQGDCEVPYAARRYLTLDVAHCHLFDESGLSVSPAASRAA transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00015,AO356_00015,AO356_RS00015,WP_013692786.1,tr|A0A0N9VWQ6|A0A0N9VWQ6_PSEFL,"ABC transporter for D-Sorbitol, permease component 1",Specific phenotypes on D-Sorbitol; D-Sorbitol. (no phenotype on mannitol),sugar ABC transporter permease,"Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MMTLKQSRSLQSVLLGTLAWVTALLLFFPIFWMVLTSFKTEIDAFATPPQFIFMPTLENYLHIQERSDYFHFAWNSVLISFSATALCMLIAVPAAYSMAFYETKRTKQTLLWMLSTKMLPPVGVLMPIYLLAKGAGLLDTRIALIVIYTLINLPIVVWMIYTYFKDIPREILEAARLDGATLGQEMLRVLLPISKGGLASTMLLSMILCWNEAFWSLNLTSSSAAPLTALIASYSSPEGLFWAKLSAVSTLACAPILIFGWISQKQLVRGLSFGAVK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00020,AO356_00020,AO356_RS00020,WP_013692785.1,tr|A0A0N9WK88|A0A0N9WK88_PSEFL,"ABC transporter for D-Sorbitol, permease component 2",Specific phenotypes on D-Sorbitol; D-Sorbitol. (no phenotype on mannitol),sugar ABC transporter permease,"Various polyols ABC transporter, permease component 1",,MNTSIIRAQLAASAPSRARRLINPGWFLVSPSVALLLLWMIVPLAMTVYFSVIRYNLLNPGENEFVGLENFAYFVTDSGFLPGALNTLILVGSVLLISVIFGVLIAALLEASEFFGRGIVRVLLISPFFIMPTVGSLIFKNLIFHPVSGILAAVWKFFGAQPVDWLAHYPLFSIIVIVSWQWLPFAILLLMTAMQSLDQEQKEAARLDGAGALAIFWHLTLPHLARPIAVVVMIETIFLLSVFAEIFTTTNGGPGFASTNLAYLIYNQALVQFDVGMASAGGLIAVVIANIAAIVLVRMIGKNLTDKA transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00025,AO356_00025,AO356_RS00025,WP_060738037.1,tr|A0A0N9WC97|A0A0N9WC97_PSEFL,"ABC transporter for D-Sorbitol, periplasmic substrate-binding component",Specific phenotype on D-Sorbitol.,sugar ABC transporter substrate-binding protein,"Various polyols ABC transporter, periplasmic substrate-binding protein",,MKITNALILSTGLSFALASHAAETLTIATVNNGDMIRMQRLSKVFEQQHPDIKLNWVVLEENVLRQRLTTDIATQGGQFDVLTIGTYETPMWGAKNWLEPMKDLPAGYDVDDIFPAVRQGLSVNDTLYALPFYGESTITYYRTDLFKAAGLTMPGQPTWSQLGEFAAKLNDPSKDQYGMCLRGKAGWGENMALLTTMANAFGARWFDEKWQPELNGPEWKAAATFYVDTLKKYGPPGVSSNGFNETLALFNSGKCAIWVDASVAGSFTTDKEQSRVVDSVGFAPAPIEVTDKGSSWLYAWSLAIPATSKHKEAAKSFVTWATSKEYIQLVTDKDGITNVPPGTRISTYSDAYLKAAPFAQVTLQMMKHADPSQPSAKPVPYVGIQYVVIPEFQSIGTSVGKLFSAALTGQMSVEQALASAQSTTEREMKRAGYPKK catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00450,AO356_00450,AO356_RS00450,WP_060738105.1,tr|A0A0N9WKE9|A0A0N9WKE9_PSEFL,D-glucosaminate dehydratase (EC 4.3.1.9),"Specifically important for: D-Glucosamine Hydrochloride. This enzyme had not previously been linked to a gene. This is the second step in catabolism of glucosamine, and the 'beta' form of the enzyme was expected to be PLP-dependent and about this size. Iwamoto et al (2003) purified a non-specific 'alpha' enzyme for this reaction (PMID: 12686150)",amino acid deaminase,D-serine deaminase (EC 4.3.1.18),,MSSVIPNAGVEKGAATVGAHLLKDVSLPALVLHRAALEHNIRWMQAFVTDSGAELAPHGKTSMTPALFRRQLDAGAWGLTLATAVQTRAAYAHGVRRVLMANQLVGTPNMALIADLLADPAFEFHCMVDHPDNVADLGAFFASRGMKLNVMIEYGVVGGRCGCRTEAEVLALAEAIRSQPALALTGIEGYEGVIHGDHAISGIRAFAASLVRLAVQLQDDDAFAIDKPIITASGSAWYDLIAESFEAQNAHGRFLSVLRPGSYVAHDHGIYKEAQCCVLERRSDLHEGLRPALEVWAHVQSLPEPGFAVIALGKRDVAYDAGLPVPLKRYTPGSDSVPGDDVSGCKVTAVMDQHAFMSVAAGVELRVGDIIAFGTSHPCLTFDKWRVGCLVDEQLRVVESMETCF transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00470,AO356_00470,AO356_RS00470,WP_028236639.1,tr|A0A0N9WS63|A0A0N9WS63_PSEFL,"ABC transporter for D-Glucosamine, permease component 1",Specific phenotypes on D-Glucosamine Hydrochloride. Detrimental on D-serine as N source,ABC transporter permease,"Amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MYESPSWLHELWVARDTLWAGFLTSVQCSLLAIVLGTLIGLVAGLVLTYGRTWMRAPFRFYVDLIRGTPVFVLVLACFYMAPALGWQIGAFQAGVLGLTLFCGSHVAEIVRGALQALPRGQMEASQAIGLTFYQSLGYVLLPQALRQILPTWVNSSTEIVKASTLLSVIGVAELLLSTQQIIARTFMTLEFYLFAGFLFFIINYAIELLGRHIEKRVALP transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00475,AO356_00475,AO356_RS00475,WP_018608019.1,tr|A0A0N7H195|A0A0N7H195_PSEFL,"ABC transporter for D-Glucosamine, permease component 2",Specific phenotypes on D-Glucosamine Hydrochloride. Detrimental on D-serine as N source,amino acid ABC transporter permease,"amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MNYQLNFAAVWRDFDTLLAGLGLGLSLALVSIAIGCVIGLAMAFALLSKHRVLRVLASVYVTVIRNTPILVLILLIYFALPSLGIRLDKLPSFVITLSLYAGAYLTEVFRGGLLSIHKGQREAGLAIGLGEWQVKAYVTVPVMLRNVLPALSNNFISLFKDTSLAAAIAVPELTYYARKINVESYRVIETWLVTTALYVAACYLIAMVLRYFEQRLAIRR transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00480,AO356_00480,AO356_RS00480,WP_060738109.1,tr|A0A0N9WRG0|A0A0N9WRG0_PSEFL,"ABC transporter for D-Glucosamine, periplasmic substrate-binding component",Specific phenotype on D-Glucosamine Hydrochloride.,ABC transporter substrate-binding protein,"Glutamine ABC transporter, periplasmic glutamine-binding protein (TC 3.A.1.3.2)",,MQRRPSLFKACVFLFAATTAAMGVAQAADSKLDSVLQRGKLIVGTGSTNAPWHFQGADGKLQGFDIDIARMVAKGLFNDPEKVEFVVQSSDARIPNLLTDKVDMSCQFITVTASRAQQVAFTLPYYREGVGLLLPANSKYKEIEDLKAAGDDVTVAVLQNVYAEELVHQALPKAKVDQYDSVDLMYQAVNSGRADAAATDQSSVKYLMVQNPGRYRSPAYAWSPQTYACAVKRGDQDWLNFVNTTLHEAMTGVEFPTYAASFKQWFGVELPSPAIGFPVEFK catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_00940,AO356_00940,AO356_RS00940,WP_060738180.1,tr|A0A0N9WKN2|A0A0N9WKN2_PSEFL,uridine/adenosine nucleosidase (EC 3.2.2.3; EC 3.2.2.7),"Specifically important for: Uridine. Also important on adenosine. Annotated by SEED as acting on uridine and inosine, and the fitness data suggests that it might also act on inosine (subtle phenotype)",nucleoside hydrolase,Inosine-uridine preferring nucleoside hydrolase (EC 3.2.2.1),,MHRYAEKMHQLIRSLLLLSLITATSAQAAEKIDLIIDTDPGADDVVALLFALASPEELHIRALTTVAGNVRLDKTSRNARLAREWAGREDVPVYAGAPKPLMRTPIYAENIHGKEGLSGVTVHEPKKGLAEGNAVNYLIDTLKAAKPHSITIAMLGPQTNLALALVQEPDIVQGIKEVVIMGGAHFNGGNITPVAEFNLFADPQAAEVVAKSSVKLTYLPLDVTHKILTSEARLKQIAALNNNASKLVGDILNEYVKGDMEHYGMTGGPVHDATVIAYLLKPQLFTGRSVNVVVDSREGPTFGQTIVDWYDGLKAPKNAFWVENGDAQGFFDLLTERLARLK catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_05200,AO356_05200,AO356_RS05210,WP_060738871.1,tr|A0A0N9WVA7|A0A0N9WVA7_PSEFL,Isomerase for D-mannose and D-mannitol catabolism (probably D-mannose-6-phosphate isomerase or D-mannose isomerase),"Specifically important for: D-Mannitol; D-Mannose. Often annotated as N-acylglucosamine 2-epimerase, but some homologs are annotated as D-mannose isomerase or D-mannose-6-phosphate isomerase, which would fit its phenotypes. (Mannitol is probably converted to mannose or mannose-6-phosphate.) A fructokinase AO356_00035 also seems to be required for consuming these substrates, which suggests that this converts D-mannose to D-fructose, but data for close homologs suggests a phosphorylated substrate instead, so the substrate is uncertain.",sugar isomerase,N-acylglucosamine 2-epimerase (EC 5.1.3.8),,MDTFQPAFSSWLNAPAHQQWLAAEGLRLLAFAKASKLPDGFGNLDELGRLPADARAETMNTARMTHSFAMAHIQGLPGFAELVDHGIQALNGRLRDAEHGGWFATTRPDEDGAGKAAYLHAFVALAASSAVVAQRPGAAALLDEAVRIIDEHFWCEEEGALRESFNRDWSEEEAYRGANSNMHATEAFLALADATDDPRWLVRALRIVERVIHGHAAANDYLVVEHFDRHWQPLHEYNQDNPADGFRPYGTTPGHGFEWARLLLHLEAARVQIGMLTPGWLAQDAQKLFDQNCRHGWDVDGAPGIVYTLDWDNRAVVRHRLHWVHAEAAAAASALLKRTDEAKYEAWYRCFWEFCDKHFIDRCNGSWHHELDPQNRPSADIWPGKPDLYHAWQAVLIPRLPLAPSMASALARLSSPAPV transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_05495,AO356_05495,AO356_RS05505,WP_060738925.1,tr|A0A0N9WVJ0|A0A0N9WVJ0_PSEFL,"ABC transporter for L-Lysine, periplasmic substrate-binding component","Specific phenotype on L-Lysine. Note that this organism has a second ABC transporter that is also important for lysine utilization, which is not explained.",ABC transporter substrate-binding protein,Lysine-arginine-ornithine-binding periplasmic protein precursor (TC 3.A.1.3.1),lysine/arginine/ornithine transport system substrate-binding protein,MKKALLTLSALALCMAAGVATAKEYKELRFGVDPSYAPFESKAADGSLVGFDIDLGNAICAELKVKCKWVESDFDGMIPGLKANKFDGVISSMTVTPAREKVIDFSSELFSGPTAYVFKKGSGLSEDVASLKGKTIGYEQGTIQEAYAKAVLDKAGVKTQAYQNQDQVYADLTSGRLDAAIQDMLQAELGFLKSPKGEGYEVSKPVDSELLPAKTAIGIKKGNSELKALLDKGIKALHDDGKYAEIQKKHFGDLNLYSGK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_05500,AO356_05500,AO356_RS05510,WP_060743074.1,tr|A0A0N9X3H8|A0A0N9X3H8_PSEFL,"ABC transporter for L-Lysine, permease component 1","Specific phenotypes on L-Lysine; L-Lysine. Note that this organism has a second ABC transporter operon that is important for lysine utilization, which is not explained.",amino acid ABC transporter permease,"Histidine ABC transporter, permease protein HisQ (TC 3.A.1.3.1)",histidine transport system permease protein,MFEQLLQNLGLSAFSLQGFGPLLMQGTWMTIKLSALSLLLSVLLGLLGASAKLSRVKLLRIPAQLYTTLIRGVPDLVLMLLIFYSLQTWLTSFTDFMEWEYIEIDPFGAGVITLGFIYGAYFTETFRGAILAVPRGQVEAATAYGLKRGQRFRFVVFPQMMRFALPGIGNNWMVMLKATALVSIIGLADLVKAAQDAGKSTYQLFYFLVLAALIYLLITSASNFILRWLERRYAAGAREAVR transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_05505,AO356_05505,AO356_RS05515,WP_060738926.1,tr|A0A0N9VZB6|A0A0N9VZB6_PSEFL,"ABC transporter for L-Lysine, permease component 2","Specific phenotypes on L-Lysine; L-Lysine. Note that this organism has a second ABC transporter operon that is also important for lysine utilization, which is not explained.",amino acid ABC transporter permease,"Histidine ABC transporter, permease protein HisM (TC 3.A.1.3.1)",histidine transport system permease protein,MIELLQEYWRPFLYSDGVNITGLAMTLWLLSASLLIGFVVSIPLSIARVSPKFYVRWPVQFYTYLFRGTPLYIQLLICYTGIYSIAAVRAQPMLDSFFRDAMNCTILAFALNTCAYTTEIFAGAIRSMNHGEVEAAKAYGLTGWKLYAYVIMPSALRRSLPYYSNEVILMLHSTTVAFTATVPDVLKVARDANSATFLTFQSFGIAALIYLTVTFALVGLFRLAERRWLAFLGPTH transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_05515,AO356_05515,AO356_RS05525,WP_060738928.1,tr|A0A0N9WEE5|A0A0N9WEE5_PSEFL,"ABC transporter for L-Lysine, ATPase component","Specific phenotype on L-Lysine. Note that this organism has a second ABC transporter operon that is also important for lysine utilization, which is not explained.",amino acid transporter,"Histidine ABC transporter, ATP-binding protein HisP (TC 3.A.1.3.1)",histidine transport system ATP-binding protein,MYKLTIEGLHKSYGEHEVLKGVSLKAKTGDVISLIGASGSGKSTFLRCINFLEQPNDGAMTLDGQPVQMIKDRHGMHVADADELQRIRTRLAMVFQHFNLWSHMTVLENITMAPRRVLGVSKQEADDRARRYLDKVGLPARVAEQYPAFLSGGQQQRVAIARALAMEPEVMLFDEPTSALDPELVGEVLKVIQGLAEEGRTMIMVTHEMSFARKVSNQVLFLHQGLVEEEGAPEDVLGNPKSERLKQFLSGNLK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_07550,AO356_07550,AO356_RS07560,WP_060743082.1,tr|A0A0N9WGP9|A0A0N9WGP9_PSEFL,L-lactate and D-lactate permease (lctP family),"This gene is often annotated as a L-lactate transporter but has a specific phenotype on D-lactate as well. Although it is difficult to rule out polar effects on the lactate dehydrogenase genes downstream, this gene's phenotype on D-lactate is conserved and is observed on both strands, and no other D-lactate transporter was identified by the mutant fitness data. (There is a zinc ABC transporter with a specific defect on D-lactate, but it is very similar to znuABC of P. aeruginosa, i.e. AO356_11480 is 65% identical to PA5498, so it is unlikely to be a lactate transporter.) Furthermore, the annotation as lactate permease seems to be derived from E. coli glcA or yghK, which transports both isomers of lactate (PMID:11283302). (KEGG_correct)",L-lactate permease,L-lactate permease,"lactate transporter, LctP family",MQTWQQLYSPLGSLGLSALAAVIPIVFFFLALAVFRLKGHVAGSITLALAIAVAIFAFNMPADMAFAAAGYGFAYGLWPIAWIIVAAVFLYKLTVKSGQFEVIRSSVLSITDDQRLQVLLIGFCFGAFLEGAAGFGAPVAITAALLVGLGFNPLYAAGLCLIANTAPVAFGALGIPIIVAGQVTGIDAFKIGAMTGRQLPLLSLFVPFWLVFMMDGLRGVRETWPAALVAGLSFAITQYFTSNFIGPELPDITSALASLISLTLFLKVWQPKRTAGAQIAGATSSATVTASVGGFGQPRSTVASPYSLGEIIKAWSPFLILTVLVTIWTLKPFKAMFAAGGSMYGWVFNFAIPHLDQMVIKVAPIVINPTAIPAVFKLDPISATGTAIFFSALISMLVLKINIKTGLTTFKETLFELRWPILSIGMVLAFAFVTNYSGMSSTMALVLAGTGAAFPFFSPFLGWLGVFLTGSDTSSNALFSSLQATTAHQIGVNDTLLVAANTSGGVTGKMISPQSIAVACAATGLVGKESDLFRFTLKHSLFFATIVGLITLAQAYWFTGMLVH catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09590,AO356_09590,AO356_RS09605,WP_060739584.1,tr|A0A0N9W738|A0A0N9W738_PSEFL,N-formylglutamate deformylase (EC 3.5.1.68),"Specifically important for: L-Histidine. KEGG suggests formimidoyl-L-glutamate as the substrate, while SEED suggests N-formylglutamate. This gene is cofit with iminohydrolase (which produces N-formylglutamate) which suggests that the SEED annotation is correct. (SEED_correct)",N-formylglutamate deformylase,N-formylglutamate deformylase (EC 3.5.1.68),formiminoglutamase,VEKVLNFKQGRVPLLVSMPHAGLRLTPAVKAGLIPEAQSLPDTDWHIPQLYEFANELGASTLAAEYSRFVVDLNRPSDDKPMYVGATTGLYPATLFDGVPLFREGLEPSAEERATYLQQVWMPYHQALRQELARLKAEFGYALLFDAHSIRSVIPHLFDGKLPDFNLGTFNGASCDPTLASQLEAICARHGQFTHVLNGRFKGGHITRHYGNPAEDIHAVQLELCQSTYMEEFEPFNYRPDLAAPTQVVLRELLEGFLAWGQKTYKH transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09610,AO356_09610,AO356_RS09625,WP_050586211.1,tr|A0A0N9X7T0|A0A0N9X7T0_PSEFL,"ABC transporter for L-Histidine, ATPase component",Specific phenotype on L-Histidine. This gene cluster also includes a permease of the nucleobase-cation-symport family (AO356_09625) that is important for histidine utilization but whose role is unclear. (SEED_correct),hypothetical protein,"Histidine ABC transporter, ATP-binding protein (TC 3.A.1)",glycine betaine/proline transport system ATP-binding protein,MSNAAISKIEVKNVFKIFGNRSKEALELIRQNKTKDQVLAETGCVVGVNDLSLSIGTGEIFVIMGLSGSGKSTLVRHFNRLIDPTSGAILVDGEDILQLDMDALREFRRHKISMVFQSFGLLPHKSVLDNVAYGLKVRGESKQVCAERALHWINTVGLKGYENKYPHQLSGGMRQRVGLARALAADTDIILMDEAFSALDPLIRAEMQDQLLELQKTLHKTIVFITHDLDEAVRIGNRIAILKDGKLIQVGTPREILHSPADEYVDRFVQRRAAVV transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09615,AO356_09615,AO356_RS09630,WP_060739588.1,tr|A0A0N9W6X3|A0A0N9W6X3_PSEFL,"ABC transporter for L-Histidine, permease component",Specific phenotypes on L-Histidine.,ABC transporter permease,"Histidine ABC transporter, permease protein (TC 3.A.1)",glycine betaine/proline transport system permease protein,MFPESFTFSIADWVNGWVDSLVTNYGDVFRHISDTLLWAIVNLEGLLRMAPWWLMLAIVGGIAWHATRKVLATAVIVGLLFLVGAVGLWDKLMQTLALMLVATLISVLIGIPLGILSARSNRLRSVLMPLLDIMQTMPSFVYLIPVLMLFGLGKVPAIFATVIYAAPPLIRLTDLGIRQVDGEVMEAINAFGANRWQQLFGVQLPLALPSIMAGINQTTMMALSMVVIASMIGARGLGEDVLVGIQTLNVGRGLEAGLAIVILAVVIDRITQAYGRPRHEVSK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09620,AO356_09620,AO356_RS09635,WP_014336129.1,tr|A0A0N9WXL6|A0A0N9WXL6_PSEFL,"ABC transporter for L-Histidine, periplasmic substrate-binding component",Specific phenotype on L-Histidine. This gene cluster also includes a permease of the nucleobase-cation-symport family (AO356_09625) that is important for histidine utilization but whose role is unclear,histidine ABC transporter substrate-binding protein,"Histidine ABC transporter, histidine-binding protein (TC 3.A.1)",glycine betaine/proline transport system substrate-binding protein,MKSNKTLLTTLLSMGLLASAGATQAAGWCESGKPVKFAGLNWESGMLLTDVLQVVLEKGYDCKTDSLPGNSITMENALSSNDIQVFAEEWVGRSEVWNKAEKAGKVVGVGAPVVGAIEGWYVPRYVVEGDAKRKLEAKAPGLKNIADLGQYAAVFKDPEEPSKGRFYNCPAGWTCELDNSEMLKSYGLEKTYTNFRPGTGPALDAAVLSSYKRGEPILFYYWSPTPLMGQVDLVKLEEKPGVDKSVSIKVGLSKTFHDEAPELVAVLEKVNLPIDILNQNLGRMAKERIESPKLAKIFLKEHPEVWHAWVSEDAAKKIDAAL catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09635,AO356_09635,AO356_RS09650,WP_060739590.1,tr|A0A0N7H1W3|A0A0N7H1W3_PSEFL,histidine utilization regulatory protein hutD,Specifically important for: L-Histidine. Believed to prevent overexpression of the hut operon (not an enzyme) (PMC:1950622),hypothetical protein,Conserved hypothetical protein (perhaps related to histidine degradation),hypothetical protein,MSQLKVLRAADYPRMPWKNGGGSTEEITRDAGTGLEGFGWRLSIADIGESGGFSTFAGYERIISVLQGDGMTLNVDGQATGPLRPLDPFAFSGESHVHCTLLGGPIRDFNLIYAPQRYRARLQWVGGQQRFFSEAETLLVFSAAPGLTIRIGESAVILGLYDCLQLSGNTGLLDITSHGQCCVIELTAR transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09895,AO356_09895,AO356_RS09910,WP_014336086.1,tr|A0A0N9WX98|A0A0N9WX98_PSEFL,"ABC transporter for L-Lysine, ATPase component","Has weaker phenotypes on lysine than the other components, but is in an apparent operon and has the correct domain. Note that this organism has a second ABC transporter operon that is also important for lysine utilization, which is not explained.",amino acid transporter,"Histidine ABC transporter, ATP-binding protein HisP (TC 3.A.1.3.1)",,MAQATPALEIRNLHKRYGQLEVLKGVSLTARDGDVISILGSSGSGKSTFLRCINLLENPNQGQILVAGEELKLKAAKNGELVAADGKQINRLRSEIGFVFQNFNLWPHMSVLDNIIEAPRRVLGQSKAEAVEVAEALLAKVGIADKRHAYPAELSGGQQQRAAIARTLAMQPKVILFDEPTSALDPEMVQEVLSVIRALAEEGRTMLLVTHEMGFARQVSSEVVFLHQGLVEEQGSPQQVFENPLSARCKQFMSSNR transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09900,AO356_09900,AO356_RS09915,WP_060739627.1,tr|A0A0N9WVY2|A0A0N9WVY2_PSEFL,"ABC transporter for L-Lysine, periplasmic substrate-binding component","Specific phenotypes on L-Lysine; L-Lysine. Note that this organism has a second ABC transporter operon that is also important for lysine utilization, which is not explained.",amino acid ABC transporter,Lysine-arginine-ornithine-binding periplasmic protein precursor (TC 3.A.1.3.1),,MQTYKKFLLAAAVSLVFSANAMAADKLKMGIEAAYPPFNNKDASGQVVGFDKDIGDALCAKMKVECEVVTSDWDGIIPALNAKKFDFLISSLSITEERKQAVDFTDPYYSNKLQFIAPKSAEFKTDKDSLKGKVIGAQRATLAGTWLEDELGSDITTKLYDTQENAYLDLTSGRVDAILADKYVNYDWLKTEAGRAYEFKGDPVVESDKIGIAVRKGDNELRNKLNAALKEIVADGTYKKINDKYFPFNIY transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09905,AO356_09905,AO356_RS09920,WP_060739628.1,tr|A0A0N9X7X5|A0A0N9X7X5_PSEFL,"ABC transporter for L-Lysine, permease component 1","Specific phenotypes on L-Lysine; L-Lysine. Note that this organism has a second ABC transporter operon that is also important for lysine utilization, which is not explained.",ABC transporter permease,"Arginine ABC transporter, permease protein ArtQ",polar amino acid transport system permease protein,MMIDLHGFGPALAAGALMTVKLALSALCLGLVLGLLGALAKTSPYKPLQWLGGTYSTLVRGIPELLWVLLIYFGTVNLMRALGEYLGMPDLALNAFAAGVIALGLCFGAYATEVFRGAILAIPKGHREAGVALGLSKWRIFTRLIMPQMWRIALPGLGNLFMILMKDTALVSVIGLEEIMRHAQIGVTVSKQPFTFYMVAALMYLGLTVLAMLGMHLLERRAARGFARSTQ transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_09910,AO356_09910,AO356_RS09925,WP_060739629.1,tr|A0A0N9W704|A0A0N9W704_PSEFL,"ABC transporter for L-Lysine, permease component 2","Specific phenotype on L-Lysine. Note that this organism has a second ABC transporter operon that is also important for lysine utilization, which is not explained.",ABC transporter permease,"Amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MNWDVIIKWLPKLAQGATLTLELVAIAVIAGLLLAIPLGIARSSRLWQVRALPYAYIFFFRGTPLLVQLFLVYYGLAQFDAVRSSALWPYLRDPFWCATVTMTLHTAAYIAEILRGAIQAIPKGEIEAARALGMSRPKALFYIMLPRAARIGLPAYSNEVILMLKASALASTVTLLELTGMARTIIARTYLPVEIFFAAGMFYLLMSFLLVQGFKQLERWLRVDACQGR catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_10715,AO356_10715,AO356_RS10730,WP_060739768.1,tr|A0A0N9WQQ6|A0A0N9WQQ6_PSEFL,2-aminoadipate:2-oxoglutarate aminotransferase (EC 2.6.1.39),Specifically important for: L-Lysine. This is required for catabolism of lysine on the way to glutarate. The amino group donor is uncertain,4-aminobutyrate aminotransferase,5-aminovalerate aminotransferase (EC 2.6.1.48) / Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),,MSKTNADLMARRTAAVPRGVGQIHPIFAESAKNATVTDVEGREFIDFAGGIAVLNTGHVHPKIIAAVTAQLNKLTHTCFQVLAYEPYVEVCEKINAKVPGDFAKKTLLVTTGSEAVENAVKIARAATGRAGVIAFTGAYHGRTMMTLGLTGKVVPYSAGMGLMPGGIFRALYPNELHGVSIDDSIASIERIFKNDAEPRDIAAIIIEPVQGEGGFYVAPKEFMKRLRALCDQHGILLIADEVQTGAGRTGTFFAMEQMGVAADLTTFAKSIAGGFPLAGVCGKAEYMDAIAPGGLGGTYAGSPIACAAALAVMEVFEEEHLLDRCKAVGERLVTGLKAIQKKYPVIGEVRALGAMIAVELFENGDTHKPNAAAVAQVVAKARDKGLILLSCGTYGNVLRVLVPLTSPDEQLDKGLAIIEECFSEL catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_10845,AO356_10845,AO356_RS10860,WP_060739786.1,tr|A0A0N7H1Z1|A0A0N7H1Z1_PSEFL,succinyl-CoA-glutarate CoA-transferase (EC 2.8.3.13),"Specifically important for: L-Lysine. Glutaryl-CoA is an intermediate in lysine catabolism and this reaction would be a way to form it from glutarate before oxidation by AO356_10850. Other reactions in this pathway were also found but the only candidate for glutarate semialdehyde dehydrogenase, AO356_26740, had little phenotype.) Note that this EC number is primarily associated with forming (S)-3-hydroxy-methylglutaryl-CoA.",CoA-transferase,L-carnitine dehydratase/bile acid-inducible protein F,,MGALSHLRVLDLSRVLAGPWAGQILADLGADVIKVERPGNGDDTRAWGPPFLKDARGENTTEAAYYLSANRNKQSVTIDFTRPEGQRLVRELAAKSDILIENFKVGGLAAYGLDYDSLKAINPQLIYCSITGFGQTGPYAKRAGYDFMIQGLGGLMSLTGRPEGDEGAGPVKVGVALTDILTGLYSTAAILAALAHRDHVGGGQHIDMALLDVQVACLANQAMNYLTTGNAPKRLGNAHPNIVPYQDFPTADGDFILTVGNDGQFRKFAEVAGQPQWADDPRFATNKVRVANRAVLIPLIRQATVFKTTAEWVTQLEQAGVPCGPINDLAQVFADPQVQARGLAMELPHLLAGKVPQVASPIRLSETPVEYRNAPPLLGEHTLEVLQRVLGLDEAAVMAFREAGVL catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_12580,AO356_12580,AO356_RS12595,WP_060740066.1,tr|A0A0N9W2U4|A0A0N9W2U4_PSEFL,gamma-glutamyl-gamma-aminobutyraldehyde dehydrogenase (EC 1.2.1.54),"Specifically important for: Putrescine Dihydrochloride; L-Arginine. Part of the gamma-glutamyl-putrescine pathway. The phenotype on arginine is mild and might reflect a side pathway from arginine to putrescine via ornithine decarboxylase. KEGG now annotates it as acting on the 4-guanidino form, and it probably also acts on the 4-amino form (see studies of PA5312 = kauB). Since this enzyme is closely related to kauB it is probably flexible.",aldehyde dehydrogenase,Aldehyde dehydrogenase (EC 1.2.1.3),,MTTLTRTDWEQRARDLKIEGRAFINGEYTDAVSGETFDCLSPVDGRLLGKIASCDVADAQRAVENARATFNSGVWSRLAPSKRKTTMIRFAGLLKQHAEELALLETLDMGKPISDSLNIDVPGAAQALSWSGEAIDKLYDEVAATPHDQLGLVTREPVGVVGAIVPWNFPLMMACWKLGPALSTGNSVVLKPSEKSPLTAIRIAALAIEAGIPKGVLNVLPGYGHTVGKALALHMDVDTLVFTGSTKIAKQLMIYSGESNMKRIWLEAGGKSPNIVFADAPDLQAAAESAASAIAFNQGEVCTAGSRLLVERSIKDTFLPLVIEALKGWKPGNPLDPATNVGALVDTQQMNTVLSYIEAGHSDGAKLVAGGKRILEETGGTYVEPTIFDGVSNAMKIAQEEIFGPVLSVIAFDTAEQAIEIANDTPYGLAAAVWTKDISKAHLTAKALRAGSVWVNQYDGGDMTAPFGGFKQSGNGRDKSLHAFDKYTELKSTWIKL catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13135,AO356_13135,AO356_RS13155,WP_025216023.1,tr|A0A0N9WZ72|A0A0N9WZ72_PSEFL,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Specifically important for: Putrescine Dihydrochloride. This is the first step in putrescine catabolism,gamma-glutamylputrescine synthetase,,glutamine synthetase,MSVPPRAVQLNEANAFLKEHPEVLYVDLLIADMNGVVRGKRIERTSLHKVYEKGINLPASLFALDINGSTVESTGLGLDIGDADRICYPIPDTLCNEPWQKRPTAQLLMTMHELEGEPFFADPREVLRQVVTKFDELGLTICAAFELEFYLIDQENVNGRPQPPRSPISGKRPHSTQVYLIDDLDEYVDCLQDILEGAKEQGIPADAIVKESAPAQFEVNLHHVADPIKACDYAVLLKRLIKNIAYDHEMDTTFMAKPYPGQAGNGLHVHISILDKDGKNIFASEDPEQNAALRHAIGGVLETLPAQMAFLCPNVNSYRRFGAQFYVPNSPTWGLDNRTVALRVPTGSADAVRLEHRVAGADANPYLLMAAVLAGVHHGLVNKIEPGAPVEGNSYEQNEQSLPNNLRDALRELDDSEVMAKYIDPKYIDIFVACKESELEEFEHSISDLEYNWYLHTV catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13140,AO356_13140,AO356_RS13160,WP_060740154.1,tr|A0A0N9W325|A0A0N9W325_PSEFL,Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),Specifically important for: Putrescine Dihydrochloride. this is part of the gamma-glutamyl-putrescine pathway for putrescine catabolism (SEED_correct),gamma-glutamyl-gamma-aminobutyrate hydrolase,Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),putative glutamine amidotransferase,MSRLPLIGVTTCSRQMGLHAYHTSGDKYARAVATAAKGLPVLVPSLADLFPPSDILDALDGILLTGSPSNVEPFHYQGPASAPGTAHDPARDATTLPLIRAAVEAGVPVLGICRGFQEMNVAFGGSLHQKVHEVGTFIDHREDDTQAVEVQYGPAHAVDIQPGGILAGLGLPQSIEVNSIHSQGIERLAPGLRAEAVAPDGLIEAVSVPEGKAFALGVQWHPEWEVSSNPHYLAIFQAFGDACRARATQRDADASNNA catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13150,AO356_13150,AO356_RS13170,WP_060740156.1,tr|A0A0N9WHY7|A0A0N9WHY7_PSEFL,Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),"Specifically important for: Putrescine Dihydrochloride. KEGG annotates this as putrescine aminotransferase, probably based on similarity to spuC (PA0299) of P. aeruginosa, but it is believed that only the g-glutamyl- pathway operates in P. aeruginosa (X. Yao et al 2011, PMC3147493)",aminotransferase,Omega-amino acid--pyruvate aminotransferase (EC 2.6.1.18),putrescine aminotransferase,MTRNNPQTREWQALSNDHHLAPFSDFKQLKEKGPRIITHAKGVYLWDSEGNKILDGMAGLWCVAVGYGREELADAASQQMRELPYYNLFFQTAHPPVLELSKAIADIAPEGMNHVFFTGSGSEGNDTMLRMVRHYWAIKGQPNKKVIISRKNGYHGSTVAGASLGGMTYMHEQGDLPIPGIVHIAQPYWFGEGGDMSPEEFGVWAANQLEEKILEIGVDNVGAFIAEPIQGAGGVIVPPDSYWPRMKEILAKYDILFVADEVICGFGRTGEWFGTDHYGLKPHMMTIAKGLTSGYIPMGGLIVRDDVVAVLNEGGDFNHGFTYSGHPVAAAVALENIRILRDEKIIERVHSETAPYLQKRLRELNDHPLVGEVRGVGLLGAIELVQDKATRKRYEGKGVGMICRQFCFDNGLIMRAVGDTMIIAPPLVISKAEIDELVTKARHCLDLTLSALQG transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13805,AO356_13805,AO356_RS13825,WP_060740268.1,tr|A0A0N9WRY0|A0A0N9WRY0_PSEFL,"ABC transporter for Carnitine, ATPase component",Specific phenotype on Carnitine Hydrochloride. Likely to be a choline or glycine betaine transporter as well (with AO356_13855 as the SBP),choline ABC transporter ATP-binding protein,L-proline glycine betaine ABC transport system permease protein ProV (TC 3.A.1.12.1),glycine betaine/proline transport system ATP-binding protein,MSIIRFEDVDVIFSNRPKAALDLLDKGFSRPEILQQTGLIVGVEKASLSIEKGEICVLMGLSGSGKSSLLRCINGLNTVSRGKLFVEHEGRQIDIASCSPAELKMMRTKRIAMVFQKFALMPWLTVRENISFGLEMQGRPEKERRKLVDEKLELVGLTQWRNKKPDELSGGMQQRVGLARALAMDADILLMDEPFSALDPLIRQGLQDELLELQRKLQKTIVFVSHDLDEALKLGTRIAIMKDGKIIQYSKPEEIVLNPADDYVRTFVAHTNPLNVLCGRSLMRTLDNCKRINGSVCLDPGGDSWLDLAEGNTIKGARQNGAVLDLQNWAPGQSVEELGRRPTLVDSNIGMRDALQIRYQTGNKLVLHDNQKVVGILGDSELYHALLGKNLG transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13810,AO356_13810,AO356_RS13830,WP_060740269.1,tr|A0A0N9WI93|A0A0N9WI93_PSEFL,"ABC transporter for Carnitine, permease component",Specific phenotypes on Carnitine Hydrochloride; Carnitine Hydrochloride.,choline ABC transporter permease subunit,L-proline glycine betaine ABC transport system permease protein ProW (TC 3.A.1.12.1),glycine betaine/proline transport system permease protein,MLIDQKIPLGQYIAGFVEWLTQNGASTFDAIALFLETMIHGVTFALTWFNPLALIGLIALLAHFIQRKWGLTVFVIASFLLILNLGYWQETMETLAQVLFATLVCVLIGVPLGIVAAHKPMFYTMMRPVLDLMQTVPTFVYLIPTLTLFGLGVVPGLISTVVFAIAAPIRLTYLGIRDVPDELMDAGKAFGCSRRQLLSRIELPHAMPSIAAGITQCIMLSLSMVVIAALVGADGLGKPVVNALNTADIALGFEAGLAIVLLAIMLDRICKQPDAKVGGDA transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13815,AO356_13815,AO356_RS13835,WP_060740270.1,tr|A0A0N9WVR6|A0A0N9WVR6_PSEFL,"ABC transporter for Carnitine, periplasmic substrate-binding component 1","Specific phenotype on Carnitine Hydrochloride. This protein is similar to CaiX of P. syringae or P. aeruginosa (Psyr_2916 and PA5388) which has been shown to be a L-carnitine SBP (see PMID:19919675). Another gene in the cluster, AO356_13855, is also a SBP and is specifically important for L-carnitine utilization. AO356_13855 is similar to CbcX of P. aeruginosa or P. syringae (PA5378 or Psyr_4709) which is specific for choline or glycine betaine (ibid). Nevertheless, insertions in _13855 have a specific defect for carnitine utilization. It is possible that these insertions are polar on the beta-cleavage enzyme (_13850) which is required for carnitine catabolism. However insertions in _13855 in either orientation have a strong phenotype.",glycine/betaine ABC transporter substrate-binding protein,L-proline glycine betaine binding ABC transporter protein ProX (TC 3.A.1.12.1),glycine betaine/proline transport system substrate-binding protein,MKGSKPLLLAAMLSLPMLANAADPEKCSTVNFSDVGWTDITVTTATTSVVLDALGYKTKTTMISVPVTYKSLADGKNMDVFLGNWMPTMENDIKAYREAGTVETVRTNLKGAKYTLAVPQALYDKGLHDFADIPKFKKELDGKIYGIEPGNDGNRLIQSMIEKNAFGLKDAGFKVVESSEAGMLSQVDRASKRGTDVVFLGWAPHPMNTRFKIQYLTGGDDFFGPDFGAATVATNTRKGYSQECSNVGQLLKNLEFTVDMESQLMGNVLDDKMKPEAAAKAWLKKNPQVLDTWLAGVTTIDGKPGLEAVKAKLAQ catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_13840,AO356_13840,AO356_RS13860,WP_060740275.1,tr|A0A0N9X9G0|A0A0N9X9G0_PSEFL,"L-carnitine 3-dehydrogenase, thioesterase-like subunit CdhB (EC 1.1.1.108)","Specifically important for: Carnitine Hydrochloride. This is CdhB (PA5385), a subunit of L-carnitine 3-dehydrogenase. See PMC2857723.",4-hydroxybenzoyl-CoA thioesterase,,acyl-CoA thioester hydrolase,MPTLTTYQTRILPEWVDYNGHLRDAFYLLIFSYATDALMDRLGLDSQNREASGHSLFTLELHLNYLHEVKLGAEVQVHTQIIGHDAKRLHLYHSLHLVGGDKELAGNEQMLLHVDLAGPRSAPFTEQTLARLDALLDEQSDLPPPAYIGRVIALPPAR catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_14165,AO356_14165,AO356_RS14185,WP_060740332.1,tr|A0A0N9WIE7|A0A0N9WIE7_PSEFL,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Tween 20. tween 20 hydrolyzes to a mix of C12, C14, and C16 fatty acids; this is probably part of beta oxidation (SEED_correct)",acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MPDYKAPLRDIRFVRDELLGYEAHYQSLPACQDATPDMVDAILEEGAKFCEQVLAPLNRVGDIEGCTWSESGVKTPAGFKEAYKQFVEGGWPSLAHDVEHGGQGLPESLGLAVSEMVGEANWSWGMYPGLSHGAMNTISEHGTPEQQEAYLTKLVSGEWTGTMCLTEPHCGTDLGMLRTKAEPQADGSYKVSGTKIFISAGEHDMADNIVHIVLARLPDAPAGTKGISLFIVPKFLPNADGSIGQRNAVSCGSLEHKMGIHGNATCVMNFDAATGYLIGPANKGLNCMFTFMNTARLGTALQGLAHAEIGFQGGLKYARDRLQMRSLTGPKAPDKAADPIIVHPDVRRMLLTMKAFAEGNRAMVYFTAKQVDIVKYGVDEEEKKKADALLAFMTPIAKAFMTEVGFESANHGVQIYGGHGFIAEWGMEQNVRDSRISMLYEGTTGIQALDLLGRKVLMTQGEALKGFTKIVHKFCQSNEGNEAVKEFVEPLAALNKEWGELTMKVGMAAMKDREEVGAASVDYLMYSGYACLAYFWADMARLAAEKLAAGTTEEAFYTAKLQTARFYFQRILPRTRTHVATMLSGANNLMDMKEEDFALGY catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_14190,AO356_14190,AO356_RS14210,WP_060740336.1,tr|A0A0N9WXM3|A0A0N9WXM3_PSEFL,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Tween 20. tween 20 hydrolyzes to a mix of C12, C14, and C16 fatty acids; this is probably part of beta oxidation (SEED_correct)",acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MADYKAPLRDMRFVLNEVFEVAKLWAQLPVLADTVDAETVEAILEEAGKVTSKSIAPLSRAADEEGCHWANGAVTTPAGFPQAYKTYAEGGWVGVGGDPVYGGMGMPKAVSAQVEEMVNSASLSFGLYPMLTAGACLSINAHASEELKAAYLPNMYAGVWAGSMCLTEPHAGTDLGIIRTKAEPQADGSYKVSGTKIFITGGEHDLTENIIHLVLAKLPDAPAGPKGISLFLVPKFMVNADGSLGARNAANCGSIEHKMGIQASATCVMNFDEAVGYLVGEPNKGLAAMFTMMNYERLGVGIQGLASGERSYQNAVEYARDRLQSRSPTGAQNKDKVADPIIVHPDVRRMLLTMKASNEGGRAFSTYVAMQLDTAKFSEDPVTRKRAEDLVALLTPVAKAFLTDLGLETTVHGQQVFGGHGYIREWGQEQLVRDVRITQIYEGTNGIQALDLVGRKIVGSGGAFYRLFADEIRHFTATASSDLAEFTKPLNDAVGTLDELTDWLLDRAKNNPNEIGAASVEYLQAFGYTAYAYMWALMAKAAFGKENQDDFYASKLGTARFYFARLLPRIHSLSASVKAGSESLFLLEPGQF catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_14225,AO356_14225,AO356_RS14245,WP_060740343.1,tr|A0A0N9WIF6|A0A0N9WIF6_PSEFL,5-aminopentanamidase (EC 3.5.1.30),Specifically important for: L-Lysine. this is an early step in the oxidation of lysine (SEED_correct),carbon-nitrogen hydrolase,5-aminopentanamidase (EC 3.5.1.30),,MRVALYQCPPLPLDPAGNLHRLHQVALEARGADVLVLPEMFMTGYNIGVDAVNVLAEVYNGEWAQQIGRIAKAANLAIVYGYPERGEDGQIYNAVQLIDAQGERLANYRKSHLFGDLDHAMFSAGDSALPIVELNGWKLGLLICYDLEFPENARRLALAGAELILVPTANMQPYEFIADVTVRARAIENQCFVAYANYCGHEAELQYCGQSSIAAPNGSRPALAGLDEALIVGELDRQLLDDSRAAYNYLHDRRPELYDDLHKH catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_14230,AO356_14230,AO356_RS14250,WP_060740344.1,tr|A0A0N9WVY4|A0A0N9WVY4_PSEFL,lysine 2-monooxygenase (EC 1.13.12.2),Specifically important for: L-Lysine. this is the first step in the oxidation of lysine (SEED_correct),amine oxidase,Lysine 2-monooxygenase (EC 1.13.12.2),tryptophan 2-monooxygenase,MNKNNRHPADGKKPITIFGPDFPFAFDDWIEHPAGLGSIPAHNHGTEVAIVGAGIAGLVAAYELMKLGLKPVVYEASKMGGRLRSQAFNGAEGVIAELGGMRFPVSSTAFYHYVDKLGLETKPFPNPLTPASGSTVIDLEGQTYYAQKLADLPALFQEVADAWADALEDGSRFGDIQQAIRDRDVPRLKELWNTLVPLWDDRTFYDFVATSKAFAKLSFQHREVFGQVGFGTGGWDSDFPNSMLEIFRVVMTNCDDHQHLVVGGVEQVPHGIWNHVPERCAHWPEGTSLNSLHLGAPRSGVKRIARSADGRFSVTDVWENTREYAAVLVTCQSWLLTTQIECEEALFSQKMWMALDRTRYMQSSKTFVMVDRPFWKDKDPETGRDLMSMTLTDRLTRGTYLFDNGDDKPGVICLSYSWMSDALKMLPHPVEKRVKLALDALKKIYPKVDIAARIIGDPITVSWEADPHFLGAFKGALPGHYRYNQRMYAHFMQDDMPAEQRGIFIAGDDVSWTPAWVEGAVQTSLNAVWGIMKHFGGETHAENPGPGDVFHEIGPIALPE transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_17535,AO356_17535,AO356_RS17560,WP_060740818.1,tr|A0A0N7H2E9|A0A0N7H2E9_PSEFL,"N-acetylglucosamine-specific PTS system, IIBC components (nagE)",Specific phenotype on NAG as a carbon source. Not sure why SEED think s this includes the IIA component. (KEGG_correct),PTS N-acetyl-D-glucosamine transporter,"PTS system, N-acetylglucosamine-specific IIA component (EC 2.7.1.69) / PTS system, N-acetylglucosamine-specific IIB component (EC 2.7.1.69) / PTS system, N-acetylglucosamine-specific IIC component (EC 2.7.1.69)","PTS system, N-acetylglucosamine-specific IIB component ; PTS system, N-acetylglucosamine-specific IIC component",MYQHFIEGLQRLGRALMLPIAILPIAGLLLRLGDTDLLDIAIIHDAGQAIFANLAMIFAIGIAVGFARDNNGTAGLAGAIGYLVMIATLKVLDASINMGMLAGIISGLLAGALYNRFKDIKLPEYLAFFGGRRFVPIVTGFSAVGLGVVFGLIWPPIQQGINGFGALLMESGSFGAFVFGVFNRLLIVTGLHHILNNMAWFIFGSFTDPATGAVVTGDLSRYFAGDPKGGQFMTGMFPVMLFGLPAACLAMYRNALPQRRKVMGGILLSMALTSFLTGVTEPIEFAFMFLAPLLFLVHALLTGVSMAVTNLLGIHLGFTFSGGFIDMVLGWGKSTNGWLVVPVGLAYAAIYYLVFDFCIRRFDLKTPGREEVPAGDKPAIAENQRAAAYIQALGGADNLITIGACTTRLRLDMVDRNKASDAQLKALGAMAVVRPGNGGSLQVVVGPMADSIADEIRLAVPSSLRPVTAPVPNAPAPTTPAALSSTEAQQWLDALGGQDNVLQLECVATSRLRVRLADDKGLSESRLKGLGCQGMSSLEDGVWHLLLGEKAPRLWQALDGLAHGRKVDAGA transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_17540,AO356_17540,AO356_RS17565,WP_060740819.1,tr|A0A0N9WA75|A0A0N9WA75_PSEFL,"N-acetylglucosamine-specific PTS system, I, HPr, and IIA components (nagF)",Specific phenotype on NAG as a carbon source. Not sure why SEED think s this includes the IIA component. (KEGG_correct),PTS N-acetyl-D-glucosamine transporter,,"PTS system, fructose-specific IIA component ; phosphotransferase system, enzyme I, PtsI ; phosphocarrier protein FPr",MPNNNKELILSAPLSGPVLTLANVPDAVFASGAMGDGIAIDPLNDTLYAPCDAEVIHVARTGHAVTLRADNGAELLLHLGLDTVELQGEGFSMLVKEGARVSHGQPLLRYDVDKVALRCKSLVSLLVITNGEHFQARPITLKGVKVGEPLLHILAKAVGQAEHHDQDSGIEVFGQVRIAHRGGLHARPAALVRQTAQGFKSRSQLHFSGKSASCDSVMGMMGLAITEQAQVHVSCRGSDAEAALQALLTTLSTALVEEAHASAPPPEPPRANAEEGVLHGVCAAPGLVTGPLVRLSGIQLPEDIGGHAIEEQRQRLSDALAQVRGEIHLTLEHARARQHRDEEAIFSAHLALLEDPVLLDAADLFIEQGSAAPHAWSRSIDTQCQVLQQLGSTLLAERANDLRDLRQRVLRVLLGEAWQFDVAAGAIVAAQELTPSDLLQLSAQGVAGVCMVEGGATSHVAILARGKGLPCLVALGDELLAQEQGQAVVLDADGGRLELTPTVERLAQVRQAQTRRTALRAQQQSLAHTPARTVDGVEVEVAANVASSAEAGESLANGADGVGLLRTEFLFVDRHTAPDEEEQRQAYQAVLEAMGDKPVIIRTIDVGGDKQLDYLPLPSEANPVLGLRGIRLAQVRPDLLDQQLRALLQTRPLDRCRILLPMVTEVDELLHIRKRLDALGSELGLSERPQLGVMVEVPAAALLAEQLAEHADFLSIGTNDLSQYTLAMDRDHAGLAARVDALHPALLRLIAQTCAGAAKHGRWVGVCGALASDPLATPVLIGLGVRELSVSPPQIGEIKDRVRHLDAAQCARLSNELLNLGSALAVRRACHRHWPLG transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_17670,AO356_17670,AO356_RS17695,WP_060740842.1,tr|A0A0N9XB41|A0A0N9XB41_PSEFL,L-alanine and D-alanine permease,Specific phenotype on D-alanine and L-alanine,D-alanine/D-serine/glycine permease,D-serine/D-alanine/glycine transporter,"amino acid transporter, AAT family",MPVGNHLPHGETAQGGPLKRELGERHIRLMALGACIGVGLFLGSAKAIEMAGPAIMLSYIIGGLAILVIMRALGEMAVHNPVAGSFSRYAQDYLGPLAGFLTGWNYWFLWLVTCVAEITAVAVYMGIWFPDVPRWIWALAALVSMGSINLIAVKAFGEFEFWFALIKIVTIIAMVIGGVGIIAFGFGNDGVALGISNLWAHGGFMPNGVSGVLMSLQMVMFAYLGVEMIGLTAGEAKNPQKTIPNAIGSVFWRILLFYVGALFVILSIYPWNEIGTQGSPFVMTFERLGIKTAAGIINFVVITAALSSCNGGIFSTGRMLYSLAQNGQAPAGFAKTSTNGVPRRALLLSIAALLLGVLLNYLVPEKVFVWVTSIATFGAIWTWVMILLAQLKFRKSLSASERAALKYRMWLYPVSSYLALAFLVLVVGLMAYFPDTRVALYVGPAFLVLLTVLFYTFKLQPTGDVQRAVRSAS transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_17790,AO356_17790,AO356_RS17815,WP_060740863.1,tr|A0A0N9WZ17|A0A0N9WZ17_PSEFL,alpha-ketoglutarate permease (MHS family),"Specific phenotype on a-ketoglutarate. Not important for the utilization of other dicarboxylates (succinate or malate). 83% identical to PA5530, which is reported to transport a-ketoglutarate and also glutarate (PMC4097582). KEGG_correct",MFS transporter,Dicarboxylate MFS transporter,"MFS transporter, MHS family, alpha-ketoglutarate permease",MDNSNALPIGSAAVPARERTTASRIKSIFSGSVGNMVEWYDWYVYAAFSLYFAKVFFPKGDTTAQLLNTAAIFAVGFLMRPIGGWLMGLYADRAGRKRALMASVYLMCFGSLIIALSPSYETIGVGAPILLVFARLLQGLSVGGEYGTSATYLSEMATKERRGFFSSFQYVTLISGQLIALGVLIVLQQFLTTEQLYAWGWRIPFAIGALCAVVALYLRRGMEETESFTKKEKSKESAMRTLLRHPKELMTVVGLTMGGTLAFYTYTTYMQKYLVNTVGMSISDSTTISAATLFLFMCLQPVIGGLSDKIGRRPILIAFGILGTLFTVPILTTLHTIQTWWGAFFLIMAALIIVSGYTSINAVVKAELFPTEIRALGVGLPYALTVSIFGGTAEYIALWFKSIGMETGYYWYVTACIAVSLLVYITMKDTRKHSRIVTD transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_18530,AO356_18530,AO356_RS18555,WP_060740975.1,tr|A0A0N9WU01|A0A0N9WU01_PSEFL,L-tyrosine transporter,Specific phenotype with tyrosine as the nitrogen source. Not important if phenylalanine is the nitrogen source.,aromatic amino acid transporter,Aromatic amino acid transport protein AroP,"amino acid transporter, AAT family",MSGQNSHSGELKRGLKNRHIQLIALGGAIGTGLFLGSAGVLKSAGPSMILGYAICGFIAFMIMRQLGEMIVEEPVAGSFSHFAHKYWGGFAGFLSGWNCWILYILVGMSELTAVGKYIHYWAPDIPTWVSAAAFFILINAINLANVKVFGEAEFWFAIIKVVAIVGMIALGSYLLVSGHGGPQASVTNLWSHGGFFPNGVSGLVMAMAIIMFSFGGLEMLGFTAAEADKPKTVIPKAINQVIYRILIFYIGALVVLLSLTPWDSLLATLNASGDAYSGSPFVQVFSMLGSNTAAHILNFVVLTAALSVYNSGTYCNSRMLLGMAEQGDAPKALSRIDKRGVPVRSILASAAVTLVAVLLNYLVPQHALELLMSLVVATLVINWAMISYSHFKFRQHMNQTQQTPLFKALWYPYGNYICLAFVVFILGVMLLIPGIQISVYAIPVWVVFMWVCYVIKNKRSARQELAVAAAK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_18700,AO356_18700,AO356_RS18725,WP_060741003.1,tr|A0A0N9WZF1|A0A0N9WZF1_PSEFL,"L-Arginine ABC transporter, periplasmic substrate-binding component",Specific phenotype on L-Arginine. (SEED_correct),ABC transporter substrate-binding protein,"Arginine/ornithine ABC transporter, periplasmic arginine/ornithine binding protein",,MKKLVLLGALALSVLSLPTFADEKPLKIGIEAAYPPFASKAPDGSIVGFDYDIGNALCEEMKVKCVWVEQEFDGLIPALKVRKIDAILSSMSITEDRKKSVDFTNKYYNTPARLVMKAGTQVSDNLAELKGKNIGVQRGSIHERFAREVLAPLGAEIKPYGSQNEIYLDVAAGRLDGTVADATLLDDGFLKTDAGKGFAFVGPAFTDEKYFGDGIGIAVRKGDALKDKINGAITALRENGKYKQIQDKYFAFDIYGK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_18705,AO356_18705,AO356_RS18730,WP_060741004.1,tr|A0A0N9XBK6|A0A0N9XBK6_PSEFL,"L-Arginine ABC transporter, permease component 2",Specific phenotype on L-Arginine. (SEED_correct),ABC transporter,"Arginine/ornithine ABC transporter, permease protein AotQ",arginine/ornithine transport system permease protein,MLKGYGAVILDGAWLTLELALSSMALAIVLGLIGVALRLSPVRWLAWLGDLYSTVIRGIPDLVLILLIFYGGQDLLNRVAPMLGYDDYIDLNPLAAGIGTLGFIFGAYLSETFRGAFMAIPKGQAEAGMAYGMSGFQVFFRVLVPQMIRLAIPGFTNNWLVLTKATALISVVGLQDMMFKAKQAADATREPFTFFLAVAAMYLVITSVSLLALRHLEKRYSVGVRAADL transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_18710,AO356_18710,AO356_RS18735,WP_060741005.1,tr|A0A0N9WA12|A0A0N9WA12_PSEFL,"L-Arginine ABC transporter, permease component 1",Specific phenotypes on L-Arginine; L-Arginine. (SEED_correct),amino acid ABC transporter permease,"Arginine/ornithine ABC transporter, permease protein AotM",,MIFDYNVIYEALPLYFSGLLTTLKLLALSLFFGLLAALPLGLMRVSKQPIVNMTAWLYTYVIRGTPMLVQLFLIYYGLAQFAIVRESFLWPWLSSATFCACLAFAINTSAYTAEIIAGSLRATPNGEIEAAKAMGMSRYKLYRRILLPSALRRALPQYSNEVIMMLQTTSLASIVTLIDITGAARTVNAQYYLPFEAYITAGAFYLCLTFILVRLFKLAERRWLGYLAPRKH transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_18715,AO356_18715,AO356_RS18740,WP_060741006.1,tr|A0A0N9X1K7|A0A0N9X1K7_PSEFL,"L-Arginine ABC transporter, ATPase component",Important for utiliizing L-arginine. Detrimental on L-lysine which might indicate that it leaks lysine. (SEED_correct),amino acid transporter,"Arginine/ornithine ABC transporter, ATP-binding protein AotP",,MYKLEVQDLHKRYGSHEVLKGVSLKAAAGDVISIIGSSGSGKSTFLRCINLLEQPHAGKILLNNEELKLVAGKDGAMKAADPKQLQRMRSRLSMVFQHFNLWSHMTALENIMEAPVHVLGVAKAEAREKAEHYLNKVGVAHRKDAYPGHMSGGEQQRVAIARALAMEPEVMLFDEPTSALDPELVGDVLKVMQALAVEGRTMVVVTHEMGFAREVSNQLVFLHKGIVEESGNPREVLVNPQSERLQQFLSGSLK transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_18980,AO356_18980,AO356_RS19005,WP_060741049.1,tr|A0A0N9WU67|A0A0N9WU67_PSEFL,sodium:C4-dicarboxylate symporter (dctA),"Important for utilization of malate, succinate, or fumarate (all C4 dicarboxylates) as carbon sources. 81% identical to PA1183 from P. aeruginosa (PMC3165536), which is also involved in growth with these substrates. KEGG_correct",C4-dicarboxylate transporter,,aerobic C4-dicarboxylate transport protein,MTTRQPLYKSLYFQVIVAIAIGILLGHFYPQTGVALKPFGDGFIKLIKMVIAPIIFCTVVSGIGGMQNMKSVGKTGGYALLYFEIVSTIALLIGLVVVNVVQPGAGMHIDVSTLDTSKIAGFISAGKDQSIIAFILNVIPNTIVGAFANGDILQVLMFSVLFGFALHRLGAYGKPVLDFIDRFAHVMFIIINMIMKLAPLGAFGAMAFTIGAYGVGSLVQLGQLMICFYITCVVFVLVVLGAICRAHGFSVIKLIRYIREELLIVLGTSSSESALPRMLIKMERLGAKKSVVGLVIPTGYSFNLDGTSIYLTMAAVFIAQATDTPMDLTHQITLLLVLLLSSKGAAGVTGSGFIVLAATLSAVGHLPVAGLALILGIDRFMSEARALTNLVGNAVATIVVAKWVKELDEDQLQTELASGGRGISDVREDDEQIAAAQIAAAETSAPGTVK catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_19460,AO356_19460,AO356_RS19485,WP_060741122.1,tr|A0A0N9W5Z8|A0A0N9W5Z8_PSEFL,allantoinase (EC 3.5.2.5),"Specifically important for: Inosine. This protein is similar to hpxB from Klebsiella, which is allantoinase, an step in the degradation of purines such as adenine or hypoxanthine (inosine is the nucleoside of hypoxanthine). See PMID:19060149",allantoinase,Uricase (urate oxidase) (EC 1.7.3.3),,VSADYPRDLIGYGNNPPHPQWPGNARIALSFVLNYEEGGERNVLHGDKESEAFLSEMVAAQPLQGERNMSMESLYEYGSRAGVWRVLKLFKAFDIPLTIFAVAMAAQRHPDVIRAMVAAGHEICSHGYRWIDYQYMDEAQEREHMLEAIRILTEITGERPLGWYTGRTGPNTRRLVMEEGGFLYDSDTYDDDLPYWEPNTPNGKPHLVIPYTLDTNDMRFTQVQGFNKGDDFFQYLKDAFDVLYAEGAEAPKMLSIGLHCRLIGRPARLASLKRFLEYVKGHEHVWFSRRVDIARHWQQAHPYQGASNPGASK catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_19465,AO356_19465,AO356_RS19490,WP_060741123.1,tr|A0A0N9WUD7|A0A0N9WUD7_PSEFL,5-hydroxyisourate hydrolase [EC: 3.5.2.17],Specifically important for: Inosine. 5-hydroxyisourate is an intermediate in purine degradation (KEGG_correct),5-hydroxyisourate hydrolase,,5-hydroxyisourate hydrolase,MGRLTTHVLDAAHGCPGSAIKVELYRVEGSQLQLVASAQTNSDGRCDAPLLQGEDYRSGVYQLQFHAGDYYRARGVQLPEPAFLDVVVLRFGISAEQEHYHVPLLISPYAYSTYRGS catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_20235,AO356_20235,AO356_RS20260,WP_060741243.1,tr|A0A0N9WBN8|A0A0N9WBN8_PSEFL,L-arabinolactonase (EC 3.1.1.15) / D-galactonolactonase (EC 3.1.1.25),"Specifically important for: L-Arabinose; D-Galactose. The SEED prediction explains the phenotype on L-arabinose but not on D-galactose. L-arabinose and D-galactose are chemically similar and some dehydrogenases act on both substrates, so it is not surprising that a lactonase would hydrolase both of their products. The dehydrogenase is probably AO356_20240. (SEED_correct)",gluconolaconase,L-arabinolactonase (EC 3.1.1.15),,MKWTAVTEHRAKLGEGPFWDAPTQALYWVDIAGKQALRLIGANVEIWQMPEHVSAFIPTQSGDALVTLSSGVYRLDLDSPGLEPRLTLLCMADPQPGNRANEARCDPQGQLWLGTMQNNIGAEGEDLPIEHRSGGLFRVGSDGRVLPLLRGLGIPNTLLWSPDGTTVYFGDSLDGTVYRHFIYPEGNLAPAEVWFGPHPRGGPDGSAMDARGYIWNARWDGSCLLRLTPDGQVDRVIELPVSRPTSCVFGGEDLKTLYITSAASPLGHPLDGAVLSMRVDVPGVACTRFAG catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_20240,AO356_20240,AO356_RS20265,WP_060741244.1,tr|A0A0N7H2L2|A0A0N7H2L2_PSEFL,L-arabinose 1-dehydrogenase (EC 1.1.1.46),"This is specifically important for L-arabinose utilization. Some closely related proteins in other Pseudomonas are important for D-galactose utilization as well, so this is probably also a D-galactose 1-dehydrogenase. However mutants in this gene had little phenotype in D-galactose (two replicates). No other dehydrogenase was identified as specifically important on D-galactose.",short-chain dehydrogenase,3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),,MAEPLVLPPVPEPPKGERLKDKVVLLTGAAQGIGEAIVAAFASQQARLVISDIQAQKVEAVAAHWRERGADVHALQADVSKQQDLQAMARRAVELHGRIDVLVNCAGVNVFRDPLEMTEEDWRRCFAIDLDGAWYGCKAVLPQMIEQGVGSIINIASVHSSHIIPGCFPYPVAKHGLLGLTRALGIEYAPKGVRVNAIAPGYIETQLNVDYWNGFADPHAERQRALDLHPPRRVGQPIEVAMTAVFLASDEAPFINASCITIDGGRSVMYHD catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_21495,AO356_21495,AO356_RS21520,WP_060741450.1,tr|A0A0N9WV93|A0A0N9WV93_PSEFL,gamma-glutamylputrescine oxidase (EC 1.4.3.-),Specifically important for: Putrescine Dihydrochloride. part of the pathway for oxidizing putrescine via gamma-glutamyl-putrescine (KEGG_correct),gamma-glutamylputrescine oxidoreductase,Nucleoside-diphosphate-sugar epimerases,gamma-glutamylputrescine oxidase,MANTPYPESYYAASANPVPPRPALQDDVETDVCVIGAGYTGLSSALFLLENGFKVTVLEAAKVGFGASGRNGGQIVNSYSRDIDVIERSVGPQQAQLLGNMAFEGGRIIRERVAKYQIQCDLKDGGVFAALTAKQMGHLESQKRLWERFGHTQLELLDQRRIREVVACEEYVGGMLDMSGGHIHPLNLALGEAAAVESLGGVIYEQSPAVRIERGASPVVHTPQGKVRAKFIIVAGNAYLGNLVPELAAKSMPCGTQVIATEPLGDELAHSLLPQDYCVEDCNYLLDYYRLTGDKRLIFGGGVVYGARDPANIEAIIRPKMLKAFPQLKDVKIDYAWTGNFLLTLSRLPQVGRLGDNIYYSQGCSGHGVTYTHLAGKVLAEALRGQAERFDAFADLPHYPFPGGQLLRTPFAAMGAWYYGLRDKLGF catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_21640,AO356_21640,AO356_RS21665,WP_060741475.1,tr|A0A0N9WCD3|A0A0N9WCD3_PSEFL,acetyl-CoA C-acetyltransferase [EC: 2.3.1.9],"Specifically important for: L-Lysine; Carnitine Hydrochloride. acetyl-CoA acetyltransferase is required for the breakdown of acetoacetyl-CoA, which is an intermediate in lysine degradation (via glutaryl-CoA) and in carnitine degradation (from the beta cleavage enzyme, see PMID: 24240508) (KEGG_correct)",acetyl-CoA acetyltransferase,,acetyl-CoA C-acetyltransferase,MQEVVIVAATRTAIGSFQGSLAAIPAPELGAAVIRRLLEQTGLSGEQVDEVILGQVLTAGSGQNPARQASILAGLPHAVPALTLNKVCGSGLKALHLGAQAIRCGDAEVIIAGGMENMSLAPYVLPAARTGLRMGHAKMIDSMITDGLWDAFNDYHMGITAENLVDKYGISREEQDAFAAASQQKAVAAIEGGRFADEITPILIPQRKGDPVAFATDEQPRAGTTAESLGKLKPAFKKDGSVTAGNASSLNDGAAAVILMSAEKAKALGLPVLAKISAYANAGVDPAIMGIGPVSATRRCLDKAGWSLEQLDLIEANEAFAAQSLAVARELKWDMDKVNVNGGAIALGHPIGASGCRVLVSLLHEMIKRDAKKGLATLCIGGGQGVALALERA catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_23175,AO356_23175,AO356_RS23200,WP_060741734.1,tr|A0A0N9X3S4|A0A0N9X3S4_PSEFL,malonate-semialdehyde dehydrogenase (acetylating) (EC 1.2.1.18),"Specifically important for: m-Inositol. 3-oxopropionate or malonate semialdehyde is an intermediate in myo-inositol catabolism (SEED_correct, although the name and EC number are confusing)",methylmalonate-semialdehyde dehydrogenase,Methylmalonate-semialdehyde dehydrogenase [inositol] (EC 1.2.1.27),methylmalonate-semialdehyde dehydrogenase,MSDAPVVGHYIDGRIQASDNARLSNVFNPATGAVQARVALAEPSTVDAAVASALAAFPAWSEQSSLRRSRVMFKFKELLDRHHDELAQIISREHGKVLSDAHGEVTRGIEIVEYACGAPNLLKTDFSDNIGGGIDNWNLRQPLGVCAGVTPFNFPVMVPLWMIPLALVAGNCFILKPSERDPSASLLMARLLTEAGLPDGVFNVVQGDKVAVDALLQHPDIEAISFVGSTPIAEYIHQQGTAQGKRVQALGGAKNHMIVMPDADLDQAADALIGAAYGSAGERCMAISIAVAVGDVGDELIAKLLPRIDQLKIGNGQQPGTDMGPLVTAEHKAKVEGFIDAGVAEGARLIVDGRGFKVPGAEQGFFVGATLFDQVTAEMSIYQQEIFGPVLGIVRVPDFATAVALINAHEFGNGVSCFTRDGGIARAFARSIKVGMVGINVPIPVPMAWHSFGGWKRSLFGDHHAYGEEGLRFYSRYKSVMQRWPDSIAKGPEFSMPTAQ catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_24585,AO356_24585,AO356_RS24610,WP_060741989.1,tr|A0A0N9WWL3|A0A0N9WWL3_PSEFL,L-arabonate dehydratase (EC 4.2.1.25),Specifically important for: L-Arabinose. L-arabonate is an intermediate in the oxidation of L-arabinose (SEED_correct),dihydroxy-acid dehydratase,L-arabonate dehydratase (EC 4.2.1.25),dihydroxy-acid dehydratase,MSDKKPSLRSAQWFGTADKNGFMYRSWMKNQGIADHQFHGKPIIGICNTWSELTPCNAHFRQIAEHVKRGVIEAGGFPVEFPVFSNGESNLRPTAMLTRNLASMDVEEAIRGNPIDGVVLLTGCDKTTPALLMGAASCDVPAIVVTGGPMLNGKHKGQDIGSGTVVWQLSEQVKAGTITIDDFLAAEGGMSRSAGTCNTMGTASTMACMAEALGTSLPHNAAIPAVDARRYVLAHMSGMRAVEMVREDLKLSKILTKEAFENAIRVNAAIGGSTNAVIHLKAIAGRIGVQLDLDDWTRIGRGMPTIVDLQPSGRFLMEEFYYAGGLPAVLRRLGEANLIPNPNALTVNGKSLGENTKDAPIYGQDEVIRTLDNPIRADGGICVLRGNLAPLGAVLKPSAATAELMQHRGRAVVFENFDEYKARINDPELDVDANSILVMKNCGPKGYPGMAEVGNMGLPAKLLAQGVTDMVRISDARMSGTAYGTVVLHVAPEAAAGGPLAAVKEGDWIELDCASGRLHLDIPDAELAARLADLAPPQQLLVGGYRQLYIDHVLQADQGCDFDFLVGCRGAEVPRHSH catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_24595,AO356_24595,AO356_RS24620,WP_060741991.1,tr|A0A0N9WN17|A0A0N9WN17_PSEFL,2-dehydro-3-deoxy-L-arabinonate dehydratase (EC 4.2.1.43),"# Specifically important in carbon source L-Arabinose. Similar to PA2216 from Pseudomonas aeruginosa (see PMC:PMC4038344) and to gguC or araD1 (Atu2345) from Agrobacterium tumefaciens (see PMC: PMC3232879), which also have this activity. In Agrobacterium, this reaction is proposed to be a step in L-arabinose oxidation. (Note that 2-keto-3-deoxy-L-lyxonate and 2-keto-3-deoxy-L-arabinonate are the same compound.)",FAH family protein,SUGAR TRANSPORTER,,MRLVQFELSNGERRVGVVDGDQVREVQGAGSVRELALAAIEAGVKLEQQVDSLGLGTGHDYARLLGELRILPPLDHPDPAHLLVSGTGLTHLGSASARDKMHQQAGDEATMTDTMRIFKWGVEGGKPQAGQTGVQPEWFYKGDGSIVVRPGHPFPLPPFAEDAGEEPEISGLYVIGHDGKPYRLGFAVGNEFSDHVMERKNYLYLAHSKLRSCSFGPELRVGELPQHLSGTSRILRNGEVLWQNEFLSGEANMCHSLENLEFHHFKYSQFLRPGDVHVHFFGTATLSFADGIRTQPGDVFEISQAEFGAPLVNGIAPVDAVFNPGTIGTL catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_28020,AO356_28020,AO356_RS28045,WP_060742590.1,tr|A0A0N7H343|A0A0N7H343_PSEFL,ethanol oxidation regulatory protein ercA,Specifically important for: Ethanol. Similar to ercA (PA1991) which apparently has a regulatory role (PMCID: PMC3754586) rather than being directly involved in catabolism. AO356_28055 is probably the main ethanol dehydrogenase. (not an enzyme),alcohol dehydrogenase,Alcohol dehydrogenase (EC 1.1.1.1); Acetaldehyde dehydrogenase (EC 1.2.1.10),,MSHVTEKMSLSPLRKFVSPEIMFGAGCRHNVGNYAKTFGARKVLIVTDPGVIAAGWVADVEASLQAQGIDYCIYSAVSPNPRVEEVMLGADLYRENHCDVIVAVGGGSPMDCGKGIGIVVAHGRSILEFEGVDTLNVPSPPLILIPTTAGTSADVSQFVIISNQQERMKFSIVSKAAVPDVSLIDPETTLSMDPFLSACTGIDALVHAIEAFVSTGHGPLTDPHALEAMRLINGNLVQMIANPADIALREKIMLGSMQAGLAFSNAILGAVHAMSHSLGGFLDLPHGLCNAVLVEHVVAFNYNSAPERFKVIAETLGIDCRGLNHREIRTRLVEHLIALKHTIGFHETLGLHGVSTSDIPFLSQHAMHDPCILTNPRESSQRDVEVVYGEAL transporters,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_28540,AO356_28540,AO356_RS28565,WP_060742692.1,tr|A0A0N9WPT3|A0A0N9WPT3_PSEFL,D-mannose and D-mannitol transporter,Specific phenotype on D-mannose and D-mannitol as carbon sources. No other transporter for these substrates was apparent in the fitnes data.,hexuronate transporter,Hexuronate transporter,,MFGQGRSLIIIMLFLAGVINYLDRSALSVAAPFIQKDYGLSTGEMGMIFSSFFVGYAAFNFIGGWAADRYGAKTTLLLAMVLWSLFSGLTVLTVGFASLVLIRILFGMGEGPLSVTTSKMVNNWYTPKRRARAIGASMSGTPLGGAISGPVVGFIAVTYGWKISFIIIMLIGLVWAAVWFKFVKERPEGEGAEDILRAEGQGELAAQPVFPLRFYLKQPTVLFTSLAFFSYNYTLFFFLTWFPSYLTMAHGLNVKDMSIATVIPWVLGFLGLALGGFISDFVFKKTGRMMFSRKVVLVTCLLACAVCIACAGMVTTLYPAVILVALAVFFLYLTGAIYWAIIQDTVPAARVGGVSGFMHFLANTSGIVGPTLTGFLVQFTGSFTSAFLLAGLLTVIGAVCVARYVKPLSVADTGNAAAQSPQPVSALGRS catabolism,Pseudomonas fluorescens FW300-N2C3,pseudo5_N2C3_1,AO356_28590,AO356_28590,AO356_RS28615,WP_060742701.1,tr|A0A0N7H357|A0A0N7H357_PSEFL,Sucrose-6-phosphate hydrolase (EC 3.2.1.B3),"Specifically important for: Sucrose. This gene is cofit with a sugar PTS system (AO356_07325; annotated as fructose specific but also apparently active on sucrose), which suggests that sucrose-6-phosphate is the correct substrate. The KEGG annotation (beta-fructofuranosidase) is broader but also includes this activity. (SEED_correct)",glycosyl hydrolase family 32,Sucrose-6-phosphate hydrolase (EC 3.2.1.B3),beta-fructofuranosidase,MTLSLNSLSNPMPASLEHAQQALRDGLSRLIHDYRPDYHLAPPTGWMNDPNGVVFFRGEYHVFYQHHPFDAKWGPMYWGHAKSADLVHWQHLPIALAPGDDFDRDGCFSGSAVVCGDTLALIYTGHTWLGEVGDERLIRQVQCLATSVDGVRFVKHGAVIEDAPQAAIMHFRDPKVWQEDGYWYLIAGARLGDTPLLPLYRSTDLRAWEFLDYVSRGTDGDGYMWECPDLFRLDGRDVLLYSPQGMQPAGYERLNKYQTGYRIGHLDSEWHFSGGPFIELDNGHDFYAAQTLVAADGRRLVWAWLDMWESPMPSQAHHWCGMLGLPRELELQGDRLGVFPARELVALRQAPLPSIAPWGESGSRWVPQVSGDRLEIHVHLDLLDCTEGHLGIALRCSADEQEQTLLYYDASLRRLVLDRSRSGAQVSGQRSVPIEPAQTQLHLRVFLDRSSIEVFEQSGRFSLSSRLYPRPDSLGVKLLASGTGGHVAISNAWPLASGWL catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_1146,,TK06_RS05765,WP_063321224.1,tr|A0A159ZWN4|A0A159ZWN4_PSEFL,2-methylbutanoyl-CoA dehydrogenase (EC 1.3.8.1),Specifically important for: L-Isoleucine. 2-methylbutanoyl-CoA is an intermediate in isoleucine catabolism.,Butyryl-CoA dehydrogenase (EC 1.3.99.2),Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MLATEEQTQIRDMARQFAEERLKPFAAEWDREHRFPREAIDEMAELGFFGMLVPEQWGGCDTGYLAYAMTLEEIAAGDGACSTIMSVHNSVGCVPILKFGNDEQKAKFLTPLASGAMLGAFALTEPQAGSDASSLKTRARLEGDHYVLNGCKQFITSGQNAGVVIVFAVTDPSAGKRGISAFIVPTDSPGYSVARVEDKLGQHASDTCQILFEDLKVPVGNRLGEEGEGYKIALANLEGGRVGIAAQAVGMARAAFEAARDYARERSSFGKPIIEHQAVAFRLADMATQIAVARQMVHYAAALRDSGQPALVEASMAKLFASEMAEKVCSMALQTLGGYGYLNDFPLERIYRDVRVCQIYEGTSDIQRMVISRNL transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_1960,,TK06_RS09830,WP_063321903.1,tr|A0A159ZWP3|A0A159ZWP3_PSEFL,"ABC transporter for D-sorbitol, ATPase component",# Specifically important in carbon source D-Sorbitol,"Various polyols ABC transporter, ATP-binding component","Various polyols ABC transporter, ATP-binding component",,MATLKIENLKKGFEGLSIIKGIDLEVKDKEFVVFVGPSGCGKSTLLRLIAGLEDVTSGTIELDGRDITEVTPAKRDLAMVFQTYALYPHMTVRKNLSFALDLAGEKKPDVERKVAEAARILELGSLLDRKPKQLSGGQRQRVAIGRAIVRNPKIFLFDEPLSNLDAALRVQTRLELSRLHKELQATMIYVTHDQVEAMTLATKVVVLNAGRIEQIGSPLELYHHPANLFVAGFLGTPKMGFLQATVHAVHASGVEVRFASGTTLLIPRDSSALSVGQSVTIGIRPEHLTLSAEGQVPVTTDVTERLGSDTFCHVNVDSGESLTVRVQGDCEVPYAARRYLTLDVAHCHLFDESGLSVSPAASRAA transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_1961,,TK06_RS09835,WP_063321904.1,tr|A0A159ZW38|A0A159ZW38_PSEFL,"ABC transporter for D-sorbitol, permease component 2",# Specifically important in carbon source D-Sorbitol. We do not have fitness data for the putative first subunit Pf6N2E2_1962.,"Various polyols ABC transporter, permease component 2","Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MMTLKQSRSLQSLLLGTLAWVAALLLFFPIFWMVLTSFKTEIDAFATPPQFIFMPTLENYLHIQERSDYFHFAWNSVLISFSATALCMLIAVPAAYSMAFYETKRTKQTLLWMLSTKMLPPVGVLMPIYLLAKGAGLLDTRIALIVIYTLINLPIVVWMIYTYFKDIPREILEAARLDGATLGQEMLRVLLPISKGGLASTMLLSMILCWNEAFWSLNLTSSSAAPLTALIASYSSPEGLFWAKLSAVSTLACAPILIFGWISQKQLVRGLSFGAVK transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_1963,,TK06_RS09845,WP_063321905.1,tr|A0A159ZYG7|A0A159ZYG7_PSEFL,"ABC transporter for D-sorbitol, periplasmic substrate-binding component",# Specifically important in carbon source D-Sorbitol,"Various polyols ABC transporter, periplasmic substrate-binding protein","Various polyols ABC transporter, periplasmic substrate-binding protein",,MKITNALILSTGLSFALASHAAETLTIATVNNGDMIRMQRLSKVFEQQHPDIKLSWVVLEENVLRQRLTTDIATQGGQFDVLTIGTYETPMWGAKNWLEPMKDLPAGYDVDDIFPAVRQGLSVNDTLYALPFYGESTITYYRTDLFKAAGLTMPAQPTWSQLGEFAAKLNDPSKDQYGMCLRGKAGWGENMALLTTMANAFGARWFDEKWQPELNGPEWKAAATFYVDTLKKYGPPGVSSNGFNETLALFNSGKCAIWVDASVAGSFTTDKEQSRVVDSVGFAPAPTEVTDKGSSWLYAWSLAIPATSKHKEAAKSFVTWATSKEYIQLVTDKDGITNVPPGTRISTYSDAYLKAAPFAQVTLQMMKHADPSQPSAKPVPYVGIQYVVIPEFQSIGTSVGKLFSAALTGQMSVEQALASAQSTTEREMKRAGYPKK catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2047,,TK06_RS10360,WP_063321988.1,tr|A0A159ZWW4|A0A159ZWW4_PSEFL,D-glucosaminate dehydratase (EC 4.3.1.9),"Specifically important for: D-Glucosamine Hydrochloride. This enzyme had not previously been linked to a gene. This is the second step in catabolism of glucosamine, and the 'beta' form of the enzyme was expected to be PLP-dependent and about this size. Iwamoto et al (2003) purified a non-specific 'alpha' enzyme for this reaction (PMID: 12686150)",D-serine deaminase (EC 4.3.1.18),D-serine deaminase (EC 4.3.1.18),,MSSVIPNAGVEKGAATVGAHLLKDVSLPALVLHRAALEHNIRWMQAFVTDSGAELAPHGKTSMTPALFRRQLDAGAWGLTLASAVQTRAAYAHGVRRVLMANQLVGTPNMALIADLLADPAFEFHCMVDHPDNVADLGAFFASRGMKLNVMIEYGVVGGRCGCRTEAEVLALAEAIQAQPALALTGIEGYEGVIHGDHAISGIRAFAASLVRLAVQLQDNGAFAIDKPIITASGSAWYDLIAESFEAQNAHGRFLSVLRPGSYVAHDHGIYKEAQCCVLERRSDLHEGLRPALEVWAHVQSLPEPGFAVIALGKRDVAYDAGLPVPLKRYTPGSDSVTGDDVSGCKVTAVMDQHAFMNVAAGVELRVGDIISFGTSHPCLTFDKWRVGCLVDEQLRVVESMETCF transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2051,,TK06_RS10380,WP_028236639.1,tr|A0A0N9WS63|A0A0N9WS63_PSEFL,"ABC transporter for D-Glucosamine Hydrochloride, permease component 1",Specific phenotypes on D-Glucosamine Hydrochloride.,"Amino acid ABC transporter, permease protein","Amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MYESPSWLHELWVARDTLWAGFLTSVQCSLLAIVLGTLIGLVAGLVLTYGRTWMRAPFRFYVDLIRGTPVFVLVLACFYMAPALGWQIGAFQAGVLGLTLFCGSHVAEIVRGALQALPRGQMEASQAIGLTFYQSLGYVLLPQALRQILPTWVNSSTEIVKASTLLSVIGVAELLLSTQQIIARTFMTLEFYLFAGFLFFIINYAIELLGRHIEKRVALP transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2052,,TK06_RS10385,WP_014338685.1,tr|A0A159ZWH7|A0A159ZWH7_PSEFL,"ABC transporter for D-Glucosamine Hydrochloride, permease component 2",Specific phenotypes on D-Glucosamine Hydrochloride; D-Glucosamine Hydrochloride. ko:K02029 : polar amino acid transport system permease protein,"amino acid ABC transporter, permease protein","amino acid ABC transporter, permease protein",polar amino acid transport system permease protein,MNYQLNFAAVWRDFDTLLAGLGLGLSLALVSIAIGCVIGLAMAFALLSKHRVLRVLASVYVTVIRNTPILVLILLIYFALPSLGIRLDKLPSFVITLSLYAGAYLTEVFRGGLLSIHKGQREAGLAIGLGEWQVKAYVTVPVMLRNVLPALSNNFISLFKDTSLAAAIAVPELTYYARKINVESYRVIETWLVTTALYVAACYLIAMLLRYFEQRLAIRR transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2053,,TK06_RS10390,WP_063321992.1,tr|A0A159ZUY1|A0A159ZUY1_PSEFL,"ABC transporter for D-Glucosamine, periplasmic substrate-binding component",Specific phenotype on D-Glucosamine Hydrochloride.,"Glutamine ABC transporter, periplasmic glutamine-binding protein (TC 3.A.1.3.2)","Glutamine ABC transporter, periplasmic glutamine-binding protein (TC 3.A.1.3.2)",,MQRRPSLFKTCVFLFAATVAAVGVAQAADSKLDSVLQRGKLIVGTGSTNAPWHFQGADGKLQGFDIDIARMVAKGLFNDPEKVEFVVQSSDARIPNLLTDKVDMSCQFITVTASRAQQVAFTLPYYREGVGLLLPANSKYKEIEDLKAAGDDVTVAVLQNVYAEELVHQALPKAKVDQYDSVDLMYQAVNSGRADAAATDQSSVKYLMVQNPGRYRSPAYAWSPQTYACAVKRGDQDWLNFVNTTLHEAMTGVEFPTYAASFKQWFGVELPSPAIGFPVEFK catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2111,,TK06_RS10665,WP_063322042.1,tr|A0A159ZX12|A0A159ZX12_PSEFL,"Dehydrocarnitine CoA-transferase and acetoacetate CoA-transferase, subunit A","Very important on cartinine and also important on L-phenylalanine and L-leucine. Acts on dehydrocarnitine during carnitine catabolism to give dehydrocarnitine-CoA. Probably also acts on acetoacetate, which is an intermediate in the degradation of phenylalanine (via tyrosine and homogentistate) and leucine (from HMG-CoA lyase). Both substrates are 3-oxoacids, but not sure if succinyl-CoA is the source of the CoA as annotated. Note that acetoacetyl-CoA is probably cleaved to acetyl-CoA by _2113 (downstream). Similar to DhcA = PA1999.",Succinyl-CoA:3-ketoacid-coenzyme A transferase subunit A (EC 2.8.3.5),Succinyl-CoA:3-ketoacid-coenzyme A transferase subunit A (EC 2.8.3.5),3-oxoacid CoA-transferase subunit A,MAGFDKRVSSYEEALEGLKDGMTVIAGGFGLCGIPENLIAEIKRKGIRDLTVVSNNCGVDGFGLGVLLEDRQIRKVVASYVGENALFEQQLLSGEIEVVLTPQGTLAEKMRAGGAGIPAFFTATGVGTPVAEGKEVREFHGRQYLMEESITGDFAIVKGWKADHFGNVIYRHTAQNFNPLAATAGKITVVEVEEIVEPGELDPTQIHTPGIYVDRVICGTFEKRIEQRTVRK catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2112,,TK06_RS10670,WP_003180355.1,tr|A0A0D9AX93|A0A0D9AX93_PSEFL,"Dehydrocarnitine CoA-transferase and acetoacetate CoA-transferase, subunit B","Specifically important for: Carnitine Hydrochloride; L-Phenylalanine. Also important on L-leucine. Converts dehydrocarnitine to dehydrocarnitine-CoA during carnitine catabolism. Probably also acts on acetoacetate, which is an intermediate in the degradation of phenylalanine (via tyrosine and homogentistate) and leucine (from HMG-CoA lyase). Both substrates are 3-oxoacids, but not sure if succinyl-CoA is the source of the CoA as annotated. Similar to DhcB = PA2000.",Succinyl-CoA:3-ketoacid-coenzyme A transferase subunit B (EC 2.8.3.5),Succinyl-CoA:3-ketoacid-coenzyme A transferase subunit B (EC 2.8.3.5),3-oxoacid CoA-transferase subunit B,MALSREQMAQRVAREMQDGYYVNLGIGIPTLVANYIPEGMEVMLQSENGLLGMGAFPTEAEVDADMINAGKQTVTARIGASIFSSAESFAMIRGGHIDLTVLGAFEVDVEGNIASWMIPGKLVKGMGGAMDLVAGAENIIVTMTHASKDGESKLLPRCSLPLTGAGCIKRVLTDLAYLEIQDGAFILKERAPGVSVEEIVAKTAGKLIVPDHVPEMQFAAQ catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2632,,TK06_RS13235,WP_063322430.1,tr|A0A159ZXT3|A0A159ZXT3_PSEFL,tyrosine aminotransferase (EC 2.6.1.57),Specifically important for: L-Phenylalanine. Phenylalanine is first hydroxylated to tyrosine (Pf6N2E2_2630) and then transaminated. KEGG has same EC# but less specific. (KEGG_correct),Aspartate aminotransferase (EC 2.6.1.1),Aspartate aminotransferase (EC 2.6.1.1),aromatic-amino-acid transaminase,MHFDAIGRVPGDPILGLMEAYGADANPSKFDLGVGVYKDAQGLTPILQSVKQAEQRLVDRQTTKTYIGGHGDAAFGQLINELVLGADSPLISAKRAGATQTPGGTGALRLSADFIAQCLPGRGVWLSNPTWPIHETIFAAAGVKVGHYPYVGADNRLDFEAMLATLNQAPKGDVVLLHACCHNPTGFDLSHEQWRQVLEVVRDRDLLPLIDFAYQGFGDGLEQDAWAVRLFAQALPEVLVTSSCSKNFGLYRDRTGALIVCARDAEKLVDIRSQLANIARNLWSTPPDHGAAVVATILGNPELKSLWADEVQAMRLRIAQLRSGLLEALEPHGLRERFAHIGVQRGMFSYTGLTPEQVKHLRERHSVYMVGTGRANVAGIDATRLDLLAEAIADACK transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2958,,TK06_RS14820,WP_063322689.1,tr|A0A159ZWV1|A0A159ZWV1_PSEFL,"ABC transporter for L-Lysine, periplasmic substrate-binding component",Specific phenotype on L-Lysine; Gly-Glu. There is another dedicated transporter for the dipeptide gly-glu so the basis of that phenotype is unclear.,Lysine-arginine-ornithine-binding periplasmic protein precursor (TC 3.A.1.3.1),Lysine-arginine-ornithine-binding periplasmic protein precursor (TC 3.A.1.3.1),lysine/arginine/ornithine transport system substrate-binding protein,MKKALLTLSALALCMAAGVATAKEYKELRFGVDPSYAPFESKAADGSLVGFDIDLGNAICAELKVKCKWVESDFDGMIPGLKANKFDGVISSMTVTPAREKAIDFSSELFSGPTAYVFKKGSGLSEDVASLKGKTVGYEQGTIQEAYAKAVLDKAGVKTQAYQNQDQVYADLTSGRLDAAIQDMLQAELGFLKSPKGEGYEVSKPVDSELLPSKTAIGIRKGNSELKALLNKGIKALHDDGKYAEIQKKHFGDLNLYSGK transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2959,,TK06_RS14825,WP_063322690.1,tr|A0A159ZWN7|A0A159ZWN7_PSEFL,"ABC transporter for L-Lysine, permease component 1",Specific phenotypes on L-Lysine. Also important on gly-glu. There is another dedicated transporter for the dipeptide gly-glu so the basis of that phenotype is unclear.,"Histidine ABC transporter, permease protein HisQ (TC 3.A.1.3.1)","Histidine ABC transporter, permease protein HisQ (TC 3.A.1.3.1)",histidine transport system permease protein,MFEQLLQNLGLSAFSLQGFGPLLMQGTWMTIKLSALSLLLSVLLGLLGASAKLSSVKLLRIPAQLYTTLIRGVPDLVLMLLIFYSLQTWLTSLTDFMEWEYIEIDPFGAGVITLGFIYGAYFTETFRGAILSVPRGQVEAATAYGLKRGQRFRFVVFPQMMRFALPGIGNNWMVMLKATALVSIIGLADLVKAAQDAGKSTYQLFYFLVLAALIYLLITSASNFILRWLERRYAAGAREAVR transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2960,,TK06_RS14830,WP_063322691.1,tr|A0A165ZC74|A0A165ZC74_PSEFL,"ABC transporter for L-Lysine, permease component 2",Specific phenotype on Gly-Glu; L-Lysine. There is another dedicated transporter for the dipeptide gly-glu so the basis of that phenotype is unclear.,"Histidine ABC transporter, permease protein HisM (TC 3.A.1.3.1)","Histidine ABC transporter, permease protein HisM (TC 3.A.1.3.1)",histidine transport system permease protein,MIELLQEYWRPFLYSDGVNVTGLAMTLWLLSASLLIGFLVSIPLSIARVSPKFYVRWPVQFYTYLFRGTPLYIQLLICYTGIYSIAAVRAQPLLDSFFRDAMNCTILAFALNTCAYTTEIFAGAIRSMNHGEVEAAKAYGLTGWRLYTYVIMPSALRRSLPYYSNEVILMLHSTTVAFTATVPDVLKVARDANSATFLTFQSFGIAALIYLTVTFALVGLFRLAERRWLAFLGPTH transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_2962,,TK06_RS14840,WP_003198401.1,tr|A0A159ZYQ4|A0A159ZYQ4_PSEFL,"ABC transporter for L-Lysine, ATPase component",Very important for L-lysine and gly-glu utilization. There is another dedicated transporter for the dipeptide gly-glu so the basis of that phenotype is unclear.,"Histidine ABC transporter, ATP-binding protein HisP (TC 3.A.1.3.1)","Histidine ABC transporter, ATP-binding protein HisP (TC 3.A.1.3.1)",histidine transport system ATP-binding protein,MYKLTIEGLHKSYGEHEVLKGVSLKAKTGDVISLIGASGSGKSTFLRCINFLEQPNDGAMSLDGQPIQMIKDRHGMHVADADELQRIRTRLAMVFQHFNLWSHMTVLENITMAPRRVLGVSKQEADDRARRYLDKVGLPARVAEQYPAFLSGGQQQRVAIARALAMEPEVMLFDEPTSALDPELVGEVLKVIQGLAEEGRTMIMVTHEMSFARKVSNQVLFLHQGLVEEEGRPEDVLGNPTSERLKQFLSGNLK catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4383,,TK06_RS21985,WP_063323808.1,tr|A0A159ZZQ3|A0A159ZZQ3_PSEFL,gamma-glutamyl-gamma-aminobutyraldehyde dehydrogenase (EC 1.2.1.54),Important on putrescine; this is the dehydrogenase reaction in the gamma-glutamyl-putrescine pathway. KEGG now annotates it as acting on the 4-guanidino form and it may also act on 4-aminobutyraldehyde (based on studies of PA5312 = kauB). Since this enzyme is closely related to kauB it is probably flexible.,Aldehyde dehydrogenase (EC 1.2.1.3),Aldehyde dehydrogenase (EC 1.2.1.3),,MTTLTRADWEQRARDLKIEGRAFINGEYTDAVSGETFDCLSPVDGRLLGKIASCDVADAQRAVENARATFSSGVWSRLAPSKRKATMIRFAGLLKQHAEELALLETLDMGKPISDSLNIDVPGAAQALSWSGEAIDKLYDEVAATPHDQLGLVTREPVGVVGAIVPWNFPLMMACWKLGPALSTGNSVVLKPSEKSPLTALRIAALAIEAGIPKGVLNVLPGYGHTVGKALALHMDVDTLVFTGSTKIAKQLMIYSGESNMKRIWLEAGGKSPNIVFADAPDLQAAAESAASAIAFNQGEVCTAGSRLLVERSIKDTFLPLVIEALKGWKPGNPLDPATNVGALVDTQQMNTVLSYIEAGHSDGAKLVAGGKRILEETGGTYVEPTIFDGVSNAMKIAQEEIFGPVLSVIAFDTAEQAIEIANDTPYGLAAAVWTKDISKAHLTAKALRAGSVWVNQYDGGDMTAPFGGFKQSGNGRDKSLHAFDKYTELKSTWIKL catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4509,,TK06_RS22580,WP_014340747.1,tr|A0A161H3M9|A0A161H3M9_PSEFL,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Specifically important for: Putrescine Dihydrochloride. This synthetase reaction is the first step in putrescine catabolism,glutamine synthetase family protein,,glutamine synthetase,VVRGKRIERTSLHKVYEKGINLPASLFALDINGSTVESTGLGLDIGDADRICYPIPDTLCNEPWQKRPTAQLLMTMHELEGEPFFADPREVLRQVVTKFDELGLTICAAFELEFYLIDQENVNGRPQPPRSPISGKRPHSTQVYLIDDLDEYVDCLQDILEGAKEQGIPADAIVKESAPAQFEVNLHHVADPIKACDYAVLLKRLIKNIAYDHEMDTTFMAKPYPGQAGNGLHVHISILDKDGKNIFASEDPEQNAALRHAIGGVLETLPAQMAFLCPNVNSYRRFGAQFYVPNSPTWGLDNRTVALRVPTGSADAVRLEHRVAGADANPYLLMASVLAGVHHGLVNKIEPGAPVEGNSYEQNEQSLPNNLRDALRELDDSEVMAKYIDPKYIDIFVACKESELEEFEHSISDLEYNWYLHTV catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4510,,TK06_RS31160,WP_086936699.1,,Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),Specifically important for: Putrescine Dihydrochloride. part of putrescine catabolism via gamma-glutamyl-putrescine (SEED_correct),Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),putative glutamine amidotransferase,MSRLPLIGVTTCSRQIGLHAYHISGEKYSRAVATAAKGLPVLIPSLADLFPPSDILDALDGILLTGSPSNVEPFHYQGPASAPGTAHDPARDATTLPLIRAAVDAGIPVLGICRGFQEMNVAFGGSLHQKVHEVGTFIDHREDDTQAVDVQYGPAHAVHIQPGGVLAGLGLPQRIEVNSIHSQGIERLAPGLRAEAVAPDGLIEAVSVPGGKAFALGVQWHPEWEVSSNPHYLAIFQAFGDACRARAAQRDADASNNA catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4512,,TK06_RS22595,WP_063323898.1,tr|A0A159ZZY6|A0A159ZZY6_PSEFL,Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),"Specifically important for: Putrescine Dihydrochloride. KEGG annotates this as putrescine aminotransferase, probably based on similarity to spuC (PA0299) of P. aeruginosa, but it is believed that only the g-glutamyl- pathway operates in P. aeruginosa (X. Yao et al 2011, PMC3147493)",Omega-amino acid--pyruvate aminotransferase (EC 2.6.1.18),Omega-amino acid--pyruvate aminotransferase (EC 2.6.1.18),putrescine aminotransferase,MTRNNPQTREWQALSNDHHLAPFSDFKQLKEKGPRIITHAKGVYLWDSEGNKILDGMAGLWCVAVGYGREELADAASQQMRELPYYNLFFQTAHPPVLELSKAIADIAPEGMNHVFFTGSGSEGNDTMLRMVRHYWAIKGQPNKKVIISRKNGYHGSTVAGASLGGMTYMHEQGDLPIPGIVHIAQPYWFGEGGDMSPEEFGVWAANQLEEKILELGVDNVGAFIAEPIQGAGGVIVPPDSYWPRMKEILAKYDILFVADEVICGFGRTGEWFGTDHYELKPHMMTIAKGLTSGYIPMGGLIVRDDVVAVLNEGGDFNHGFTYSGHPVAAAVALENIRILRDEKIIERVHSETAPYLQKRLRELNDHPLVGEVRGVGLLGAIELVQDKATRKRYEGKGVGMICRQFCFDNGLIMRAVGDTMIIAPPLVISKAEIDELVAKARQCLDLTLSALQG transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4681,,TK06_RS23410,WP_063324024.1,tr|A0A160A3C6|A0A160A3C6_PSEFL,"ABC transporter for Carnitine, ATPase component","Specific phenotype on Carnitine Hydrochloride. This ABC transporter might also take up choline or glycine-betaine, in conjunction with another SBP, Pf6N2E2_4683",L-proline glycine betaine ABC transport system permease protein ProV (TC 3.A.1.12.1),L-proline glycine betaine ABC transport system permease protein ProV (TC 3.A.1.12.1),glycine betaine/proline transport system ATP-binding protein,MSIIRFEDVDVIFSNRPKAALDLLDKGLSRPEILQQTGLIVGVEKASLSIEKGEICVLMGLSGSGKSSLLRCINGLNTVSRGKLFVEHEGRQIDIASCSPAELKMMRTKRIAMVFQKFALMPWLTVRENISFGLEMQGRPEKERRKLVDEKLELVGLTQWRNKKPDELSGGMQQRVGLARALAMDADILLMDEPFSALDPLIRQGLQDELLELQRKLQKTIVFVSHDLDEALKLGTRIAIMKDGKIIQYSKPEEIVLNPADDYVRTFVAHTNPLNVLCGRSLMRTLDNCKRINGSVCLDPGGDSWLDLAEGNTIKGARQNGAALDLQNWVPGQAVEDLGRRPTLVDSNIGMRDALQIRYQTGNKLVLHDNQQVVGILGDSELYHALLGKNLG transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4682,,TK06_RS23415,WP_063324025.1,tr|A0A160A470|A0A160A470_PSEFL,"ABC transporter for Carnitine, permease component","Specific phenotypes on Carnitine Hydrochloride. This ABC transporter might also take up choline or glycine-betaine, in conjunction with another SBP, Pf6N2E2_4683",L-proline glycine betaine ABC transport system permease protein ProW (TC 3.A.1.12.1),L-proline glycine betaine ABC transport system permease protein ProW (TC 3.A.1.12.1),glycine betaine/proline transport system permease protein,MLIDQKIPLGQYIAGFVEWLTQNGANTFDAIALFLETMIHGVTFALTWFNPLALIGLIALLAHFIQRKWGLTIFVIASFLLILNLGYWQETMETLAQVLFATLVCVLIGVPLGIVAAHKPMFYTMMRPVLDLMQTVPTFVYLIPTLTLFGLGVVPGLISTVVFAIAAPIRLTYLGIRDVPDELMDAGKAFGCSRRQLLSRIELPHAMPSIAAGITQCIMLSLSMVVIAALVGADGLGKPVVNALNTADIALGFEAGLAIVLLAIMLDRICKQPDAKVGGDA catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4689,,TK06_RS23445,WP_063324030.1,tr|A0A160A4C1|A0A160A4C1_PSEFL,"L-carnitine 3-dehydrogenase, B subunit (EC 1.1.1.108)",Specifically important for: Carnitine Hydrochloride. Similar to CdhB = PA5385,FIG143042: Thioesterase-like protein,,acyl-CoA thioester hydrolase,MPTLTTYQTRILPEWVDYNGHLRDAFYLLIFSYATDALMDRLGMDSQNREASGHSLFTLELHLNYLHEVKLDAQVEVHTHIIGHDAKRLHLYHSLHLVGGDKELAGNEQMLLHVDLAGPRSAPFTEQTLDRLSALLDEQSDLPPPLYIGRIIALPLPR catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4690,,TK06_RS23450,WP_063324031.1,tr|A0A160A2B4|A0A160A2B4_PSEFL,"L-carnitine 3-dehydrogenase, A subunit (EC 1.1.1.108)",Specifically important for: Carnitine Hydrochloride. Similar to CdhA = PA5386,Carnitine 3-dehydrogenase (EC 1.1.1.108) @ 3-hydroxybutyryl-CoA dehydrogenase (EC 1.1.1.157) @ 3-hydroxyacyl-CoA dehydrogenase (EC 1.1.1.35),Carnitine 3-dehydrogenase (EC 1.1.1.108) @ 3-hydroxybutyryl-CoA dehydrogenase (EC 1.1.1.157) @ 3-hydroxyacyl-CoA dehydrogenase (EC 1.1.1.35),3-hydroxybutyryl-CoA dehydrogenase,MSFITDIKTFAALGSGVIGSGWISRALAHGLDVIAWDPAPGAEAALRKRVANAWGALEKQGLAPGASQDRLRFVATIEECVRDADFIQESAPERLELKLELHGQISAAAKPNALIGSSTSGLLPSEFYEGATHPERCVVGHPFNPVYLLPLVEVVGGKNTAPEAVQAAMQVYESLGMRPLHVRKEVPGFIADRLLEALWREALHLVNDGVATTGEIDDAIRFGAGLRWSFMGTFLTYTLAGGDAGMRHFMAQFGPALQLPWTYLPAPELTEKLIDDVVDGTSAQLGSHSISALERYRDDCLLAVLEAVKATKAKHGMAFSE catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4692,,TK06_RS23455,WP_063324032.1,tr|A0A160A201|A0A160A201_PSEFL,dehydrocarnitine cleavage enzyme (EC 2.3.1.-),Specifically important for: Carnitine Hydrochloride. Similar to CdhC = PA5387,FIG004891: hypothetical protein in carnitine cluster,FIG004891: hypothetical protein in carnitine cluster,,MNHDVIITCALTGAGDTTAKSPHVPVTPKQIADAAIEAAKAGATVVHCHVRDPQTGKFSRDVALYREVMERIREADVDIIVNLTAGMGGDLEIGAGEQPMEFGPNTDLVGPLTRLAHVEELLPEICTLDCGTLNFGDGDTIYVSTPAQLRAGAKRITELGVKAELEIFDTGHLWFAKQLIKEGLLDNPLFQLCLGIPWGAPADTTTMKAMVDNLPADAVWAGFGIGRMQMPMAAQAVLLGGNVRVGLEDNLWLDKGVLATNGQLVERAGEILSRLGARVLTPAEGRKKMGLTQRG transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_4693,,TK06_RS23460,WP_063324033.1,tr|A0A160A3D7|A0A160A3D7_PSEFL,"ABC transporter for Carnitine Hydrochloride, periplasmic substrate-binding component","Specific phenotype on Carnitine Hydrochloride. Similar to CaiX. There might also be another SBP, Pf6N2E2_4683 (similar to CbcX) that is expected to be specific for choline and glycine-betaine. However _4683 is important for carnitine utilization as well and it does not appear to be a polar effect, so it could be another subunit.",L-proline glycine betaine binding ABC transporter protein ProX (TC 3.A.1.12.1),L-proline glycine betaine binding ABC transporter protein ProX (TC 3.A.1.12.1),glycine betaine/proline transport system substrate-binding protein,MKRLISSCVLALCGTTFLNGTAMAAEPAACQNVRLGVVNWTDVIATSALTQVMLDGLGYKTKQTSASQQIIFAGIRDQRLDLFLGYWNPLMTQTITPFVEAKQVKVLEQPSLKDARATLAVPTYLADKGLKTFADIAKFEKELGGKIYGIEPGSGANTQIKAMIAKNQFGLGKFQLVESSEAGMLAAVDRAVRRKEAVVFFGWAPHPMNVNVQMTYLTGSDDALGPNEGMATVWTVTAPDYAQQCPNVGRLLSNLTFSAEDESRMMQPLLDHKDPLESARQWLKDHPQDKQRWLEGVTTFDGKPAADNLKLTSN catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_515,,TK06_RS02565,WP_063320678.1,tr|A0A159ZT15|A0A159ZT15_PSEFL,malonate-semialdehyde dehydrogenase (acetylating) (EC 1.2.1.18),"Specifically important for: m-Inositol. 3-oxopropionate or malonate semialdehyde is an intermediate in myo-inositol catabolism (SEED_correct, although the name and EC number are confusing)",Methylmalonate-semialdehyde dehydrogenase [inositol] (EC 1.2.1.27),Methylmalonate-semialdehyde dehydrogenase [inositol] (EC 1.2.1.27),methylmalonate-semialdehyde dehydrogenase,MSDAPVVGHYLNGHVQDHDSTRFSNVFNPATGAVQARVALAEPGTVDAAVASALAAFPAWSEQSSLRRSRVMFKFKELLDRHHDELAQIISREHGKVLSDAHGEVTRGIEIVEYACGAPNLLKTDFSDNIGGGIDNWNLRQPLGVCAGVTPFNFPVMVPLWMIPLALVAGNCFILKPSERDPSASLLMARLLTEAGLPDGVFNVVQGDKVAVDALLQHPDIEAISFVGSTPIAEYIHQQGTAHGKRVQALGGAKNHMIVMPDADLDQAADALIGAAYGSAGERCMAISIAVAVGDVGDELIAKLLPRIDQLKIGNGQQPGTDMGPLVTAEHKAKVEGFIDAGVAEGARLIVDGRSFKVPGAEQGFFVGATLFDQVTAEMSIYQQEIFGPVLGIVRVPDFATAVALINAHEFGNGVSCFTRDGGIARAFARSIKVGMVGINVPIPVPMAWHSFGGWKRSLFGDHHAYGEEGLRFYSRYKSVMQRWPDSIAKGPEFSMPTAQ transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5402,,TK06_RS26960,WP_003205302.1,tr|A0A160A3S6|A0A160A3S6_PSEFL,"ABC transporter for D-Alanine, periplasmic substrate-binding component",Very important for D-alanine utilization. This transporter may also uptake L-histidine.,Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),general L-amino acid transport system substrate-binding protein,MKLLKSTLAVMTAAAVLGVSGFAQAGATLDAVQKKGFVQCGVSDGLPGFSVPDSTGKIVGIDADFCRAVAAAVFGDATKVKFSQLNAKERFTALQSGEIDMLSRNSTMTSSRDAGMGLKFPGFITYYDGIGFLANNKLGVKSAKELDGATICIQAGTTTELNVSDYFRANGLKYTPITFDTSDESAKSLESGRCDVLTSDKSQLFAQRSKLASPKDYVVLPETISKEPLGPVVRNGDDEWLAIVRWTGYALLNAEEAGVTSKNVEAEAKSTKNPDVARMLGADGEYGKDLKLPKDWVVQIVKQVGNYGEMFERNLGKGTPLEIDRGLNALWNAGGIQYAPPVR transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5403,,TK06_RS26965,WP_063324518.1,tr|A0A160A3G2|A0A160A3G2_PSEFL,"ABC transporter for D-Alanine, permease component 2",Very important for D-alanine utilization. This transporter may also uptake L-histidine.,Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),general L-amino acid transport system permease protein,VRAWVFQVVTVVAVIALGWFLFDNTQTNLQHRGITSGFGFLERSAGFGIAQHLIDYTEADSYARVFLIGLLNTLLVTFIGVILATILGFIIGVARLSQNWIISKLATVYVEVFRNIPPLLQILFWYFAVFLSMPGPRAAHNFGDTFFVSSRGLNMPAALVAEGFWPFVISVVLAIVAIVLMTRWANKRFEATGEPFHKFWVGLALFLVIPALSALLFGAPVHWEMPELKGFNFVGGWVLIPELLALTLALTVYTAAFIAEIVRSGIKSVSHGQTEAARSLGLRNGPTLRKVIIPQALRVIIPPLTSQYLNLAKNSSLAAGIGYPEMVSLFAGTVLNQTGQAIEVIAITMSVYLAISISISLLMNWYNKRIALIER transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5404,,TK06_RS26970,WP_063324519.1,tr|A0A160A4M7|A0A160A4M7_PSEFL,"ABC transporter for D-Alanine, permease component 1","Specific phenotypes on D-Alanine. Also important for histidine utilization, so this may also take up L-histidine",Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),general L-amino acid transport system permease protein,MTTHTFKPDMPPPGSSIGVVAWMRANMFSSWINTLLTLFAFYLIYLIVPPLVQWAILDANWVGTTRADCTKEGACWVFIQQRFGQFMYGYYPADLRWRVDLTVWLAVIGVAPLFISRFPRKAIYGLSFLVLYPISAWCLLHGGVFGLDAVATSQWGGLMLTLVIATVGIVGALPLGIVLALGRRSNMPAIRVVCVTFIEFWRGVPLITVLFMSSVMLPLFLPEGMNFDKLLRALIGVILFQSAYIAEVVRGGLQAIPKGQYEAAAAMGLGYWRSMGLVILPQALKLVIPGIVNTFIALFKDTSLVIIIGLFDLLNSVKQAAADPKWLGMATEGYVFAALVFWIFCFGMSRYSMHLERKLDTGHKR transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5405,,TK06_RS26975,WP_003178331.1,tr|A0A0D9AS17|A0A0D9AS17_PSEFL,"ABC transporter for D-Alanine, ATPase component",Very important for D-alanine utilization. This transporter may also uptake L-histidine.,"ABC-type polar amino acid transport system, ATPase component","ABC-type polar amino acid transport system, ATPase component",general L-amino acid transport system ATP-binding protein,MSEAIKQPVSPEGIIQMQGVNKWYGQFHVLKDINLNVKQGERIVLCGPSGSGKSTTIRCLNRLEEHQQGRIVVDGVELTNDLKQIEAIRREVGMVFQHFNLFPHLTILQNCTLAPMWVRKMPKRKAEEIAMHYLERVRIPEQAHKYPGQLSGGQQQRVAIARALCMKPKIMLFDEPTSALDPEMVKEVLDTMIGLAEDGMTMLCVTHEMGFARTVANRVIFMDKGEIVEQAAPNDFFDNPQNDRTKLFLSQILH transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5660,,TK06_RS28180,WP_063324706.1,tr|A0A160A465|A0A160A465_PSEFL,"L-Arginine ABC transporter, periplasmic substrate-binding component",Specific phenotype on L-Arginine. (SEED_correct),"Arginine/ornithine ABC transporter, periplasmic arginine/ornithine binding protein","Arginine/ornithine ABC transporter, periplasmic arginine/ornithine binding protein",,MKKLVLLGALALSVLSLPTFADEKPLKIGIEAAYPPFASKAPDGSIVGFDYDIGNALCEEMKVKCVWVEQEFDGLIPALKVRKIDAILSSMSITEDRKKSVDFTNKYYNTPARLVMKAGTQVSDNLAELKGKNIGVQRGSIHERFAREVLAPLGAEIKPYGSQNEIYLDVSAGRLDGTVADATLLDDGFLKTDAGKGFAFVGPAFTDEKYFGDGIGIAVRKGDALKDKINGAITAIRENGKYKQIQDKYFAFDIYGK transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5661,,TK06_RS28185,WP_003204800.1,tr|A0A160A3X7|A0A160A3X7_PSEFL,"L-Arginine ABC transporter, permease protein AotQ",Specific phenotypes on L-Arginine; L-Arginine. no data for ornithine (SEED_correct),"Arginine/ornithine ABC transporter, permease protein AotQ","Arginine/ornithine ABC transporter, permease protein AotQ",,MLKGYGAVILDGAWLTLELALSSMALAIVLGLIGVALRLSPVRWLAWLGDLYATVIRGIPDLVLILLIFYGGQDLLNRVAPMLGYDDYIDLNPLAAGIGTLGFIFGAYLSETFRGAFMAIPKGQAEAGMAYGMNGFQVFFRVLVPQMIRLAIPGFTNNWLVLTKATALISVVGLQDMMFKAKQAADATREPFTFFLAVAAMYLVITSVSLLALRHLEKRYSVGVRAADL transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5662,,TK06_RS28190,WP_063324707.1,tr|A0A160A563|A0A160A563_PSEFL,"L-Arginine ABC transporter, permease protein AotM",Specific phenotypes on L-Arginine; L-Arginine. (SEED_correct),"Arginine/ornithine ABC transporter, permease protein AotM","Arginine/ornithine ABC transporter, permease protein AotM",,MIFDYNVIYEALPLYFSGLLTTLKLLALSLFFGLLAALPLGLMRVSKQPIVNMTAWLYTYVIRGTPMLVQLFLIYYGLAQFAIVRESFLWPWLSSATFCACLAFAINTSAYTAEIIAGSLRATSNGEIEAAKAMGMSRYKLYRRILLPSALRRALPQYSNEVIMMLQTTSLASIVTLIDITGAARTVNAQYYLPFEAYITAGVFYLCLTFILVRLFKLAERRWLGYLAPRKH transporters,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5663,,TK06_RS28195,WP_063324708.1,tr|A0A160A5V6|A0A160A5V6_PSEFL,"L-Arginine ABC transporter, ATPase component",Very important for utilizing L-arginine. (SEED_correct),"Arginine/ornithine ABC transporter, ATP-binding protein AotP","Arginine/ornithine ABC transporter, ATP-binding protein AotP",,MYKLEVQDLHKRYGSHEVLKGVSLKAAAGDVISIIGSSGSGKSTFLRCINLLEQPHAGKILLNNEELKLVAGKDGALKAADPKQLQRMRSRLSMVFQHFNLWSHMTALENIMEAPVHVLGVTKAQAREKAEHYLNKVGVAHRKDAYPGHMSGGEQQRVAIARALAMEPEVMLFDEPTSALDPELVGDVLKVMQGLALEGRTMVVVTHEMGFAREVSNQLVFLHKGIVEESGNPREVLVNPQSERLQQFLSGSLK catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_5967,,TK06_RS29720,WP_063324926.1,tr|A0A165ZSW6|A0A165ZSW6_PSEFL,L-arabinose 1-dehydrogenase / D-galactose 1-dehydrogenase (EC 1.1.1.46; EC 1.1.1.48),"Important for utilization of L-arabinose and D-galactose in several Pseudomonas. Both of these sugars are catabolized via a 1-dehydrogenase followed by lactonase and dehydratase reactions. This is the only dehydrogenase is specifically important for either of these carbon sources in several Pseudomonas, except for an alpha-ketoglutarate semialdehyde dehydrogenase that is expected to be the last dedicated step in L-arabinose catabolism (i.e., Pf6N2E2_612). L-arabinose and D-galactose are chemically similar and some dehydrogenases are already known to act on both substrates. This gene might also be involved in D-gluconate utilization.",3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),3-oxoacyl-[acyl-carrier protein] reductase (EC 1.1.1.100),,MAEPLSLPPVPEPPKGERLKNKVVLLTGAAQGIGEAIVAAFASQQARLVISDIQAEKVETVAAHWRERGADVHALKADVSNQQDLHAMARHAVERHGRIDVLVNCAGVNVFRDPLEMTEEDWRRCFAIDLDGAWYGCKAVLPQMIEQGVGSIINIASTHSSHIIPGCFPYPVAKHGLLGLTRALGIEYAPKGVRVNAIAPGYIETQLNVDYWNGFADPYAERQRALDLHPPRRIGQPIEVAMTAVFLASDEAPFINASCITIDGGRSVMYHD catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_611,,TK06_RS03025,WP_063320756.1,tr|A0A159ZTV2|A0A159ZTV2_PSEFL,2-dehydro-3-deoxy-L-arabinonate dehydratase (EC 4.2.1.43),"# Specifically important in carbon source L-Arabinose. Similar to PA2216 from Pseudomonas aeruginosa (see PMC:PMC4038344) and to gguC or araD1 (Atu2345) from Agrobacterium tumefaciens (see PMC: PMC3232879), which also have this activity. In Agrobacterium, this reaction is proposed to be a step in L-arabinose oxidation. (Note that 2-keto-3-deoxy-L-lyxonate and 2-keto-3-deoxy-L-arabinonate are the same compound.)",SUGAR TRANSPORTER,SUGAR TRANSPORTER,,MRLVQFELPNGERRVGVVDGDQVREVQGAASVRDLALAAIEAGVKLEEQVNSLGLGAGHDYARLLSELRILPPLDHPDPAHLLVSGTGLTHLGSASARDKMHQQAGDESAMTDTMRIFKWGVEGGKPQAGQAGVQPEWFYKGDGSIVVRPGHPFPLPPFAEDAGEEPEISGLYVIGHDGKPYRLGFAVGNEFSDHVMERKNYLYLAHSKLRSCSFGPELRVGELPQHLSGTSRILRNGEVLWQNEFLSGEANMCHSLENLEFHHFKYSQFLRPGDVHVHFFGTATLSFADGIRTQPGDVFEISQAEFGAPLVNGIAPVDAVFNPGTIGTL catabolism,Pseudomonas fluorescens FW300-N2E2,pseudo6_N2E2,Pf6N2E2_80,,TK06_RS00420,WP_063320323.1,tr|A0A165YII0|A0A165YII0_PSEFL,gamma-glutamylputrescine oxidase (EC 1.4.3.-),Specifically important for: Putrescine Dihydrochloride. This is part of the gamma-glutamyl-putrescine pathway. (KEGG_correct),Nucleoside-diphosphate-sugar epimerases,Nucleoside-diphosphate-sugar epimerases,gamma-glutamylputrescine oxidase,VIGAGYTGLSSALFLLENGFKVTVLEAAKVGFGASGRNGGQIVNSYSRDIDVIERSVGPQQAQLLGNMAFEGGRIIRERVAKYQIQCDLKDGGVFAALTAKQMGHLESQKRLWERFGHTQLELLDQRRIREVVACEEYVGGMLDMSGGHIHPLNLALGEAAAVESLGGVIYEQSPAVRIERGASPVVHTPQGKVRAKFIIVAGNAYLGNLVPELAAKSMPCGTQVIATEPLGDELAHSLLPQDYCVEDCNYLLDYYRLTGDKRLIFGGGVVYGARDPANIEAIIRPKMLKAFPQLKDVKIDYAWTGNFLLTLSRLPQVGRLGDNIYYSQGCSGHGVTYTHLAGKVLAEALRGQAERFDAFADLPHYPFPGGQLLRTPFAAMGAWYYGLRDKLGF catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF1044,Psest_1077,PSEST_RS05235,WP_003280093.1,tr|L0GFY2|L0GFY2_PSEST,"acetyl-CoA:acetoacetate CoA transferase, B subunit (EC 2.8.3.8)","Specifically important for: L-tyrosine disodium salt; L-Leucine. This role makes sense because of its involvement in acetoacetate catabolism (from tyrosine or leucine). It had been annotated as succinyl-CoA:3-ketoacid-coenzyme A transferase (a broader range of substrates and with succinyl-CoA not acetyl-CoA as donor). Similar to E. coli atoA (52% identical), which has this activity.","3-oxoacid CoA-transferase, B subunit",Succinyl-CoA:3-ketoacid-coenzyme A transferase subunit B (EC 2.8.3.5),3-oxoacid CoA-transferase subunit B,MAWTREQMAQRAAQELQDGFYVNLGIGLPTLVANYIPEGMDVWLQSENGLLGIGPFPTEEEIDPDLINAGKQTVTALPGSSFFDNAQSFAMIRGGHINLAILGAMQVSEKGDLANWMIPGKMVKGMGGAMDLVAGVKRVVVLMEHTAKGGAHKILPACDLPLTGLGVVDRIITDLGVLDVTEQGLKLVELAEGVSFDELQEATGSPIQR catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF1045,Psest_1078,PSEST_RS05240,WP_003294550.1,tr|L0GK00|L0GK00_PSEST,"acetyl-CoA:acetoacetate CoA transferase, A subunit (EC 2.8.3.8)","Specifically important for: L-Leucine. Important on tyrosine as well. This role makes sense because of its involvement in acetoacetate catabolism (from tyrosine or leucine). It had been annotated as succinyl-CoA:3-ketoacid-coenzyme A transferase (a broader range of substrates and with succinyl-CoA not acetyl-CoA as donor). Similar to E. coli atoD (46% identical), which has the same activity.","3-oxoacid CoA-transferase, A subunit",Succinyl-CoA:3-ketoacid-coenzyme A transferase subunit A (EC 2.8.3.5),3-oxoacid CoA-transferase subunit A,MNKIYPSAAHALEGLVEDGMTIAVGGFGLCGIPEQLIAALRDSGKKDLTAISNNAGVDGFGLGLLLETRQISKMVSSYVGENKEFERQYLAGELALEFTPQGTLAEKLRAGGAGIPAFYTKTGYGTLVAEGKETRQFNGEWYVMEESLTADLALVKAWKADKAGNLLFRKTARNFNPLAAMAGEVCVVEVEEIVETGELDPDQIHLPGIYVHRIVHNPNPEKRIEKRTVRS catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF1145,Psest_1178,PSEST_RS05740,WP_015276062.1,tr|L0GIY0|L0GIY0_PSEST,D-glycerate 2-kinase (EC 2.7.1.-),"Specifically important for: Glycolic Acid. Either glycerate kinase or hydroxypyruvate reductase could be involved in glycolate utilization (via glyoxylate and tartronate-semialdehyde, which can be converted to glycerate, perhaps via hydroxypyruvate). However this protein is similar to TM1585 (also PDB:2b8nA or GCK_THEMA), which is a glycerate kinase. We were not able to find a rationale for KEGG's annotation as hydroxypyruvate reductase. (SEED_correct)",Putative glycerate kinase,D-glycerate 2-kinase (EC 2.7.1.-),hydroxypyruvate reductase,MTLDPQALLRQLFDSAIEAAHPRHVLADHLPEDRSGRAIVIGAGKAAAAMAEAIEKVWEGELSGLVVTRYEHHADCKRIEVVEAAHPVPDDAGERVARRVLELVSNLEESDRVIFLLSGGGSSLLALPAEGISLADKQAINKALLRSGAHIGEMNCVRKHLSAIKGGRLAKACWPASVYTYAISDVPGDEATVIASGPTVADPTTSEQALEILERYHIEVPANVRAWLEDPRSETLKPGDPMLSRSHFRLIATPQQSLDAAAEVARAAGITPLILGDLEGEAREVAKVHAGIARQVVLHGQPIAAPCVILSGGETTVTVRGNGRGGRNAEFLLALTENLQGLPNVYALAGDTDGIDGSEDNAGALMMPDSYARAETLGLRAADALANNDGYGYFAALDDLIVTGPTRTNVNDFRAILILPPSA transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF2080,Psest_2123,PSEST_RS10360,WP_015276942.1,tr|L0GLK1|L0GLK1_PSEST,fusion of gluconokinase and the small permease component of the D-gluconate TRAP transporter,"This protein has pleiotropic phenotypes which are not explained, but it is most important for fitness with D-gluconate as the carbon source, consistent with its putative roles as part of the tripartite gluconate transport system and as gluconate kinase. Also, the N-terminal part is 49% identical to PP3416 or gnuK from P. putida, which is the catabolic gluconate kinase (PMC1951859). The SEED and KEGG annotations ignore the dctQ-like C-terminal portion","carbohydrate kinase, thermoresistant glucokinase family",Gluconokinase (EC 2.7.1.12),gluconokinase,MPSVHTSKASALPVLVVMGVSGSGKTETSHAVADALGLPHIEADNFHPAENVARMRAGTPLSDADRMEWLHALIAEMQRTLAAGSGFVLACSALKRSYRELLRSAVPELRFAHLAIDYETAVQRVGGRAGHFMPISLVDSQFATLESPEGEPGVLTVDASQPREGVLRQIVEWMQGSGLDELIETRVDLSSRPFDSATTAPPLTNEPIYSGRVAQHFDRLTDWLMAALMAFMVIVVFSSVVLRYAFGTGWTGAEELSRLAFVWLVFVGVASSMRRGELMSFSMLRDRFPRLFRRVVDSLSWLLVAAASCLAAWGGWNQMQFGWTINSPVVGYPLGLAMLPVAASMVALAVLALLQLVNVWRRDQPSATAAANVTAD transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF2081,Psest_2124,PSEST_RS10365,WP_014820165.1,tr|L0GIU4|L0GIU4_PSEST,"D-gluconate TRAP transporter, large permease component",specific phenotype on D-gluconate and no other transporter is apparent in the fitness data.,"TRAP transporter, DctM subunit","TRAP-type C4-dicarboxylate transport system, large permease component",,MTVVVFLSSLLGFMTLGMPIAFALLLTGSVLMWYLDFWDVQLLAQNLQAGADSFPLLAVPFFILAGELMNAGGISRRIIAMAQAYFGHKRGGLGYVAIAASVLLASMSGSALADTAALATLLLPMMRERGYPLSSSSGLVAAGGIIAPIIPPSMPFVIYGVVTGTSISQLFLAGMVPGLIMGMGLIVAWTLIARRIDEPKQEKASAAERRRVLVDGAAALMLPVIIVGGLRGGLFTPTEAAVVAAVYALAVSTLLYRELNWAGLVEVLTRASRTTASVMFLCAAATVSAYMITLAQLPDEIAAMLGPLAQDPKLLMVAIMLLMIAVGMVLDLTPTILILGPVLAPIAIKAGIDPVYFGVMFVLIGSIGLITPPVGTVLNVVGGIGRLRMETLVRGVMPFFLIYLVIVGLLIAVPSIITVPLAWLR transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF2082,Psest_2125,PSEST_RS10370,WP_015276943.1,tr|L0GMV7|L0GMV7_PSEST,"D-gluconate TRAP transporter, periplasmic component",specific phenotype on D-gluconate and no other transporter is apparent in the fitness data,"tripartite ATP-independent periplasmic transporter solute receptor, DctP family","TRAP-type transport system, periplasmic component, predicted N-acetylneuraminate-binding protein",,MKRLLISTLAAALLGSTLSLGYAQAADDIRPRMIRFGYGLNEDSNQGRAAKLLAEEVAKASGGKLKVRTFASASLGSDDQMQNALIGGAQEMMVGSTATLVGISKEMAVWDTPFLFTDPRQADQVLDGPVGRQVMDKLEEKGLVGLVYWENGFRNVTNSARPIEKLEDFNGVKLRVMPNPVFIDTFKRMGANAVPLPFSELFTALETKAVDGQENPFNTILSSKFYEVQKYLSVTNHVYSPWIVTVSKRWWDGLSATEQGILMEAAEKARDAEREDTRREASQALAALKERGMQINEVSPDEIQRMREKAQPAIQTVIDAVGQELFDQVQAEVEKAAP catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF2392,Psest_2440,PSEST_RS11895,WP_015277224.1,tr|L0GMF3|L0GMF3_PSEST,isobutyrl-CoA dehydrogenase (EC 1.3.8.1),"Important for valine utilization as N source, and isobutyrl-CoA is an intermediate in valine catabolism. SEED annotated it as butyryl-CoA dehydrogenase, which is also expected to perform this reaction",Acyl-CoA dehydrogenases,Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MHDLELSEDQRMIRDMARDFARREIAPHAQAWEKAGWIDDTLVAQMGELGLLGMVVPEEWGGSYIDYVAYALAVEEISAGDGATGALMSIHNSVGCGPVLNYGSQAQKDEWLTELASGRAIGCFALTEPQAGSEAHNLRTRAELVDGHWVLNGSKQFCSNAKRSKLAIVFAVTDPELGKKGLSAFLVPTDTPGFAVERSEHKMGIRASDTCGVSLSDCRIPEANLLGERGKGLAIALSNLEGGRIGIGAQALGIARAAFEAALLYARERVQFGKPIAEHQSIANMLADMQTQLNAARLLILHAARLKSAGLPCLSEASQAKLFASEMAEKVCSQAVQIHGGYGYLEDYPVERYYRDARITQIYEGSSEIQRLLIARELANYAL catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF2397,Psest_2445,PSEST_RS11915,WP_015277229.1,tr|L0GMG4|L0GMG4_PSEST,2-methylbutanoyl-CoA dehydrogenase (EC 1.3.8.1),"Specifically important for: L-Isoleucine. SEED has it as butyryl-CoA dehydrogenase, which is also expected to perform this reaction. 2-methylbutanoyl-CoA is an intermediate in isoleucine catabolism",Acyl-CoA dehydrogenases,Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MLPNEDQNAIAEMARQFAQERLKPFAEQWSREHRYPAEAIGEMAALGFFGMLVPEQWGGSDTGYLAYAMALEEIAAGDGACSTIMSVHNSVGCVPILRFGNEQQKSDFLTPLARGEQIGAFALTEPQAGSDASSLRTRARRDGDHYVLNGAKQFITSGKHAGTVIVFAVTDPDAGKGGISAFIVPTDSPGYQVVRVEDKLGQHASDTCQIAFEDLRVPVANRLGEEGEGYRIALANLEGGRIGIAAQAVGMARAAFEAARDYARDRETFGKPIIEHQAVAFRLADMATQIAVARQMVHHAAALREVGRPALVEASMAKLFASEMAEKVCSAAIQTLGGYGYLADFPVERIYRDVRVCQIYEGTSDIQRLVISRNLGGPV catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF3447,Psest_3512,PSEST_RS17065,WP_015278222.1,tr|L0GPV2|L0GPV2_PSEST,fumarylacetoacetate (FAA) hydrolase (EC 3.7.1.2),"Specifically important for: L-tyrosine disodium salt; casamino acids. The annotation of psa:PST_0871 has been updated to fumarylacetoacetate (FAA) hydrolase [EC:3.7.1.2], which is a step in tyrosine catabolism (KEGG_correct)","2-keto-4-pentenoate hydratase/2-oxohepta-3-ene-1,7-dioic acid hydratase (catechol pathway)",Fumarylacetoacetate hydrolase family protein,,MKLATLNQGRDGVLVVVSRDLAQAVKVPQIAATLQAALDDWNYCKPKLEAVYQRLNDGLEEGAFAFDQTACHSPLPRAYHWADGSAYVNHVELVRKARGAEMPESFWHDPLMYQGGADAFIPPHSPIRLADEAWGIDLEGELAVITDDVPMGATPAEAASHIQLLMLVNDVSLRNLIPGELAKGFGFYQSKPSSSFSPVAVTPDELGETWRDGKVHRPLVSHINGELFGQPDAGTDMTFNFPTLVAHAARTRPLGAGTIIGSGTVSNYDRSAGSSCLAEKRMLEVVEHGEAKTPFLKFGDRVRIEMFDAAGQSIFGAIDQQVERYGH catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF3452,Psest_3517,PSEST_RS17090,WP_015278227.1,tr|L0GPV5|L0GPV5_PSEST,"branched-chain ketoacid ferredoxin reductase (EC 1.2.7.7) active on 4-methyl-2-oxopentanoate, (S)-3-methyl-2-oxopentanoate, or 3-methyl-2-oxobutanoate",Specifically important for: L-Leucine; L-Isoleucine. This oxidizes and decarboxylates the alpha-keto acid that is produced by a branched chain amino acid dehydrogenase (4-methyl-2-oxopentanoate or (S)-3-methyl-2-oxopentanoate). This is also improtant for utilization of valine as a nitrogen source implying a third substrate (3-methyl-2-oxobutanoate).,"Indolepyruvate ferredoxin oxidoreductase, alpha and beta subunits","Indolepyruvate ferredoxin oxidoreductase, alpha and beta subunits",indolepyruvate ferredoxin oxidoreductase,MSLAEIRLDDKYRLATGHLYLTGTQALTRLPMLQHQRDQARGLNTGGFISGYRGSPLGGLDKSLWEARDYLKQHAIHFQPGVNEELAATAVWGSQQTNLFPGAKYDGVFAMWYGKGPGVDRAGDVFKHANAAGVSPQGGVLLLAGDDHGCKSSTLPHQSEHAFIAASIPVLNPANVQEILDYGIIGWELSRYSGCWVALKTIAENVDSSAVVEVDPLRVQTRIPEDFELPEDGVHIRWPDPPLAQEKRLNLYKIYAARAFARANNLNRVMLDSPNPRLGIITTGKSYLDVRQALDDLGLDEALCASVGLRVLKVGMSWPLEPVSVHEFAQGLDEILVVEEKRSIIEDQLTGQLYNWPVSKRPRVVGEFDEQGNSLLPNLSELTPAMIARVIAKRLAPIYTSDSIQARLAFLAAKEKALAARSYSTVRTPHYCSGCPHNSSTKVPEGSRASAGIGCHYMVQWMDRRTETFTQMGGEGVNWIGQAPFTDTPHMFQNLGDGTYFHSGSLAVRAAVAAGVNVTYKILYNDAVAMTGGQPIDGELRVDQLSRQIFHEGVKRIALVSDEPDKYPSRDTFAPITSFHHRRELDAVQRELREFKGVSVIIYDQTCATEKRRRRKRGKMEDPAKRAFINPAVCEGCGDCGEKSNCLAVLPLETELGRKREIDQNACNKDFSCVEGFCPSFVTVHGGGLRKPEAVAGGIEAATLPEPQHPTLDRPWNVLIPGVGGSGVTTLGALLGMAAHLEGKGCTVLDQAGLAQKFGPVTTHVRIAAKQSDIYAVRIAAGEADLLLGCDLIVAAGDESLTRLNEQISNAVVNSHESATAEFTRNPDAQVPGAAMRQAISDAVGADKTHFVDATRLATRLLGDSIATNLFLLGFAYQQGLLPISAEAIEKAIELNGVSAKLNLQAFRWGRRAVLEREAVEQLARPVDMVEPICKTLEEIVDWRVDFLTRYQSAGLARRYRQLVERVRDADSADDLALSKAVARYYFKLLAYKDEYEVARLYSEPEFRQQLEAQFEGDYKLQFHLAPAWLAKRDPVTGEPRKRELGPWVLNLFGVLAKFRFLRGTPLDPFGYGHDRRVERQLISEYEKTVDELLAQLKPTNYRTAVAIAALPEQIRGYGPVKERSIAKARQQEKLLREQLAKGDEVQSVRLFQPAA catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF3770,Psest_3839,PSEST_RS18675,WP_015278495.1,tr|L0GSM3|L0GSM3_PSEST,"D-lactate oxidase and glycolate oxidase, iron-sulfur subunit (EC 1.1.3.15)",Specifically important for: Glycolic Acid; Sodium D-Lactate. This is the first step in both D-lactate and glycolate oxidation. KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase),Fe-S oxidoreductase,"Glycolate dehydrogenase (EC 1.1.99.14), iron-sulfur subunit GlcF",glycolate oxidase iron-sulfur subunit,MQTNLSEAAKKLPRAEEAESILRSCVHCGFCNATCPTYQLLGDELDGPRGRIYLMKQMFEGGEVTESTQLHLDRCLTCRNCETTCPSGVKYHNLLDIGRDFIEQQVQRPLGERVVRGGLRTVIPRPGLFKALLGAGNALKPLMPASLKDHLPREIRPAKPRPQVMHSRRVLILEGCVQPSLSPSTNAAAARVLDRLGISVSPAREAGCCGAVDYHLNAQDAGLDRARRNIDAWWPAIEAGAEAIVQTASGCGAFVKEYGHLLKDDPAYAAKAARVSELAKDLVEVLRSAELEKLNVRADKRMAFHCPCTLQHAQKLGGAVEDVLTRLGYQLTAVPDAHLCCGSAGSYSITQPEISHQLRDNKLNALESGKPEVIVTANIGCQTHLDGAGRTPVKHWIEVVEESMQ catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF3771,Psest_3840,PSEST_RS18680,WP_015278496.1,tr|L0GSF4|L0GSF4_PSEST,"D-lactate oxidase and glycolate oxidase, FAD binding subunit (EC 1.1.3.15)",Specifically important for: Sodium D-Lactate; Glycolic Acid. This is the first step in both D-lactate and glycolate oxidation. KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase),FAD/FMN-containing dehydrogenases,"Glycolate dehydrogenase (EC 1.1.99.14), FAD-binding subunit GlcE",glycolate oxidase FAD binding subunit,MSVIANDASAQLLDQVNQALAANTPLRIQGSGSKSFLGLQADGVLLDTREHRGIVSYDPTELVVTVRAGTPLTELETALDEAGQMLPCEPPHFGEGATVGGMIAAGLSGPRRPWSGSVRDFVLGSRVITGQGKHLRFGGEVMKNVAGYDLSRLMAGSFGCLGVLTEVSLKVLPKPRLCTSLRLEIDLERALLKLAEWGQQPIPISAASHDGQALHLRLEGGEGSVGAARERIGGEDLDPGYWNDLREQRLAFFADPRPLWRLSLPNNTPALGLPGDQLVDWAGAQRWLKSDADAVTIRGIAIEVGGHATCFTAGATTNPFQPLAAPLLRYHRQLKAALDPQGIFNPGRMYSEV catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF3772,Psest_3841,PSEST_RS18685,WP_015278497.1,tr|L0GQM8|L0GQM8_PSEST,"D-lactate oxidase and glycolate oxidase, FAD-linked subunit (EC 1.1.3.15)",Specifically important for: Sodium D-Lactate; Glycolic Acid. This is the first step in both D-lactate and glycolate oxidation. KEGG has the correct EC # but describes it as glycolate oxidase; SEED has it as 1.1.99.14 (glycolate dehydrogenase),"glycolate oxidase, subunit GlcD","Glycolate dehydrogenase (EC 1.1.99.14), subunit GlcD",glycolate oxidase,MNILYDERVDGALPKVDKAALLAELQAQLPDLDILHRSEDLKPYECDGLSAYRTTPLLVVLPERIEQVETLLKLCHQRGVPVVARGAGTGLSGGALPLEQGILLVMARFNKILEVDPAGRFARVQPGVRNLAISQAAAPYELYYAPDPSSQIACSIGGNVAENAGGVHCLKYGLTVHNLLKVDILTVEGERMTLGSDALDSPGFDLLALFTGSEGMLGIVTEVTVKLLPKPQVAKVLLAAFDSVEKAGRAVGDIIAAGIIPGGLEMMDNLSIRAAEDFIHAGYPVDAEAILLCELDGVEADVHDDCARVSEVLKLAGATEVRLAKDEAERVRFWAGRKNAFPAVGRISPDYYCMDGTIPRRELPGVLKGISDLSEQFGLRVANVFHAGDGNMHPLILFDANQPGELERAEDLGGKILELCVKVGGSITGEHGVGREKINQMCSQFNADELTLFHAVKAAFDPSGLLNPGKNIPTLHRCAEFGRMHIHNGQLPFPELERF transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF4058,Psest_4131,PSEST_RS20130,WP_015278763.1,tr|L0GRH7|L0GRH7_PSEST,alpha-ketoglutarate sensor protein (mifS),"Specific phenotype on a-ketoglutarate and 56% identical to PA5512 (mifS), which regulates a-ketoglutarate transport in P. aeruginosa (PMC4482717). The transporter in this organism is Psest_0084:0085.",Signal transduction histidine kinase regulating C4-dicarboxylate transport system,C4-dicarboxylate transport sensor protein,"two-component system, NtrC family, C4-dicarboxylate transport sensor histidine kinase DctB",MPALPNRLRVTLIVALILVGLLLSMHWAGRLAEQRAWAERSQESQGQLELYAQAIHTQVERFRSVPALLALDSDIQGLLADPGNRALRRELNQRLEQQNHAAGSSVLYLLDRNGETIAASNWRDWSSFVGNNYAFRPYFRDAVAHDSGRYFAVGVTTGIPGYFLSSSVKSATGEVLGVLVVKLELEDMQRDWVGQPGILLIADSLDIVILTNRPAWRFRYLRPLSDEVRSRLIDVRRYAEQTLQPLQSSRVQQLSESSERRLVDGPDGRREYLWQRLALPEEDWTLHLLHDPQMVVASVRSYRLAAAGVWMTLAFLLLYLAQRRKTRRVEMRSRSELEHLVHERTRELHTAQDELVHAARMAALGQMSAALAHEINQPLTALRMQLASLRLLLDSGRDGEVREGLGHVEGLLERMAALTGHLKTFARKSPAGLRQRLSLAEVLEQALQLLSPRIRSEQVEVFRQVPAEAMVSGDAIRLEQVLINLLHNALDAMAGRPQRRLRILCQLNGDSWQLSVGDNGGGIASEHLDQVFEPFFTTKPVGQGLGLGLAVSYGIVRDMGGTLEVSNDAHGAVFTLTLLAVEGHAAE transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF4059,Psest_4132,PSEST_RS20135,WP_015278764.1,tr|L0GS32|L0GS32_PSEST,alpha-ketoglutarate DNA-binding response regulator (mifR),"Specific phenotype on a-ketoglutarate and 75% identical to PA5511 (mifR), which regulates a-ketoglutarate transport in P. aeruginosa (PMC4482717). The transporter in this organism is Psest_0084:0085.","Response regulator containing CheY-like receiver, AAA-type ATPase, and DNA-binding domains",Nitrogen regulation protein NtrX,"two-component system, NtrC family, C4-dicarboxylate transport response regulator DctD",MSGQVIFIDDEAAIRQAVQQWLELSGFQVRTFSRAREALTALDRDFPGVLISDVRMPDLDGLGLLEQLVALDADLPVIMVTGHGDVPMAVQALRQGAYDFIEKPFTPERLLDSVRRAMDKRRLVCENRQLREQFARKGRIESQLLGVSRAMDNLRRQVLELAGTDVNVLIRGETGSGKEQVARCLHDFSPRAGGPFVALNCAAIPETIFESELFGHESGAFTGAQGKRIGRIEHAAGGTLFLDEIESMPLAQQVKLLRVLQEKTLERLGSNRSIEVDLRVISAAKPDLLEEVRGGRFREDLLYRLNVAELHIPPLRERREDIPLLFEHFASQAAQRHGRAAPPVTPGELTQLLAHDWPGNVRELINAAERHALGLSAPAPASSGGQSLAEQMEAFEAQCLHNALQQCKGNITEVMTQLQLPRRTLNEKMQRHGLSRSDYLPAGSADS catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF418,Psest_0419,PSEST_RS02075,WP_015275423.1,tr|L0GI16|L0GI16_PSEST,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: L-tyrosine disodium salt; Sodium butyrate; Tween 20. tween 20 hydrolyzes to a mix of C12, C14, and C16 fatty acids; this is probably part of beta oxidation (SEED_correct)",Acyl-CoA dehydrogenases,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MADYQAPLRDMRFVLNEVFDAPKLWQALPALAEVVDAETADAILEEAGKITANSIAPLNRSGDEEGCRWDAGAVSTPAGYREAYQLYAKGGWVGVGGDPAFGGMGMPKVISAQVEEMMNSASLAFGLYPMLTSGACLSIYAHASEELKQKYLPNMYAGVWSGSMCLTEPHAGTDLGIIRTKAEPQADGSYKVSGTKIFITGGEHDLTENIIHLVLAKLPDAPAGSRGISLFLVPKVMVNEDGSLGERNSLSCGSIEHKMGIQASATCVMNFDGAVGWMVGEPNKGLAAMFTMMNYERLGVGIQGLATGERSYQSAIEYARDRIQSRAPTGPVAQDKAADPIIVHPDVRRMLLTMKALNEGGRAFSSYVALQLDIAKFSDDDEARQRAEAQVALLTPVAKAFLTDMGLETTVHGQQVFGGHGFIREWGQEQLVRDCRITQIYEGTNGIQALDLVGRKIVGSGGTMYQAFVDEIRAFIADAGPELTEFAEPLKAAMDNLDELTAWVIDQAKANPNEIGAASVEYLHVFGYTAYAYMWARMAAVAVAKREEGDFYQSKLGTARFYFARLLPRIHSLSASVKAGSESLYLLDAAQF transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF4196,Psest_4269,PSEST_RS20825,WP_015278892.1,tr|L0GPQ3|L0GPQ3_PSEST,"C4-dicarboxylate transporter, DctQ subunit",Important for succinate and fumarate utilization (KEGG_correct). The other components (Psest_4268 and Psest_4270) were already annotated correctly,"TRAP-type C4-dicarboxylate transport system, small permease component","TRAP-type transport system, small permease component, predicted N-acetylneuraminate transporter","C4-dicarboxylate transporter, DctQ subunit",MNALWRVWDHFEEGFIAFLLAAMTLVTFVYVILNNLYTLFYNLGDRFEGSADFWFAIGDFIIGLAQAMTWSTALTKALFAWLIFSGLAYGVRTAGHIGVDALVKLAPRHIQRIIGIIACLLCLGYAGLLTVASFEWIQTLFIANIGAEDLGHIGVKQWHIGMIVPFGFAMVFIRFAEIFVRILRNEQTGLGLADEAADALKHGEEEPK catabolism,Pseudomonas stutzeri RCH2,psRCH2,GFF765,Psest_0779,PSEST_RS03815,WP_015275719.1,tr|L0GHV7|L0GHV7_PSEST,tyrosine aminotransferase (EC 2.6.1.57),Specifically important for: L-tyrosine disodium salt. This is cofit with 4-hydroxyphenylpyruvate dioxygenase (Psest_0779) which would be the second step in tyrosine catabolism (KEGG_correct),Aspartate/tyrosine/aromatic aminotransferase,Aspartate aminotransferase (EC 2.6.1.1),aromatic-amino-acid transaminase,MHFGQIPRVPGDPILGLMDLYRADPNPAKLDLGVGVYKDAQGLTPIPRAVKLAEQRLVDGEQSKSYIGGHGDAQFGALLLRQALGSRAIALGEQRAGCTQAPGGTGALRLAGEFIAKCLPGRSIWLSDPTWPIHETLFAAAGLRVQHYPYVGADNRLDVEGMLAALQQVPPGDVVLLHACCHNPTGFDLNHDDWLRVLEVVRARELLPLFDFAYQGFGDGLEEDAWAVRLFAETLPEMLITTSCSKNFGLYRERTGALIAVTSDAERLLDVRSQLASLARNLWSTPPAHGAAVVATILADEPLRQIWQGEVERMRRRIASLRQGLVEALMPYGLAERFAHIAQQRGMFSYTGLTAEQVRRLRAEDSVYLVESGRANVAGLDAERLDALAKAIARVVSN transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF84,Psest_0084,PSEST_RS00420,WP_015275111.1,tr|L0GG09|L0GG09_PSEST,"alpha-ketoglutarate TRAP transporter, 4TM/12TM components","specific phenotype on a-ketoglutarate and no other transporter for this substrate is apparent in the fitness data. (Mutants in another TRAP system, Psest_4268:Psest_4270, have a much milder defect in a-ketoglutarate utilization).","TRAP transporter, 4TM/12TM fusion protein","TRAP transporter, 4TM/12TM fusion protein, unknown substrate 1",,MSESQGLHASPSEWPRALFYVALLFSIYQIVTAAFHPVSSQVLRAGHVGFLLLLVFLCYPARGNGKPFQPVAWLLGLAGFATFFYQWYFEADLIQRSGDMTTADMVVGLTLIVLVFEAARRVMGIALPIICALFLAYGLLGEYLPGDLAHRGYYLDQIVNQLSFGTEGLYGTPTYVSATYIFLFILFGSFLEQAGMIKLFTDFAMGLFGHKLGGPAKVSVVSSALMGTITGSGVANVVTTGQFTIPLMKRFGYRPAFAGGVEATSSMGSQIMPPVMGAVAFIMAETINVPFVEIAKAALIPALLYFGSVFWMVHLEAKRAGLKGLPKDECPSAMAAVKERWYLLIPLVVLVWLLFSGRTPMFAGTIGLALTAIVILGSAIILKVSNFALRIAFWIALGLLCAGFFQLGIGVIFGVIAALVAVCWFIKGGRDTLVICLHALVEGARHAVPVGIACALVGVIIGVVSLTGVASTFAGYILAVGENNLFLSLLLTMLTCLVLGMGIPTIPNYIITSSIAAPALLDLGVPLIVSHMFVFYFGIMADLTPPVALACFAAAPIAKERGLKISMWAIRIAIAGFIVPFMAVYNPALMMQGGDWGATLYMLFKAAFAVGLWGAVFTGYLQRPMALWEKVLAFAAAASMVLAMPISDEIGFALGALFLIQHIWRARRAEPATA transporters,Pseudomonas stutzeri RCH2,psRCH2,GFF85,Psest_0085,PSEST_RS00425,WP_015275112.1,tr|L0GD92|L0GD92_PSEST,"alpha-ketoglutarate TRAP transporter, solute receptor component","specific phenotype on a-ketoglutarate and no other transporter for this substrate is apparent in the fitness data. (Mutants in another TRAP system, Psest_4268:Psest_4270, have a much milder defect in a-ketoglutarate utilization).","TRAP transporter solute receptor, TAXI family","TRAP transporter solute receptor, unknown substrate 1",,MRLTKRLGLLAAAAAFTASTAAVAAPTFINILTGGTSGVYYPIGVALSQQYNKIDGAKTSVQATKASVENLNLLQAGRGELAFSLGDSVEDAWNGVEDAGFKAPLKRLRAIAGTYNNYIQIVASAESGIKTLDDLKGKRISVGAPKSGTELNARAIFKAAGLDYKDMGRVEFLPYAESVELIKNRQLDATLQSSGLGMAAIRDLASTMPVTFVEIPAEVVEKIESDAYLAGVIPAGTYDGQDADVPTVAITNILVTHEKVSDEVAYQMTKLMFDNLAALGNAHSAAKDIKLENATKNLPIPLHPGAERFYKEAGVLK catabolism,Shewanella loihica PV-4,PV4,5208605,Shew_1116,SHEW_RS05745,WP_011864920.1,tr|A3QBY9|A3QBY9_SHELP,N-acetylglucosamine kinase (EC 2.7.1.59),Specifically important for: N-Acetyl-D-Glucosamine. The first step in NAG utilization (SEED_correct),"ATPase, BadF/BadG/BcrA/BcrD type (RefSeq)",N-acetylglucosamine kinase of eukaryotic type (EC 2.7.1.59),,MGFNQTEEQALVIGIDGGGSKCRATIYAADDSVLGTGVAGRANPLYGLTHTFDSISRATELALQDAGLKAGDGKTMVAGVGLAGVNVAHLYQAIKAWQHPFAEMYVTTDLHTACIGAHKGGDGAVIITGTGSCGYAHVGEQSLSLGGHGFALGDKGSGAWLGLQAAQQVLLDLDGFGPATQLTERLLEHFKVNDAMGIVEHLAGKSSGCYATLARTVLSCAQAQDEVAKAIVVEGAEYISALAHKLFEIHPPRFSMIGGLAEPLAPWLDKRVVDKISPILAPPELGAAYFARQQLGFSS transporters,Shewanella loihica PV-4,PV4,5208943,Shew_1444,SHEW_RS07400,WP_011865243.1,tr|A3QCW3|A3QCW3_SHELP,"dicarboxylate TRAP transporter (succinate, fumarate, L-malate, and alpha-ketoglutarate), large permease component","Important for utilizing succinate, fumarate, and L-malate, as expected, and also for utilizing a-ketoglutarate","TRAP dicarboxylate transporter, DctM subunit (RefSeq)","TRAP-type C4-dicarboxylate transport system, large permease component","C4-dicarboxylate transporter, DctM subunit",MTIATLFISLFLCMLLGMPIAIALGFSSMLTILLFSDDSLASVALKLYESTSEHYTLLAIPFFILSSAFLSTGGVARRIIDFAMDSVGHIRGGLAMASVMACMLFAAVSGSSPATVAAIGSIVIVGMVRAGYPEKFAAGVITTSGTLGILIPPSIVMLVYAAATEVSAARMFMAGLIPGLMMGLLLMLAIYIVARIKKLPSRPFPGFRPLAISSAKAMGGLALIVIVLGSIYGGIASPTEAAAVACVYAYFIAVFGYRDIGPLKNVSWRDSGEPLIRAILRNLGFMVLAVFKTPADKEIRHVVRDGAKVSIMLLFIIANAMLFAHVLTTERIPHLIAETIVGMGLPVWGFLIIVNLLLLAAGNFMEPSAILLIMAPILFPIATQLGIDPIHLGIIMVVNMEIGMLTPPVGLNLFVTAGITGRSMGWVIHSCIPWLALLLFFLALITYIPQISLFLPEYIDKLNGY transporters,Shewanella loihica PV-4,PV4,5208944,Shew_1445,SHEW_RS07405,WP_011865244.1,tr|A3QCW4|A3QCW4_SHELP,"dicarboxylate TRAP transporter (succinate, fumarate, L-malate, and alpha-ketoglutarate), small permease component","Important for utilizing succinate, fumarate, and L-malate, as expected, and also for utilizing a-ketoglutarate",tripartite ATP-independent periplasmic transporter DctQ (RefSeq),,"C4-dicarboxylate transporter, DctQ subunit",MMSRFFSHIEEVVLNALITAMTLLVFVEVIARFFFNTGFLWIQELTLTICGWFVLFGMSYGVKVGAHIGVDAFVKKLPAQGRKYTAILAVAICLIYCGMFLVGSWDYLAKMYQIGVPMEDIDLPHFLIGGLDGDFAWEYLRIDVEEPAVPLWTSQSILLIGFILLTWRFLQLALAIITNKTDGFAFADEAKESMHLIDQEAQADDKQPGDKK transporters,Shewanella loihica PV-4,PV4,5208945,Shew_1446,SHEW_RS07410,WP_011865245.1,sp|A3QCW5|DCTP_SHELP,"dicarboxylate TRAP transporter (succinate, fumarate, L-malate, and alpha-ketoglutarate), solute receptor component","Important for utilizing succinate, fumarate, and L-malate, as expected, and also for utilizing a-ketoglutarate","TRAP dicarboxylate transporter, DctP subunit (RefSeq)","TRAP-type C4-dicarboxylate transport system, periplasmic component",C4-dicarboxylate-binding protein DctP,MTRLNTCTFIKQIVKMTSIAALLGASLNSWAAPTEIKFSHVVAENTPKGQMALKFKQLVEERLPGEYQVNVFPNSQLFGDNNELSALLLNDVQFVAPSLSKFERYTKKLQLFDLPFLFKDMDAVNRFQQSDAGQQLLNSMKRKGVVGLGYLHNGMKQFSASSPLVLPEDAQGKKFRIMASDVLAAQFQAVEAIPVKKPFSEVFTLLQTRAIDGQENTWSNIYSKKFYEVQSNITESNHGVLDYMVVTSNTFWKSLPADKRKVIKASLDEAIAYGNEIAAAKVNKDKQAIIDSKRSEVTYLTPEQRAAWVNAMKPVWAQFEDKIGKDLIDAAVASNE catabolism,Shewanella amazonensis SB2B,SB2B,6936372,Sama_0560,SAMA_RS02985,WP_011758678.1,tr|A1S312|A1S312_SHEAM,D-mannose isomerase (EC 5.3.1.7),Specifically important for: D-Mannose; D-Mannitol. This is the first step in D-mannose catabolism. D-mannitol is probably oxidized to mannose first. (SEED_correct),hypothetical protein (RefSeq),D-mannose isomerase (EC 5.3.1.7),,MKFYNRDFLLSHSQSILDFYDPRVLDASGGYFHNYYDDGSLFEPGFRQLVSSCRITVNYARAADILDKPEYLAHARHGLNYLLNVHLQADERFAWTLKSHRPEDMTQQAYGYAFALLAFAACRKSGILKDNNKLLWIYNLLEQRFWQPEYGLYADEIGADGVLSDYRGQNANMHLCEAMIAAFEASGEGRFLDRAMEIADKIANRQAALTGGIIWEHFTPGFAINWEYNKDDPKNLYRPWGFQGGHQTEWAKLLLSLARHSHQPWLISRAKTLFDTAFEKSWDKEHGGMVYGFGPNGDWCDDDKYFWVQAESFAAAAMLYQQTGEQKYLDAYNALWNYAWQHFVDHEHGAWFRVLYRDNRKYSNEKSTAGAKCDYHTLGACFDSLRDLT transporters,Shewanella amazonensis SB2B,SB2B,6936374,Sama_0562,SAMA_RS02995,WP_011758680.1,tr|A1S314|A1S314_SHEAM,D-mannitol and D-mannose transporter (MFS superfamily),Specific phenotype on mannitol and mannose and no other transporter for these compounds is apparent in the fitness data. SEED_correct,glucose/galactose transporter (RefSeq),"Predicted mannose transporter, GGP family",,MAFVSSTTPQNGSAAPAQSHQQLLFGAMTSLFFIWGFITALNDILIPHLKGIFDLSYTQAMLVQFCFFGAYFLVSPLAGVLIARIGYLRGIIFGLSTMATGCLLFYPASSLEQYALFLLALFVLASGITILQVSANPFVARLGPERTAASRLNLAQALNSLGHTLGPLFGSLLIFGAAAGTHEAVQLPYLLLAAVIGIIAVGFIFLGGKVKHADMGVDHRHKGSLLSHKRLLLGALAIFLYVGAEVSIGSFLVNYFAEPSIGGLDEKSAAELVSWYWGGAMIGRFAGAALTRRFNPAMVLAANAVFANLLLMLTIVSSGELALVAVLAVGFFNSIMFPTIFTLAIEGLGELTSRGSGLLCQAIVGGALLPVIQGVVADNVGVQLSFIVPTFCYFYICWYAFFARNRMNGETAS catabolism,Shewanella amazonensis SB2B,SB2B,6936606,Sama_0794,SAMA_RS04165,WP_011758911.1,tr|A1S3P5|A1S3P5_SHEAM,gamma-glutamyl-gamma-aminobutyrate hydrolase [EC: 3.5.1.94],"Specifically important for: Putrescine Dihydrochloride. Similar to puuD (PUUD_ECO57), a step in putrescine catabolism. Also, is cofit with other genes from the gamma-glutamyl-putrescine pathway.",hypothetical protein (RefSeq),"Para-aminobenzoate synthase, amidotransferase component (EC 2.6.1.85)",putative glutamine amidotransferase,MPLQQESARQERKPVILMSMGQQDRNGHAYQVMTHKYMQPVVDISDCIPLLIPTCFGVADIEQYLDMADGVYLSGAASNIDPSLYGQENLTPEKKQDLARDLVDIALIKGAVKRGLPILGICRGMQEMNIAFGGDLYQKVHDEDHLNDHREDPDTPPDVQYGASHSISMVKGSWLHKLLGDTIEVNSLHGQGIKTLGKGLEALALAEDGLVEALHAPYLPQFTLGVQWHPEWKALENPDSIKIFKAFGEACRRRAGSALDLRIDKAS catabolism,Shewanella amazonensis SB2B,SB2B,6936757,Sama_0944,SAMA_RS04925,WP_011759060.1,tr|A1S445|A1S445_SHEAM,N-acetylglucosamine kinase (EC 2.7.1.59),Specifically important for: N-Acetyl-D-Glucosamine. This is the first step in NAG utilization (SEED_correct),hypothetical protein (RefSeq),N-acetylglucosamine kinase of eukaryotic type (EC 2.7.1.59),,MAFDQTLEGQLYLGIDGGGSKCRATLYNNKLDVLGTGVAGRANPLFGLEQTFESILASTEMALRDAGLSLNDASLLVAGLGLAGVNVPRLLADVQAWQHPFKTMYVTSDLHTACIGAHQGGDGAVIITGTGSCGYVHVGDESLSLGGHGFALGDKGSGAWLGLRAAEHVLLQLDGFAEPTALTERLFANLGVSDALGIVENLAGRSSSCYATLAREVFAAADEGDKVAVGILREGAAYISEMARKLFTLEPARFSMIGGLAEPLQKWLDADVVARLEPSLAAPETGAVLFAIQQHNKQSAA transporters,Shewanella amazonensis SB2B,SB2B,6937231,Sama_1401,SAMA_RS07300,WP_011759516.1,tr|A1S5F2|A1S5F2_SHEAM,D-cellobiose transporter (MFS superfamily),"Specific phenotype on cellobiose and no other transporter for this compound was apparent in the fitness data. Also, this protein is 70% identical to bglT from S. fridgimarina, which is a cellobiose transporter (PMC2996990).",sugar (glycoside-Pentoside-hexuronide) transporter (RefSeq),"Predicted beta-glucoside transporter, GPH family","glycoside/pentoside/hexuronide:cation symporter, GPH family",MLSVREKIAYGLGDTASNIVFQTVMLFLTFFYTDIFGISAAYVGTMFLAVRIMDAVTDPLMGYLADRTNTRWGRYRPYLLWFAFPFAAISVLAFTTPDLSESGKEWYAFATYALLMLAYTAINIPYCALGAALTTNPAERVSVQSYRFVFAMLGGVMVSALTLPLVDFFGQGDKAKGYQLTILAMSIVGTVMFLLCFIGTKERDFSSDDNSGNFKAASKALWANDQWRVLSAAAIFLLTGLVLKSTLAIYYVKYFLGREDMISVFVTSGVVGNIFGVALAKKLADKMCKVKAYIRLQLIAAALCMAAWFVPADQYVLALVFYIAWNFTINMGTPLLWAKMADTVDYGQFKTGVRTTGLVYSSVIFFIKLGLAIGGALGGWLLAAYGYQPDVAQTEETRAGILLCFTLYPALASIAVAFVMRHYTLDSQRVAEISVSLQQKHSGT catabolism,Shewanella amazonensis SB2B,SB2B,6937235,Sama_1405,SAMA_RS07320,WP_011759520.1,tr|A1S5F6|A1S5F6_SHEAM,glucokinase (EC 2.7.1.2),"Specifically important for: D-Cellobiose. After cellobiose is cleaved to 2 glucose (by Sama_1404), it would be phosphorylated to enter glycolysis (SEED_correct)",fructokinase (RefSeq),"Glucokinase, ROK family (EC 2.7.1.2)",fructokinase,MYRSGIDLGGTKIELVTLNEKGEEVFRKRVPTPKDYRATLEAVAGLVHDSEKETGQVSSVGIGIPGVVSAVTGRVKNSNAVWLNGQPMDKDLGAMLGREVRIANDANCFAVSESVDGGGAGKTLVFGAILGTGCGAGIAINHKVHGGGNGIGGEWGHNPLPWMTADEFNSTRCFCGNADCIETFVSGTGFVRDFRAHGGEAASGIEIVALMGKGEPLAEAAFGRFIDRLARALAHVINLLDPDVIVLGGGVSNIDEIYEYLPALLPKYVLGGECATKVVKNHHGASSGVRGAAWLWAPGEQAALPGLQEL catabolism,Shewanella amazonensis SB2B,SB2B,6937966,Sama_2099,SAMA_RS10975,WP_011760212.1,tr|A1S7E8|A1S7E8_SHEAM,UMP phosphatase (EC 3.1.3.5),"Specifically important for: Thymidine. Probably required for thymidine utilization because it allows overflow metabolism of pyrimidine intermediates, as with umpH from E. coli (which is 72% identical), see PMID 23903661",UMP phosphatase (RefSeq),Phosphatase NagD predicted to act in N-acetylglucosamine utilization subsystem,NagD protein,MKNIICDIDGVLLHDNKLVPGSDKFIHRVLEQGNPLVILTNYPVQTGKDLQNRLDAAGINVPEECFYTAAMATADFLKHQEGSKAFVIGEGALTHELYKAGFTITDINPDFVIVGETRSYNWDMIHKAARFVADGARFIATNPDTHGPAFSPACGALCAPIERITGRKPFYVGKPSSWIVRSALNHINGHSENTVIIGDNMRTDILAGFQAGLETILVLTGVSRLEDIEKEPFRPNHVFNCAGDIDIF transporters,Shewanella amazonensis SB2B,SB2B,6938088,Sama_2209,SAMA_RS11590,WP_011760322.1,tr|A1S7Q8|A1S7Q8_SHEAM,"alpha-ketoglutarate TRAP transporter, solute receptor component","specific phenotype on a-ketoglutarate. A putrescine ABC transporter (Sama_2642:Sama_2638) is also important in this condition, which is not explained. This organism can also utilize succinate or fumarate (but not L-malate), which we do not have fitness data for these under aerobic conditions. This could also transport some C4 compounds.",C4-dicarboxylate-binding periplasmic protein (RefSeq),"TRAP-type C4-dicarboxylate transport system, periplasmic component",C4-dicarboxylate-binding protein DctP,MKVSKPATLQSLFTLGKASLLATVLGFSFGAVAEPVEIKFSHVVAENTPKGQMALKFKELVESRLPGEYKVSVFPNSQLFGDNNELAALLLNDVQLVAPSLSKFERYTKKLQVFDLPFLFEDMDAVDRFQQSEAGQQLLNSMSRKGLVGLGYLHNGMKQFSANNALSLPGDAAGKKFRIMPSDVIAAQFEAVGAIPVKKPFSEVFTLLQTRAIDGQENTWSNIYSKKFYEVQTHITESNHGVLDYMLVTSETFWKSLPKDKREIIKQSMDEAVALGNKLALEKANEDRQLILDSKRVELVTLTPEQRQAWVNAMRPVWSQFEDKIGKDLIEAAESANKP transporters,Shewanella amazonensis SB2B,SB2B,6938089,Sama_2210,SAMA_RS11595,WP_011760323.1,tr|A1S7Q9|A1S7Q9_SHEAM,"alpha-ketoglutarate TRAP transporter, small permease component","specific phenotype on a-ketoglutarate. A putrescine ABC transporter (Sama_2642:Sama_2638) is also important in this condition, which is not explained. This organism can also utilize succinate or fumarate (but not L-malate), which we do not have fitness data for these under aerobic conditions. This could also transport some C4 compounds.","C4-dicarboxylate transporter, putative (RefSeq)",,"C4-dicarboxylate transporter, DctQ subunit",MISRIFGYFEEGVLNLLITLMTLLVFMEVIARFFFNTGFLWIQELTLTFCGWFVLFGMSYGIKVGAHIGVDAFVKKLKPGARRISALLAVSICLIYCAMFLKGTWDYLSQIYHIGIGMEDLDVPSVLMKQMSEDFAWDVMRIDPEDPAIPLWISQSILLLGFVMLTWRFLELAVAIFRGTSNGFSFHDEAKESMHLADEQSGANDNNNDGAKS transporters,Shewanella amazonensis SB2B,SB2B,6938090,Sama_2211,SAMA_RS11600,WP_011760324.1,tr|A1S7R0|A1S7R0_SHEAM,"alpha-ketoglutarate TRAP transporter, large permease component","specific phenotype on a-ketoglutarate. A putrescine ABC transporter (Sama_2642:Sama_2638) is also important in this condition, which is not explained. This organism can also utilize succinate or fumarate (but not L-malate), which we do not have fitness data for these under aerobic conditions. This could also transport some C4 compounds.",C4-dicarboxylate transport protein (RefSeq),"TRAP-type C4-dicarboxylate transport system, large permease component","C4-dicarboxylate transporter, DctM subunit",MTIATLFLTLFLCMLLGMPIAIALGFSSMLTILLFSNDSLASVALKLYEATSEHYTLLAIPFFILSSAFLSTGGVARRIIDFAMDSVGHIRGGLAMASVMACMLFAAVSGSSPATVAAIGSIVIVGMVRAGYPQKFAAGVITTSGTLGILIPPSIVMLVYAAATEVSAARMFMAGLIPGLLMGVLLMVAIYIVARIKNLPSRPFPGVKALSLSSAKAMGGLALIFIVLGSIYGGVASPTEAAAVACVYAYLVAVFGYRDIGPLKEVPWRKEGEAILAAIVRNLLHVGLGLIKTPTDKEIRNVVRDGAKVSIMLLFIIANAMLFAHVLTTERIPHIIAETIVGWGLPPWGFLIIVNLLLLAAGNFMEPSAILLIMAPILFPIAVQLGIDPIHLGIIMVVNMEIGMLTPPVGLNLFVTAGITGRSIGWVIHACLPWLLLLLGFLVLITYVPQISLFLPEYLDSLRGFK catabolism,Shewanella amazonensis SB2B,SB2B,6938110,Sama_2231,SAMA_RS11700,WP_011760344.1,tr|A1S7T0|A1S7T0_SHEAM,glucokinase (EC 2.7.1.2),"Specifically important for: D-Maltose monohydrate. Maltose is presumably cleaved to glucose, so the annotation as glucokinase (but not as fructokinase) fits. The other glucokinase (Sama_1405) is not important on maltose. (SEED_correct)",fructokinase (RefSeq),"Glucokinase, ROK family (EC 2.7.1.2)",fructokinase,MMRMGVDLGGTKIELVALGEDGSELFRKRIATPREYQGTLNAVVTLVNEAEATLGTQGSLGIGIPGVISPYTGLVKNANSTWINGHPLDRDLGALLNREVRVANDANCFAVSEAVDGAAAGKRVVFGAILGTGCGAGLAFDGRVHEGGNGIGGEWGHNPLPWMRPDEFNTTECFCGNKDCIETFVSGTGFVRDFRNSGGTAQNGAEIMSLVDAGDELANLVFDRYLDRLARSLAHVINMLDPDAIVLGGGMSNVQAIYARLPAILPKYVVGRECRTPVVQNLYGCSSGVRGAAWLWEKR catabolism,Shewanella amazonensis SB2B,SB2B,6938540,Sama_2643,SAMA_RS13845,WP_011760751.1,tr|A1S8Y9|A1S8Y9_SHEAM,Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),Specifically important for: Putrescine Dihydrochloride. Putrescine is catabolized via gamma-aminobutyrate (SEED_correct),putative aminotransferase (RefSeq),Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),putrescine aminotransferase,MQQQQQDTISALQAMDAAHHLHPFTDSADLAKRGTRVIERAEGVYIWDAKGNKLLDAMAGLWCVNVGYGRKSIADAAYAQLQTLPFYNNFFQCTHEPAIRLASKIASLAPGHMNRVFFTGSGSEANDTNLRMVRRYWDLKGMPSKKTIISRKNAYHGSTVAGASLGGMGFMHQQGDLPIPGIVHIDQPYWFGEGRDMSPEAFGIKTAQALEAKILELGEDKVAAFIAEPFQGAGGVIIPPDSYWNEIKRILEKYNILFILDEVISGFGRTGNWFAAQTLGLKPDLITIAKGMTSGYIPMGGVIVSDRVADVLISDGGEFAHGFTYSGHPVAAAVALENIRILEEERLVDKVRTDTGPYLQDRLQTLSAHPLVGEVRGMGMVGAIELVADKHSMVRFGSEISAGMLCREACIESGLVMRAVGDTMIISPPLCITRDEIDELIFKASQALSLTLEKIAARGN catabolism,Shewanella amazonensis SB2B,SB2B,6938542,Sama_2645,SAMA_RS13855,WP_011760753.1,tr|A1S8Z1|A1S8Z1_SHEAM,Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),Specifically important for: Putrescine Dihydrochloride. Part of the gamma-glutamyl-putrescine pathway for putrescine catabolism (SEED_correct),glutamine amidotransferase (RefSeq),Gamma-glutamyl-GABA hydrolase (EC 3.5.1.94),putative glutamine amidotransferase,MSDATIPLIGVSACNTPIGLQTFNTVGEKYLLGVINGTGGWPLIIPSIGDGMPTELLLERLDGILFTGSPSNVEPHHYSGPASEPGTHHDPRRDATTLPLIKAAIAAGVPVLGICRGFQEMNVAFGGSLHQKLHETGVFEEHREDRTAPLEVQYGLAHTVTLEPGGVIFEAWGRSSAEVNSVHTQGVERLGNGLRPEAYAPDGLIEAFSVTDAKNFALGVQFHPEWKVADNAFYLSIFNAFGDACRRRAQERAR transporters,Sinorhizobium meliloti 1021,Smeli,SM_b20002,SM_b20002,SM_b20002,NP_436545.1,tr|Q92XF6|Q92XF6_RHIME,"ABC transporter for Lactose, ATPase component",Specific phenotype on Beta-Lactose. (KEGG_correct),sugar ABC transporter ATP-binding protein,"Various polyols ABC transporter, ATP-binding component",lactose/L-arabinose transport system ATP-binding protein,MSELQLSDVRKSYGGLEVIKGVDLDIKSGEFVVFVGPSGCGKSTLLRMIAGLEEISSGDLTIDDVRMNDVDPSKRGIAMVFQSYALYPHMTVRENMGFALRFAGVPRAEIEKRVNEAAHILELGALLDRKPKQLSGGQRQRVAIGRAIVRHPKIFLFDEPLSNLDAELRVHMRIEIARLHKQLATTIVYVTHDQVEAMTLADKIVVMRAGVVEQVGSPLDLYDDPANLFVAGFIGSPKMNFLKGVIEIDEDQAYARLPDYGDAKIPVTLQAAAGTAVTIGIRPEHFDEAGPAALDLAIDMLEHLGGETFAYARHHGNGELIVVETKNGRGLKTGDRLTARFDPVSVLVFDGEGKRLRS transporters,Sinorhizobium meliloti 1021,Smeli,SM_b20325,SM_b20325,SM_b20325,NP_436851.1,tr|Q7ANS2|Q7ANS2_RHIME,"ABC transporter for D-Trehalose, periplasmic substrate-binding component",Specific phenotype on D-Trehalose dihydrate.,trehalosemaltose-binding protein,"Maltose/maltodextrin ABC transporter, substrate binding periplasmic protein MalE",,MNVKPFVRTLISCAAIAGAIDLAAAAELSMAANSTGKNLSFLRDQIARFEKETGHKVNLVTMPASSSEQFSQYRLWLAAGNKDVDVYQTDVIWAPQLAEQFVDLTEATKDVVGEHFPSIIQSQTVNGKLVALPFYTDAPALYYRKDLLDKYGKTPPKTWDELAATAKEVQDKERAAGSADIWGFVFQGNAYEGLTCNALEWIKSSGGGQIIEPDGTISVNNEKAAAAVEKVKEWIGTIAPKGVLAYQEEESRGVWQTGNAVFMRNWPYAYALGNGDDSAVKGKFEVAPLPAATDGDQPSSTLGGWNLAVSKYSDEQEAAIAFVKFLGSAETQKVRAIELSNLPTIAALYDDPEVAAAQPFMPHWKPIFQSAVPRPSAVAKVKYNEVSSKFWSAVHNTLSGNGTAAENLELLEVELTELKGDAW transporters,Sinorhizobium meliloti 1021,Smeli,SM_b20326,SM_b20326,SM_b20326,NP_436852.1,tr|Q7ANS1|Q7ANS1_RHIME,"ABC transporter for D-Trehalose, permease component 1","Specific phenotypes on D-Trehalose dihydrate. no phenotype on maltose, and there is an alpha-glucoside transporter with specific phenotype on maltose.",trehalosemaltose transporter permease,"Maltose/maltodextrin ABC transporter, permease protein MalF",trehalose/maltose transport system permease protein,MTDLSLADRPAALAHGGRIGSDLQAQRVRSAWLFLAPTFLVLALVAGWPLIRTIYFSFTNASLTNLSGAEFVGFANYLSWITLKSGRTIYRGLLADPAWWNAVWNTLKFTVLSVSIETALGLIVALVLNAQFPGRGLVRAAILIPWAIPTIVSAKMWAWMLNDQFGILNDMLIGLGLIGEKIAWTASPDTAMIAELIVDVWKTTPFMALLILAGLQMVPGDIYEAAKIDGVHPVRVFWRVTLPLIRPALMVAVIFRMLDALRIFDLIYVLTPNNAQTKTMSVMARENLFDFDKFAYGAAASTMLFLIIATITILYMWLGRLNLSGGER transporters,Sinorhizobium meliloti 1021,Smeli,SM_b20327,SM_b20327,SM_b20327,NP_436853.1,tr|Q7ANS0|Q7ANS0_RHIME,"ABC transporter for D-Trehalose, permease component 2","Specific phenotypes on D-Trehalose dihydrate. no phenotype on maltose, and there is an alpha-glucoside transporter with specific phenotype on maltose.",trehalosemaltose transporter permease,"Maltose/maltodextrin ABC transporter, permease protein MalG",trehalose/maltose transport system permease protein,MVVAIAKRTAFYALVAVIILVAVFPFYYAILTSLKSGTALFRIDYWPTDISLANYAGIFSHGTFVRNLGNSLLVATLVVAISLLLAVTAAYALARVRFRGRGLLLLTILSVSMFPQIAVLAGLFELIRFVGIFNTPLALIFSYMIFTLPFTVWVLTTFMRDLPIEIEEAAIVDGASPWVVITRVFMPLMWPALVTTGLLAFIAAWNEFLFALTFTSSNTQRTVPVAIALLSGGSQFEIPWGNIMAASVIVTVPLVVLVLIFQRRIISGLTAGGVKG transporters,Sinorhizobium meliloti 1021,Smeli,SM_b20328,SM_b20328,SM_b20328,NP_436854.1,tr|Q7ANR9|Q7ANR9_RHIME,"ABC transporter for D-Trehalose, ATPase component",Specific phenotype on D-Trehalose dihydrate.,trehalosemaltose transporter ATP-binding protein,Maltose/maltodextrin transport ATP-binding protein MalK (EC 3.6.3.19),trehalose/maltose transport system ATP-binding protein,MAELQLRDIRKSFGAFDVIKGVSMEIKPGEFMVFVGPSGCGKSTLLRLIAGLEEITSGTLAFDGQIVNQLTPSRRGIAMVFQSYALYPHMTVYENMAFGMQLAGKDKQQCRKRVEAAAEMLQLTPYLERLPRQLSGGQRQRVAIGRAIVRDPKVFLFDEPLSNLDAALRVATRLEIAKLHRSMHKTTMIYVTHDQVEAMTLADRICVLRDGLVEQIGTPLELYETPNSVFVAGFIGSPKMNFLSGAFAEPYKADTIGIRAEHLEIDEQGGEWSGTVIHSEMLGSDSYIYLDIGTGEPVIVRESGIAKHQPGQTIRISPAAGQVHRFDAGGRALGRMQMRGAA catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b20329,SM_b20329,SM_b20329,NP_436855.1,tr|Q7ANR8|Q7ANR8_RHIME,required for glucoside 3-dehydrogenase activity on raffinose or trehalose (thuA) (EC 1.1.99.13),Specifically important for: D-Raffinose pentahydrate; D-Trehalose dihydrate. a close homolog is described by PMID 23772075; (SEED_correct),"ThuA protein, function",Trehalose utilization protein ThuA,,MPQITRRMTLSINAIVWGENIHEQTNAVVREIYPDGMHNTIAAALNSDPGIDATTATLQEPEHGLSETRLAAADVLLWWGHKDHGAVDDAIVERVAKRVWEGMGLIVLHSGHFSKVFKRLMGTPCALKWREAGERERVWVVNPRHPIAEGLGENFVIENEEMYGEQFSVPEPLETVFISWFAGGEVFRSGLTWRRGAGNIFYFRPGHETYPTYHDATVHKVLRNAVKWAYNPQGTYKAIHDAPNVPVEKALEPIVERGPRLHRAGEAGYR catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b20330,SM_b20330,SM_b20330,NP_436856.1,tr|Q7ANR7|Q7ANR7_RHIME,glucoside 3-dehydrogenase; active on raffinose or trehalose (thuB) (EC 1.1.99.13),Specifically important for: D-Raffinose pentahydrate; D-Trehalose dihydrate. a close homolog is described by PMID 23772075,"trehalosemaltose utilization protein, function",Nucleoside-diphosphate-sugar epimerases,,MKGTPMRLLILGTGGMANSHAKAFAEIEGVEMVGAVDVDPSRAKAFAVTHGIENTFTSLDDAIAWGEFDAATNVTPDKAHHPTTLALIAAGKHVLCEKPLAENYEKAAEMAAAAERAGLVTMVNLTYRNVAPLQKARELVVAGEIGRVRHLEASYLQSWLVSRAWGDWASESKWLWRLSTKHGSNGVLGDVGIHILDFAGYGANSSVERVFARLKAFDKAPDNRIGEYDLDANDSFAMTAEFENGAMAVIHASRWATGHLNELRLRLHGDRGALEVIHTPDGSSLRGCMGPDVEKAVWRTVDAGTVPTNYQRFVEAVKAGRTVEPGFRHAADLQRVLDLAIETERSRRELGVPDIDAVVQERAVG catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b20890,SM_b20890,SM_b20890,NP_437724.1,tr|Q92UE7|Q92UE7_RHIME,L-arabonate dehydratase (EC 4.2.1.25),Specifically important for: L-Arabinose. L-arabonate is an intermediate in the oxidation of L-arabinose (SEED_correct),dihydroxy-acid dehydratase,L-arabonate dehydratase (EC 4.2.1.25),dihydroxy-acid dehydratase,MKKKAEWPRKLRSQEWFGGTGKNAIMHRSWMKNQGLPADTFDGRPIIGICNTWSELTPCNAHLRDLAERVKRGVYEAGGFPVEFPVFSTGESTLRPTAMMFRNLAAMDVEESIRGNPVDGVVLLGGCDKTTPSLLMGAASVDIPAIVVSGGPMLNGKWRGKDVGSGTAIWQFSEMVKSGEMSLEEFMDAEQGMARSAGSCMTMGTASTMASMAEALGMTLSGNAAIPAVDARRRVISQLTGRRIVEMVKEDLKPSDILTKEAFENAIRVNGAVGGSTNAVLHLLALAGRVGVDLSLDDWDRLGRDVPTIVNLQPSGKYLMEEFYYAGGLPVVIKAVAEMGLLHNDAITVSGDTIWNDVKGVVNYNEDVILPREKALTKSGGIAVLRGNLAPRGAVLKPSAASPHLMQHKGRAVVFESIEDYHARINREDLDIDETCIMVLKYCGPKGYPGMAEVGNMGLPPKVLKKGITDMIRISDARMSGTAYGTVILHTAPEAAEGGPLALVENGDLIEVDIPNRTLHLHVSDEELARRRAAWVSPVKPLTGGYGGLYIKTVMQADAGADLDFLVGARGSVVERDSH catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b20891,SM_b20891,SM_b20891,NP_437725.1,tr|Q92UE6|Q92UE6_RHIME,Ketoglutarate semialdehyde dehydrogenase (EC 1.2.1.26),"Specifically important for: L-Arabinose. The only dehydrogenase steps in the arabinose catabolism pathway are L-arabinose 1-dehydrogenase (which is SMc00588) and a-ketoglutarate semialdehyde dehydrogenase (EC 1.2.1.26). So, this is the semialdehyde dehydrogenase.",dehydrogenase,Aldehyde dehydrogenase B (EC 1.2.1.22),,MTLHQNLIAGEWVGGDGVANINPSNTDDVVGEYARASAEDAKAAIAAAKAAFPAWSRSGILERHAILKKTADEILARKDELGRLLSREEGKTLAEGIGETVRAGQIFEFFAGETLRLAGEVVPSVRPGIGVEITREPAGVVGIITPWNFPIAIPAWKLAPALCYGNTIVFKPAELVPGCSWAIVDILHRAGLPKGVLNLVMGKGSVVGQAMLDSPDVQAITFTGSTATGKRVAVASVEHNRKYQLEMGGKNPFVVLDDADLSVAVEAAVNSAFFSTGQRCTASSRIIVTEGIHDRFVAAMGERIKGLVVDDALKPGTHIGPVVDQSQLNQDTDYIAIGKQEGAKLAFGGEVISRDTPGFYLQPALFTEATNEMRISREEIFGPVAAVIRVKDYDEALAVANDTPFGLSSGIATTSLKHATHFKRNAEAGMVMVNLPTAGVDFHVPFGGRKASSYGPREQGKYAAEFYTNVKTAYTLA catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b20892,SM_b20892,SM_b20892,NP_437726.1,tr|Q92UE5|Q92UE5_RHIME,2-dehydro-3-deoxy-L-arabinonate dehydratase (EC 4.2.1.43),"# Specifically important in carbon source L-Arabinose. Similar to PA2216 from Pseudomonas aeruginosa (see PMC:PMC4038344) and to gguC or araD1 (Atu2345) from Agrobacterium tumefaciens (see PMC: PMC3232879), which also have this activity. In Agrobacterium, this reaction is proposed to be a step in L-arabinose oxidation. (Note that 2-keto-3-deoxy-L-lyxonate and 2-keto-3-deoxy-L-arabinonate are the same compound.)",hypothetical protein,SUGAR TRANSPORTER,,MLISQVRKEDGSVIVAVRAPGETARAVRGAESVYALAMEAANSRRSLRDVVDAKGFGETIDLEEAYSQGRLLSPITHPDPAHLHLTGTGLTHLGSAATRDAMHKKATEATEETLTDSMKMFRMGLEGGKPKSGEKGVQPEWFYKGNGTTAVAPGEPLVSPSFAEDGGEEPEMAGIYVISDKGVPFRLGFAVANEFSDHKTERVNYLWLAHSKLRQASFGPEIRIGAAPEDIRGASRILRGGKVLWEKPFLSGEANMSHSFANLEYHHFKYGLFRAPGDIHVHMFGTATLSFGDGIRTEEGDVFEIEAKEFGLPLRNPLAIAAEEEVAVHQL catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21101,SM_b21101,SM_b21101,NP_437220.1,tr|Q92VM0|Q92VM0_RHIME,"L-fucono-1,5-lactonase; D-arabinolactonase","# Specifically important in carbon source D-Arabinose; carbon source L-Fucose. A related protein (29% identical) was shown by Hobbs et al (2013) to be a L-fucono-1,5-lactonase. Note that D-arabinolactone is chemically similar to L-fuconolactone, with one methyl group replaced with a hydrogen.",hypothetical protein,L-fuconolactone hydrolase,,MIIDTHLHLIDKSALNYPWLAGVPALDRDFLYATYAAEAKRVGVAASLHMEVDVDPAEIELETREVARLAGEPGSLLKGAIAACRPEDEGFAAYLERQEENAFVKGFRRVLHVVTDDLSEQPLFRENVKRLSGTRFTFDLCVLPHQIPKAIALADLAPDVQFILDHCGVPDIKGHAEHPWRDHMTEIARHPNVVAKISGVVAYAEEDWALDSIRPYVEHTISVFGWDRVVWGSDWPVCTLGGNLSTWVAATQALIEGCSPQERRKLLSGNAQRIWNLI transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21103,SM_b21103,SM_b21103,NP_437222.1,tr|Q926H4|Q926H4_RHIME,"ABC transporter for L-Fucose, periplasmic substrate-binding protein",Specific phenotype on L-Fucose.,sugar ABC transporter substrate-binding protein,"Maltose/maltodextrin ABC transporter, substrate binding periplasmic protein MalE",multiple sugar transport system substrate-binding protein,MNRLLSGVSAGVIMLACAMGAAKAADLPGKFEGVTIDAKLIGGQQYEKLYERIGEWEKATGAKVNILSKKNHFELDKEIKSDIATGGLTWCVGSNHSSFAPQYPDIYADLFGLIPSEEVAKFVPAVIDASTLEGKLVMLPRAQFDVSALYFQKSLYQDEAKKTEFKAKYGYDLAPPDTWAQVSDQAEFFAAPPNFYGTQFAGKEEAINGRFYEMLVAEGGEYLDKDGRPAFNSDAGVRALEWFVKLYKDKAVPPGTTNYLWDDLGQGFASGSIAVNLDWPGWASFFNDPKSSKVAGNVGVKVQPAGSSGKRTGWSGHHGFSVTESCADKEAAASLVWWLTNEDSQKLESAAGPLPTRSAVWDFNIKAAEGDAYKTEVLQAFQEAAKHAFPVPQTAEWIEISNAVYPELQAAILGDKTSKEALDAAAEKATGILEDAGKL transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21104,SM_b21104,SM_b21104,NP_437223.2,tr|Q92VL8|Q92VL8_RHIME,"ABC transporter for L-Fucose, permease component 1",Specific phenotypes on L-Fucose.,sugar ABC transporter permease,"Maltose/maltodextrin ABC transporter, permease protein MalF",,MKLSKLSAPTLLLLPAFIVLAVFIVLPLIFSLYSSFTPFRLTKPDSLWVFIGFRNYVNVLTNAEFWVAFGRTVLLLTVALNAEMFLGLGLALLVNKATYGQRALRTAMMFPMMFSPVLVGFQFKFLFNDNIGFVNNALQSLGLTDRAIPWLIDGNLALFSIIVAEVWSSTAVFAILILAGLLAMPKDPVEAAHVDGCTPWQTFRYVTWPYLMPFAFIAMTIRSLDVARAYDIVKIMTDGGPAKRTELLWTLIGRTAYGDARMGMANAMAYVAILLSIFFTVYFFRKLAAARQQIGAEW transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21105,SM_b21105,SM_b21105,NP_437224.1,tr|Q92VL7|Q92VL7_RHIME,"ABC transporter for L-Fucose, permease component 2",Specific phenotypes on L-Fucose.,sugar ABC transporter permease,"Maltose/maltodextrin ABC transporter, permease protein MalG",multiple sugar transport system permease protein,MDTNASHRLRRRLLKVAHLAGLFLAMLVICLPGLWIVLSSLRPTVEIMAKPPVWIPETLSLDAYRAMFSGAGQGGVPVWDYFRNSLIVSVTSTVIALAIGLSGGYAFARYRFKAKSAIFLGFMLTRAVPGIALSLPLFMLYARTGIIDTHFSLILTYVALNVPFTIWLIDGFFRQVPKDLAEAAQIDGCTPWQAFWQVEFPLAGPGIASAGIFAFLTSWNEYALASQITRSVNSKTLPVGLLDYTAEFTIDWRGMCALAVVMIVPALTLTFIIQKHLVSGLTFGAVKG transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21106,SM_b21106,SM_b21106,NP_437225.1,tr|Q92VL6|Q92VL6_RHIME,"ABC transporter for L-Fucose, ATPase component",Specific phenotype on L-Fucose.,sugar ABC transporter ATP-binding protein,Maltose/maltodextrin transport ATP-binding protein MalK (EC 3.6.3.19),multiple sugar transport system ATP-binding protein,MAPVTLKKLVKRYGALEVVHGIDLEVKDREFIALVGPSGCGKSTTLRMIAGLEEVSGGAIEIGGRKVNDLPPRARNISMVFQSYALYPHMTVAENMGFSLKIAGRPAEEIKTRVAEAAAILDLAHLLERRPSQLSGGQRQRVAMGRAIVRQPDVFLFDEPLSNLDAKLRTQVRTEIKKLHARMQATMIYVTHDQVEAMTLSDRIVIMRDGHIEQVGTPEDVFRRPATKFVAGFIGSPPMNMEEAVLTDGKLAFASGATLPLPPRFRSLVREGQKVTFGLRPDDVYPSGHGLHAGDADAVHEIELPVTITEPLGNETLVFTQFNGRDWVSRMLNPRPLRPGEAVPMSFDLARAHLFDGETGRALAS catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21107,SM_b21107,SM_b21107,NP_437226.1,tr|Q92VL5|Q92VL5_RHIME,dehydratase involved in L-fucose catabolism,"# Specifically important in carbon source L-Fucose. T. Lukk's 2009 thesis reported that this protein had weak activity as a L-fuconate dehydratase, which was the expected substrate. The fitness data shows that two other dehydratases are involved in fucose catabolism as well (see SMc02776 and SM_b21110), so the pathway is probably more complicated.",mandelate racemase or evolutionary related enzyme of the mandelate racemase muconate lactonizing enzyme family protein,mandelate racemase/muconate lactonizing enzyme family protein,,MARIEKTELRMVDLPPKNKRTDAIQSFVSQETPIVTITDADGATGTGYSYTIGTGGSSVMRLLSDHLVPILLGEDADCIEALWQKMEFATHATTIGAITALALAAVDTALWDLRAKKQKLPLWKLAGGAKESCPLYTTEGGWLHIEKQALVDDALQAKANGFSGSKVKIGKPSGAEDYDRLSAMRAALGDGFEIMTDCNQGFTVDEAIRRAARLRELDLAWIEEPLPADDLDGHIRLTRSTPTPIAVGESIYSIRHFREYMQKGACSIVQVDVARIGGITPWLKVAHAAEAFDIPVCPHFLMELHVSLVCAVPNGKYVEYIPQLDDLTQMGMEIREGRAIAPSNPGIGIAWDWEAVKARSVAEFTREFRR catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21108,SM_b21108,SM_b21108,NP_437227.1,tr|Q92VL4|Q92VL4_RHIME,L-fucose mutarotase (EC 5.1.3.29),Specifically important for: L-Fucose. The first step in fucose catabolism. This protein is related to XCC4070 (FUCM_XANCP),hypothetical protein,,,MQRMGMVIGLEPSKIAEYKRLHAAVWPEILALISECNITNYSIFLKEPENLLFGYWEYVGEDFEADMTKMAAHPKNQEWWSVCMPCQKPLESRRQGEWWAMMEEVFHHD catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21109,SM_b21109,SM_b21109,NP_437228.1,tr|Q92VL3|Q92VL3_RHIME,L-fucose dehydrogenase (EC 1.1.1.122),Specifically important for: L-Fucose. The second step in fucose catabolism.,oxidoreductase,"oxidoreductase, Gfo/Idh/MocA family",,MTETFDPSSLRQWWPKPVAPRPIVIFGAGSIVGDAHLPAYRNAGFPVAGIFDPDAGKAAALARASDVMAFTSEEEALSAENAIFDLATPPAAHASILSKLPKGSFALIQKPLGSDLAAATGILEICRERNIRAAANFQLRFAPMMLALKDAIATGYLGEVVDFDAWLALATPWGLWPFLKGLPRIEIAMHSIHYLDLVRSLLGDPRGVHAKTIGHPNHDVAQTRTAAILDYGDAVRCVLSVNHDHDFGRRFQACEFRICGTRGAAYVKLGVNLDYPRGEPDELWIRPAGGADWIQVPLEGSWFPDAFANRMANLQRHAGGEDDELIGSVEDAWRTMALVEAAYQSSARPATPIAALPLEN catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21110,SM_b21110,SM_b21110,NP_437229.1,tr|Q92VL2|Q92VL2_RHIME,dehydratase involved in L-fucose catabolism,# Specifically important in carbon source L-Fucose. This protein is similar to the (R)-enoyl-CoA hydratase portion of E. coli maoC (36% identical). It is one of three dehydratases involved in L-fucose catabolism (see SMc02776 and SM_b21107),"MaoC-like (monoamine oxidase-like) protein, NodN",Aldehyde dehydrogenase (EC 1.2.1.3),,MSEQTIYYEDYEQGHVRLTSGRTITETDFVVHAGHTGDFFPHHMDAEFAKTLPGGQRIAHGTMIFSIGVGLTASLINPVAFSYGYDRLRFVRPVHIGDTIRTRVTIAAKEDDPKRPGAGRVVERCEVINQRGEVVLAADHILIVERKPEGTIQ catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21112,SM_b21112,SM_b21112,NP_437231.1,tr|Q92VL0|Q92VL0_RHIME,"L-2,4-diketo-3-deoxyrhamnonate hydrolase; 2,4-dioxopentanoate hydrolase","# Specifically important in carbon source L-Fucose; carbon source D-Arabinose. SM_b21112 is 58% identical to LRA6, which is L-2,4-diketo-3-deoxyrhamnonate hydrolase (PMID:19187228). This reaction is also proposed to be involved in L-fucose catabolism (PMID:17144652), which explains the phenotype on L-fucose. Its role in D-arabinose catabolism suggests that it also acts on a five-carbon substrate such as 2,4-dioxopentanoate. This protein belongs to the fumarylacetoacetate hydrolase family, which hydrolyses substrates of the form R-CO-CH2-CO-R'. Both keto groups are involved in the catalytic mechanism (Bateman et al 2001) so we propose that 2,4-dioxopentanoate is the intermediate in D-arabinose catabolism. This would be formed from D-arabinoate by two successive dehydratase reactions (see SMa0247 and SM_b21112).","bifunctional 2-hydroxyhepta-2,4-diene-1, 7-dioatesomerase/5-carboxymethyl-2-oxo-hex-3-ene-1, 7-dioatedecarboxylase","2,4-diketo-3-deoxy-L-fuconate hydrolase",,MKLLRYGEPGQEKPGLLGSDGIIRDLSGHVSDLAAGALDPSKLDELANLDVETLPAVSGNPRLGPCVAGTGKFICIGLNYSDHAAETGATVPPEPIIFMKATSAIVGPNDDLVLPRGSEKTDWEVELGIVIGKTAKYVSEAEALDYVAGYCTVHDVSERAFQTERHGQWTKGKSCDTFGPTGPWLVTKDEVADPQDLAMWLKVNGETMQDGSTKTMVYGAAHLVSYLSQFMSLRPGDIISTGTPPGVGMGMKPPRYLKAGDVVELGIEGLGSQKQRVRADA catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21164,SM_b21164,SM_b21164,NP_437370.1,tr|Q92V79|Q92V79_RHIME,N-formylglutamate deformylase (EC 3.5.1.68),"Specifically important for: L-Histidine. KEGG suggests formimidoyl-L-glutamate as the substrate, while SEED suggests N-formylglutamate. This gene is cofit with a deiminase or iminohydrolase (which produces N-formylglutamate) which suggests that the SEED annotation is correct. (SEED_correct)",formiminoglutamase,N-formylglutamate deformylase (EC 3.5.1.68),formiminoglutamase,MAVFEVRQGSSPVILGFPHTGTDVPASIRERLNDNGRILADTDWHIHDLYQGLLPDATAVRATFHRYVIDANRDPAGVSLYPGQNTTGLVPETDFDGLPIWKEGEGPTEVDITERLRDFHAPYHAALSAEIARVKAIHGVVVLYDCHSIRSHIPFLFEGRLPDFNIGTDMGKTCATEIERAAAEIAAGAECYSHILNGRFKGGWTTRHYGRPEQGVHAIQMELAQSTHLATEAPPFALDRAKADRLRRPLKAILERIETVAKELKRTGSEA transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21216,SM_b21216,SM_b21216,NP_437260.1,tr|Q92VI3|Q92VI3_RHIME,"ABC transporter for D-Glucosamine, ATPase component",Specific phenotype on D-Glucosamine Hydrochloride.,sugar ABC transporter ATP-binding protein,Maltose/maltodextrin transport ATP-binding protein MalK (EC 3.6.3.19),,MSALEIRNIRKRYGEVETLKGIDIALESGEFLVLLGSSGCGKSTLLNIIAGLAEPSGGDILIGERSVLGVHPKDRDIAMVFQSYALYPNLSVARNIGFGLEMRRVPQAEHDKAVRDTARLLQIENLLDRKPSQLSGGQRQRVAIGRALVRNPQVFLFDEPLSNLDAKLRMEMRTELKRLHQMLRTTVVYVTHDQIEAMTLATRIAVMRDGRIEQLAAPDEVYDRPATLYVAGFVGSPPMNILDAEMTANGLKIEGCEEVLPLPAAFNGAAWAGRRVKVGIRPEALRLAAGSEAQRLTASVEVVELTGPELVTTATVGSQRITACLPPRTAVGMGSAHAFTFDGTALHLFDPESGRSLRME transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21219,SM_b21219,SM_b21219,NP_437263.1,tr|Q92VI0|Q92VI0_RHIME,"ABC transporter for D-Glucosamine, permease component 1",Specific phenotypes on D-Glucosamine Hydrochloride.,sugar ABC transporter permease,"Glycerol-3-phosphate ABC transporter, permease protein UgpE (TC 3.A.1.1.3)",multiple sugar transport system permease protein,MERQSPLFSVFIHASALLLAVVILAPVAWLLIMSISPAADLSAKPLAWWPSDIDLSRYRTLLSAVENSAGAAFIASLLNSIKVAGMATLAAVVVAVPAAWAVSRTPAVAWSLYAVIATYMLPPVALAVPLYMGLAYFGLLNSVFGLALVYLTILAPFTTWLLKSGFDSIPREIESAAMIDGARLDQILRILTLPLAAPVMATSALFAFLLAWDEFFYALLFTSDQRAKTLTVAIADLAGGRVSDYGLIATAGVLAALPPVLIGLIMQRALISGLTSGGVKG transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21220,SM_b21220,SM_b21220,NP_437264.2,tr|Q92VH9|Q92VH9_RHIME,"ABC transporter for D-Glucosamine, permease component 2",Specific phenotypes on D-Glucosamine Hydrochloride.,sugar ABC transporter permease,"Glycerol-3-phosphate ABC transporter, permease protein UgpA (TC 3.A.1.1.3)",multiple sugar transport system permease protein,MTGTWISTRAWLLMLPLLVVMTAVIGWPLVDTVRLSFTDAKLVGTEGGFVGTANYIKMLGGSNFQRALVTTTWFAVISVAAEMVLGVLAALLLNQQFRGRTALRALMILPWALPTVVNATLWRLIYNPEYGALNAALTQLGLLDSYRSWLGEPGTALAALIVADCWKNFPLVALIALAALQAVPRDITAASLVDGAGPFNRFRFVIMPYLAGPLLVALVLRTIEAFKVFDIIWVMTRGGPANSTRTLSILVYQEAFSFQRAGSGASLALIVTLLVTILAAAYAALLRKAAGAS transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21221,SM_b21221,SM_b21221,NP_437265.1,tr|Q926H2|Q926H2_RHIME,"ABC transporter for D-Glucosamine, periplasmic substrate-binding protein",Specific phenotype on D-Glucosamine Hydrochloride. (SEED_correct),sugar uptake ABC transporter substrate-binding protein precursor,"Glucosamine ABC transport system, periplasmic sugar-binding protein",multiple sugar transport system substrate-binding protein,MLKSTRNALIGATLIGAGFTGHAQAETTLNALFMAQAAYSEADVRAMTDAFAKANPDIKVNLEFVPYEGLHDKTVLAQGSGGGYDVVLFDVIWPAEYAANNVLLDVTDRVTDEMNKGVLPGAWTTVEYDGKRYGMPWILDTKYLFYNKEILEKAGIKQPPKTWEELSEQATAIKDKGLLESPIAWSWSQAEAAICDYTTLVSAYGGKFLDGGKPAFTTGGGLDALNYMVTSYTSGLTNPNSKEFLEEDVRKVFQNGEAAFALNWTYMYNLANDPKESKVAGKVGVVPAPGAAGKSEVSAVNGSMGLGITATSKHPEEAWKYIVHMTSQETQNAYAKLSLPIWASSYENPNVTKGQEELIAAAKRGLAAMYPRPTTPKYQELSTALQQAIQEALLGQSSAEDALKSAAENSGL catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21373,SM_b21373,SM_b21373,NP_437491.1,tr|Q92UY3|Q92UY3_RHIME,tagatose 6-phosphate kinase [EC: 2.7.1.144],"Specifically important for: D-Tagatose. annotated as 6-phosphofructokinase by SEED, but tagatose 1,6-bisphosphate is the relevant intermediate for tagatose catabolism (KEGG_correct)",sugar kinase,"6-phosphofructokinase (EC 2.7.1.11), PF08013 family",tagatose 6-phosphate kinase,MQENHLIDIARWSERPGPRGIPSICSAHPLVIEAAMLRAHREKAPVLIEATCNQVNQDGGYTGMTPEDFTRFVGAIADRIEFPREKILLGGDHLGPNPWKHLPADEAMAKAEAMITAYAKAGFTKLHLDTSMGCAGEPTALPDATTAARAARLAAVAEDAVGGRGGVLPVYIIGTEVPIPGGALEELDTLEVTAPEAAIETVRVHRAAFEEAGAAGAFSRVVGAVVQPGVEFGNENVIAYDRARAEKLSATLGQLHGMVFEAHSTDYQTPDALRELVADGFAILKVGPGLTFALREALYGLDQIAAFLFPAARERTLAEVTEAVMREEPANWAKYYHGSAEEQRLQRHFSYSDRIRYYWPHPKAAAAVDELMSLLDGVAIPETLISQFLAGSYARVRNGEVAPQAKPLALAAVDAVLQDYFAACRV catabolism,Sinorhizobium meliloti 1021,Smeli,SM_b21374,SM_b21374,SM_b21374,NP_437492.1,tr|Q92UY2|Q92UY2_RHIME,tagatose kinase (EC 2.7.1.101),Important on tagatose. This is the first step in tagatose catabolism.,sugar kinase,Fructokinase (EC 2.7.1.4),,MHKILTIGEILVEIIATEKGDGFRKATPLIGPFPSGAPAIFIDQAGKLGQPCAIISRVGGDDFGTVNLERLKRDGVDISGIEVDPLATTGSAFVRYRPDGSRAFVFNIRDSACGKITLDERMTRLVGECSHVHVMGSSLYAPSVVESILAAIGIVKAGGGTVSFDPNLRPEILKSPGMREALLTVLAETDLFLPSGDELFLFTEAKTESQAVAELLASGIKAVVVKRGAAGASYFDAGAALSLPGFPVEEIDPTGAGDCFGATFVSFWLNGASPREALEFAAASGARAVMHFGPMEGASTRAELERFISEQQA transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21644,SM_b21644,SM_b21644,NP_438103.1,tr|Q92TF7|Q92TF7_RHIME,"ABC transporter for D-Raffinose, ATPase component",Specific phenotype on D-Raffinose pentahydrate.,ABC transporter ATP-binding protein,Oligopeptide transport system permease protein OppB (TC 3.A.1.5.1),peptide/nickel transport system ATP-binding protein; peptide/nickel transport system ATP-binding protein,MVTIESIVPAPEERRDRDMKDERPVIDARKVAVSFKVENGTVQAVKDVSFQLYRGETVAIVGESGSGKSVTARTVMGLLSKRATIAPQARIEYDGRDVLKFSKRERRALRGDRISMIFQEPMSSLNPVYTIGSQIIEAIRAHRRVSRRAAAERALELLRHVQIPDPEARLNQYPHQLSGGQRQRVMIAMALANDPDVLIADEPTTALDVTVQAQILNLIRKLQQELGMAVILITHDLTVVRQFSDYVYVMQLGEVKEHNTTEALFADPQHAYTRRLLSSEPSGSANPLPDDAPILLDGRNVRVSFTLKKGGFFRPEFKELVAVDGLSLNLRRHETLGLVGESGSGKTTFGQALIRLLNTDGGEIYFEGEPIHDKDRKGMRPLRSKIQIVFQDPFSSLNPRMSVGQIIEEGLIVNGMGENRKDRLKRVEDALVSAGMPSNILSRFPHEFSGGQRQRIAIARAVALEPEFILLDEPTSALDLSVQAQIIELLRRLQDERGLSYLVISHDLKVVRALCHRVVVMQDGKIVEEGPVSEVLNNPKTAYTERLVKAAFEVAA transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21645,SM_b21645,SM_b21645,NP_438104.1,tr|Q92TF6|Q92TF6_RHIME,"ABC transporter for D-Raffinose, permease component 1",Specific phenotypes on D-Raffinose pentahydrate.,ABC transporter permease,"ABC transporter, permease protein",peptide/nickel transport system permease protein,MTIEAESTLPVAIEPEEKHHHESYSALVWRRLKRSWTGLLGLVLVCLLILMAVFAEFLSPVDPKATDVAFAQPQTISFRDKEGNFVFPPRSYPVRETEELDPVTFQPIIGPDYDNPQVLGFFVKGAPYRLFGLIPAERHLFGAVDGTPVHLLGTDKFGRDVLSRILYGSRISLMIALTVVFIVTVVGTTVGMVSGYFGGRFDAWVQRFVELVLAFPQLPLYLALASLIPVTAPTNVFLAFVIIVMSALGWAQMSREVRGKTLALARIEYVRAAIAIGATDRRIIFQHIFPNVMSHVIVAVTLAIPHVVLLESFLGFLGFAVKPPLISWGLMLQDTANYSAIGSYPWILSPVAFVLVTVFAFNALGDGLRDAIDPY transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21646,SM_b21646,SM_b21646,NP_438105.1,tr|Q92TF5|Q92TF5_RHIME,"ABC transporter for D-Raffinose, permease component 2",Specific phenotypes on D-Raffinose pentahydrate.,ABC transporter permease,"ABC transporter, permease protein",peptide/nickel transport system permease protein,MFRFLLVRIASAIPVLLVLSVVTFGIIQAPPGDYSDYIRSQLINQGGASFEEADAQAQAYRKEHGLDKPLPIQYVNWITGIVTRGDFGHSLYYNKPVADVVGERLPRTLALALVCHILASVIGIAFGIIAATRQYSWIDSLLSTVSFLGMTVPRFLMALIIVYILVFHFNVSEINSFHSARYGGAPWSWDKFVDLIKHVWPVVAIATFGGLAYNMRVMRGNLLDTLNAQYVETARAKGLSEGAVVMRHAVPNALHPLIMYQGVVLPYMLTGEIETAIIFALPTVGPAIVGSMWVGDVYVTATFMLVLSATLIVGNIIADMMLAALDPRVRMGGGVYA transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21647,SM_b21647,SM_b21647,NP_438106.1,sp|Q9X4Y1|AGPA_RHIME,"ABC transporter for D-Raffinose, periplasmic substrate-binding protein",Specific phenotype on D-Raffinose pentahydrate.,alpha-galactoside ABC transporter substrate-binding protein precursor,Periplasmic alpha-galactoside-binding protein precursor,peptide/nickel transport system substrate-binding protein,MKTHRLNMTASLLIGISAFAVQAFASEPTVVPEQPPFPAQGKITYVSRDSILEFKALREYREPEWVTEKFVKAGKLPPVAERLPKEPMVFKAGNMPDGMGVYGDVMRHVIGGRPEGWNYSAGQTQGWGGIDIGMFECLTRTAPLFQVEADDMEPLPNLAKSWDWSEDGRKLTMHLIEGAKWSDGDPFDADDVMFYWEDNVLDSSVSPLNGATPETFGEGTTLKKIDQYTVEWTFKEAFPRQHLFAMAYGTFCPGPSHILKTKHPKYAGTTYNEYKNGFPAEYMNLPVMGAWVPVAYRPDDIIVLRRNPYYWKVDEAGNQLPYLNELHYKLSTWADRDVQAIAGSGDISNLEQPENFVESLKRAANESAPARLAFGPRVIGYNMHMNFSGNGWGDPDERAKAVRELNRNLDFRKAVTMAVDRKKLGEALVKGPFTAIYPGGLSSGTSFYDRNSTIYYPHDLEGAKVLLEKVGLKDTDGNGFVNFPAGKLGGRDVEIVLLVNSDYSTDRNLAEGMVGQMEKLGLRVVLNALDGKQRDAANYAGRFDWMIHRNTAEFASVVQNTPQLAPTGPRTSWHHRAPEGGEVDVMPHEQELVDIVNKFIASNDNDERTELMKQYQKVATTNVDTVGLTEYPGALIINKRFSNIPPGAPIFMFNWAEDTIIRERVFVAADKQGDYELYPEQLPGKPGESGPIN transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21652,SM_b21652,SM_b21652,NP_436541.1,tr|Q926I0|Q926I0_RHIME,"ABC transporter for Lactose, periplasmic substrate-binding component",Specific phenotype on Beta-Lactose. (KEGG_correct),lactose ABC transporter substrate-binding protein,ABC transporter sugar binding protein,lactose/L-arabinose transport system substrate-binding protein,MDIQTRAYLPRIAGLALAGASFLGVTAAQAKEITIWCWDPNFNVAIMKEAGARYTKTHPDVTFNIVDFAKLDVEQKLQTGLSSGTADALPDIVLIEDYGAQKYLQSFPGAFAPLSGTVDYSGFAPYKVELMTLDGQVYGMPFDSGVTGLYYRKDYLEAAGFKPEDMQDLTWDRFIEIGKQVEEKTGKKMMGLDPNDAGLVRIIMQSAGQWYFDKEGKPNIAGNAALKAALETIGKIMQANIYKPANGWSDWVGTFTSGDVATVVTGVWITGTVKAQPDQSGNWGVAPIPALSIEGATHASNLGGSSWYVLESSEEKAEAIDFLNEIYAKDIDFYQKILQDRGAVGSLLAARGGAAYEAADPFFGGEKVWQNFSDWLAKVPSVNYGIFTNEADLAVTAQLPAVTQGTPVDEVLQAIEAEVSAQIQ transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21653,SM_b21653,SM_b21653,NP_436542.1,tr|Q92XF9|Q92XF9_RHIME,"ABC transporter for Lactose, permease component 1",Specific phenotypes on Beta-Lactose. Not important for arabinose utilization but there is no other clear arabinose tranpsorter so who knows (KEGG_correct),lactose ABC transporter permease,Inositol transport system permease protein,lactose/L-arabinose transport system permease protein,MVRARRGIGRYYDVNGWLFVAPALGLITLFMVYPIAWSLWMSFQSGRGMTLKFAGFANIVRLWNDPVFIKALTNTMTYFVVQVPIMILLALILASLLNNPRLVGRGVFRTAIFLPCVSSLVAYSVLFKGMFATDGIVNSTLQAIGLAASPIPWLTHPFWAKVLVILAITWRWTGYNMIFYLAALQNIDKSIYEVARIDGVPAWARLTHLTIPLLKPVILFTTVISTIGTLQLFDEVYNLTEGKGGPSNATLTLSLYIYNLTFRFMPNLGYAATVSYVIVVLVALLAFVQFFAARERDR transporters,Sinorhizobium meliloti 1021,Smeli,SM_b21654,SM_b21654,SM_b21654,NP_436543.1,tr|Q92XF8|Q92XF8_RHIME,"ABC transporter for Lactose, permease component 2",Specific phenotypes on Beta-Lactose. Not important for arabinose utilization but there is no other clear arabinose tranpsorter so who knows (KEGG_correct),lactose ABC transporter permease,"Glycerol-3-phosphate ABC transporter, permease protein UgpE (TC 3.A.1.1.3)",lactose/L-arabinose transport system permease protein,MNGFGRFAAMAATYGFLGLMAFLSVFPFIWMVLGATNSSIDIIKGKLLPGAAFATNVANFFTLVNVPLVFWNSAKIAIVATVLTLAVSSLAGYGFEMFRSRRRERVYRAMLLTLMIPFAALMIPLFVMMGKAGLINTHLAVVLPSIGSAFVIFYFRQSTKAFPSELRDAAKVDGLKEWQIFLFIYVPVMRSTYAAAFVIVFMTAWNNYLWPLIVLQTNETKTITLVISSLASAYYPDYGVVMVGTILATLPTLAVFFFMQRQFVQGMLGSVK catabolism,Sinorhizobium meliloti 1021,Smeli,SMa0247,SMa0247,SMa0247,NP_435377.1,tr|Q930R4|Q930R4_RHIME,"2-dehydro-3-deoxy-D-arabinonate dehydratase, 2,4-dioxopentanoate forming","# Specifically important in carbon source D-Arabinose. SMa0247 is related to 2-keto-3-deoxyxylonate dehydratase (xylX), which acts on the same substrate. Furthermore, a close homolog (HSERO_RS19360, 59% identical) is important for D-xylose utilization. However, the product of XylX is 2,5-dioxopentanoate, but in S. meliloti the pathway probably proceeds via 2,4-dioxopentanoate (see SM_b21112).",hypothetical protein,Fumarylacetoacetate hydrolase family protein,,MSGGFLVSFEQALSAESIQPADASSAMLVGRVWSKTAGGPCPVLISEGEVFDLTPLAATISALLEIDGLVDALRDPSRFASLGSLDAFLRGEAGDLLAPADLQAVKAAGVTFADSMLERVIEEQAKGDPLRAQEIRGRLAPVLGDNLKGLVAGSDKAAEVKKLLQELGLWSQYLEVGIGPDAEIFTKAQPMSSVGCGAYIGIHPKSDWNNPEPEVVLAVTSKGKIVGATLGNDVNLRDFEGRSALLLSKAKDNNASCSIGPFIRLFDGAFTIEDVKQAEVSLVVDGKEGFKMTGISPMSAISRSPEDLVSQLLNDNHQYPDGVVFFLGTMFAPVKDRRGTGLGFTHEIGDRVEISTPRLGRLVNWVDHSDRCPKWSFGLGALMKNLAERGLLQAKREG catabolism,Sinorhizobium meliloti 1021,Smeli,SMc00588,SMc00588,SMc00588,NP_385263.1,tr|Q92QY5|Q92QY5_RHIME,L-arabinose 1-dehydrogenase; D-galactose 1-dehydrogenase (EC 1.1.1.46; EC 1.1.1.48),"Important on L-arabinose and D-galactose; may also act on D-glucose, given phenotype with some N sources with glucose as C source; 55% identical to ARAA_AZOBR, which has L-arabinose and D-galactose 1-dehydrogenase activity in vitro",D-galactose 1-dehydrogenase,Uncharacterized oxidoreductase ydgJ (EC 1.-.-.-),D-galactose 1-dehydrogenase,MSPINIAIVGVGKIVRDQHLPALAKNADYRLIAAASRHGTVDGIDNFKSIEAMIDAVPAVEAVSLCMPPQYRYEAARTALAAGKHVFLEKPPGATLSEVADLEALAEEKGVSLFASWHSRYAPAVEAAKTFLASAAIRNVRIIWKEDVRHWHPNQEWIWAAGGLGVFDPGINALSIMTHILPRPVFITSATLEFPENRDAPIAATIAFSDAEKLDVAAEFDWRQTGKQSWDIVAETDAGGMVLSEGGAKLAIDGKIVHEEPEQEYPMLYRRFAEIIKAGRSDVDLAPLRHVADAFMLGRRKFVEAFHD catabolism,Sinorhizobium meliloti 1021,Smeli,SMc00781,SMc00781,SMc00781,NP_384832.1,tr|Q92RW4|Q92RW4_RHIME,malonate-semialdehyde dehydrogenase (acetylating) (EC 1.2.1.18),Specifically important for: m-Inositol; Uridine. 3-oxopropionate or malonate semialdehyde is an intermediate in myo-inositol catabolism and also in pyrimidine catabolism,malonic semialdehyde oxidative decarboxylase,Methylmalonate-semialdehyde dehydrogenase (EC 1.2.1.27),methylmalonate-semialdehyde dehydrogenase,MYELGHFIDGKRVAGTSGRVSNIFNPATGEVQGTVALASDADLAAAVESAKAAQPKWAATNPQRRARVFMKFVQLLNDNMNELAEMLSREHGKTIDDAKGDIVRGLEVCEFVIGIPHLQKSEFTEGAGPGIDMYSIRQPVGIGAGITPFNFPGMIPMWMFAPAIACGNAFILKPSERDPSVPIRLAELMIEAGLPAGILNVVNGDKGAVDAILTHPDIAAVSFVGSTPIARYVYGTAAMNGKRAQCFGGAKNHMIIMPDADLDQAANALIGAGYGSAGERCMAISVAVPVGEETANRLIDKLVPMVESLRIGPYTDEKADMGPVVTKEAEQRIRSLIDSGIEQGAKLVVDGRDFKLQGYENGHFIGGCLFDDVTPDMDIYKTEIFGPVLSVVRARNYEEALSLPMKHEYGNGVAIYTRDGDAARDFASRINIGMVGVNVPIPVPLAYHSFGGWKSSSFGDLNQHGTDSIKFWTRTKTITSRWPSGIKDGAEFSIPTMR catabolism,Sinorhizobium meliloti 1021,Smeli,SMc00832,SMc00832,SMc00832,NP_384883.1,tr|Q92RS3|Q92RS3_RHIME,"D-lactate oxidase, FAD-linked subunit (EC 1.1.3.15)","Specifically important for: Sodium D,L-Lactate. This is the first step in D-lactate oxidation. KEGG has the correct EC # but describes it as glycolate oxidase; SEED has it as 1.1.99.14 (glycolate dehydrogenase)",glycolate oxidase subunit protein,"Glycolate dehydrogenase (EC 1.1.99.14), subunit GlcD",,MPETIGFLKPRQAVLDRRREIVADLADLLPEGGLISDERGLKPFETDAFIAYRRMPLAVVLPETTEHVAAVLKYCSRYGIPIVPRGAGTSLSGGAIPQEDAIVVGLSKMSRTLDIDLFNRTATVQAGVTNLNISDAVSADGFFYAPDPSSQLACTIGGNIGMNSGGAHCLKYGVTTNNLLGVKMVLFDGTVIELGGKALDAPGYDLLGLVCGSEGQLGIVTEATVRLIAKPEGARPVLFGFASSESAGSCVADIIGSGIIPVAIEFMDRPAIEICEAFAQAGYPLDVEALLIVEVEGSEAEMDATLAGIIEIARRHGVMTIRESQSALEAALIWKGRKSAFGATGRIADYICMDGTVPLSQLSHVLRRTGEIVAGYGLRVANVFHAGDGNMHPLILYNINDPEEAARAEAAGNDILKLCVEAGGCLTGEHGVGIEKRDLMLHQYSRADLGQQMAARAAFDPQWLMNPSKVFPLEGRPAA catabolism,Sinorhizobium meliloti 1021,Smeli,SMc00833,SMc00833,SMc00833,NP_384884.1,tr|Q92RS2|Q92RS2_RHIME,"D-lactate oxidase, FAD binding subunit (EC 1.1.3.15)","Specifically important for: Sodium D,L-Lactate. This is the first step in D-lactate oxidation. KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase)",glycolate oxidase subunit protein,"Glycolate dehydrogenase (EC 1.1.99.14), FAD-binding subunit GlcE",glycolate oxidase FAD binding subunit,MIVHFEPASEEGIASVVRSAAAERVTLAVVGGGTRAGLGNPVRADRTLSTRRLSGIVTYDPAEMTMSALAGTPVAEVEAALHAKGQMLSFEPMDHRPIFATTGEPTIGGVFAANVSGPRRYVAGAARDSLLGVRFVNGRGEPIKAGGRVMKNVTGLDLVKLMAGSYGTLGILTEVTFKVLPLPPAAATVVVSGLNDAEAAAVMAEAMAQPVEVSGASHLPESVRSRFLDGALPDGAATVLRLEGLAASVAIRAEKLGEKLSRFGRISQLDEAQTRTLWAEIRDVKPYADGTRRPLWRISVAPSAGHQLVAALRLQTGVDAFYDWQGGLVWLRMEADPEAELLRRYIGAVGGGHAALLRAGEEARGRIPAFEPQPPAVARLSERIRAQFDPSGIFNPGRAAALVRN catabolism,Sinorhizobium meliloti 1021,Smeli,SMc00926,SMc00926,SMc00926,NP_384885.2,tr|Q92RS1|Q92RS1_RHIME,"D-lactate oxidase, iron-sulfur subunit (EC 1.1.3.15)","Specifically important for: Sodium D,L-Lactate. This is the first step in D-lactate oxidation. KEGG oddly has no EC #; SEED has it as 1.1.99.14 (glycolate dehydrogenase)",glycolate oxidase iron-sulfur subunit protein,"Glycolate dehydrogenase (EC 1.1.99.14), iron-sulfur subunit GlcF",glycolate oxidase iron-sulfur subunit,MIQRPQGATWSTRLQTNFSPEQLADPHVAESETILRKCVHCGFCTATCPTYVVLGDELDSPRGRIYLIKDMLENGRAADSETVTHIDRCLSCLSCLTTCPSGVDYMHLVDHARAHIEKTYKRPFKDRLARSVIAATLPYPSRFRLALGAAGLARPLAGLLKRVPFLRTLGVMLDLAPSALPAARGAKPAVYAAKGTPRARVALLTGCAQPVLRPEINDATIRLLTGQGVEVVVSAGEGCCGALVHHMGRDEQALQAGRHNIDVWLKAAEEDGLDAIIITASGCGTTIKDYGHMLRLDPAYAEKAARVSALAKDVTEYLATLDLPEQGARNLTVAYHSACSMQHGQKITSAPKQLLKRAGFSVREPAEGHLCCGSAGTYNILQPEISAKLKARKVRNIEATKPEVIATGNIGCITQIASGTEIPILHTVELLDWAYGGPKPAGL catabolism,Sinorhizobium meliloti 1021,Smeli,SMc01105,SMc01105,SMc01105,NP_384521.1,tr|Q92SI0|Q92SI0_RHIME,uridine nucleosidase (EC 3.2.2.3),Specifically important for: Uridine. annotated by SEED as acting on uridine and inosine,nucleoside hydrolase (SEED_correct),Inosine-uridine preferring nucleoside hydrolase (EC 3.2.2.1),purine nucleosidase,MSSARKIIIDTDPGQDDAAAIMLALGSPEEIEVLGITAVAGNVPLTLTARNARIVCELCNRTDVKIFAGAERPVARPLVTAEHVHGKTGLDGPALDEPTMPLQEQHAVDFIVETLRAEAPGAVTLCTLGPLTNIALALTKAPEIAPSVRELVMMGGGFFEGGNITPAAEFNIYVDPEAAEIVFRSGIPIVMMPLDVTHRVLTRKTRVEKIRAIGSPAAVALAEMLEFFERFDIEKYGTDGGPLHDPTVIAYILRPELFTGRDCNVEIETASPLTTGMTVVDWWQVTGRAHNAKVMRHIDDEGFFELLAERLARI transporters,Sinorhizobium meliloti 1021,Smeli,SMc02118,SMc02118,SMc02118,NP_385584.1,tr|Q92Q71|Q92Q71_RHIME,"ABC transporter for L-Glutamine, L-Histidine, and other L-amino acids, periplasmic substrate-binding component",Specific phenotype on L-Histidine; L-Glutamine.,general L-amino acid-binding periplasmic ABC transporter protein,Glutamate Aspartate periplasmic binding protein precursor GltI (TC 3.A.1.3.4),general L-amino acid transport system substrate-binding protein,MARRILTALVGAAVVGIGTHAASAATLDDVKAKGFVQCGVNTGLAGFAAPDASGNWSGFDVDYCKAIAAAIFGDGSKVKYTPLSAKERFPALQSGEVDVLARNTTWSINRDTALGFNFRPVNYYDGQGFMVRKELDVKSALELSGAAVCVQTGTTTELNLADYFKANNLQYNPVVFEKLEEVNAAYDAGRCDVYTTDQSGLYSLRLTLSKPDDHIVLPEIISKEPLAPAVRQGDDQWFDIVSWVHYALVQAEEFGVTQANLEEMKKSTNPDVQRFLGVEADSKIGTDLGLTNEWAVNIVKAVGNYGEVFDRNIGAGSPLKIERGLNALWNKGGLQYAPPVR transporters,Sinorhizobium meliloti 1021,Smeli,SMc02119,SMc02119,SMc02119,NP_385583.1,tr|Q92Q72|Q92Q72_RHIME,"ABC transporter for L-Glutamine, L-Histidine, and other L-amino acids, permease component 1",Specific phenotypes on L-Glutamine; L-Histidine. Mild phenotypes on proline or lysine (KEGG_correct),general L-amino acid transport permease ABC transporter protein,Glutamate Aspartate transport system permease protein GltJ (TC 3.A.1.3.4),general L-amino acid transport system permease protein,MAIGVTNAPERSRSSGSIINDPQVRGIFYQAITIIILAALIYWIVDNTVDNLRRANIASGYDFVRSRAGFDVGQSLISFTSDSTYGRALLVGFINTLLVAITGIITATIIGFIVGIGRLSHNWIIAKLSLAYVEVFRNIPPLLVIFFWYSGVLSILPQARDALALPFDIFLSNRGVAFPRPIAEEGAEYTLLAFVIAVAASVFFARYARKRQLATGERLPVLWTVLGLIIGLPLVTFLVTGAPITFDIPVAGKFNLTGGSVVGPEFMSLFLALSFYTAAFIAEIVRAGIRGVSKGQTEAAHALGIRPALTTRLVVVPQAMRIIIPPLTSQYLNLTKNSSLAVAIGYADLVAVGGTILNQTGQSIEIVSIWLIVYLSLSLATSLFMNWYNARMALVER transporters,Sinorhizobium meliloti 1021,Smeli,SMc02120,SMc02120,SMc02120,NP_385582.1,tr|Q92Q73|Q92Q73_RHIME,"ABC transporter for L-Glutamine, L-Histidine, and other L-amino acids, permease component 2",Specific phenotypes on L-Glutamine; L-Histidine. Mild phenotypes on proline or lysine (KEGG_correct),general L-amino acid transport permease ABC transporter protein,Glutamate Aspartate transport system permease protein GltK (TC 3.A.1.3.4),general L-amino acid transport system permease protein,MSTNQASFVRASMIEASPAPSLESGAVSWLRKNLFATPKDTALTIISLLILAWLVPPAIQWLFIDAAWSGGGRGVCATLSQGGSQPEGWSGACWAFVNAKFAQFLFGRYPLDERWRPALVGILFVLLLVPMLIPRIPYKGLNALLLLVALPILSAILLPGGWFGLTYVETPLWGGLMVTLVLSFVGIAVSLPLGILLALGRRSNMPVIKMLCTVFIEVIRGVPLITVLFMASVMLPLFLPQGVTFDKFLRALIGVSLFASAYMAEVVRGGLQAIPKGQYEGADSLGLSFWQKMGFIVLPQALKLVIPGIVNTFIGLFKDTSLVSIIGMFDLLGIVRLNFSDTNWATAVTPLTGLIFAGFVFWLFCFGMSRYSGFMERLLDRSQR transporters,Sinorhizobium meliloti 1021,Smeli,SMc02121,SMc02121,SMc02121,NP_385581.1,tr|Q92Q74|Q92Q74_RHIME,"ABC transporter for L-Glutamine, L-Histidine, and other L-amino acids, ATPase component",Very important for histidine and glutamine utilization and also for lysine utilization. It could be the ATPase component for lysine transport by Smc00140:Smc00138 as well (as there is no ATPase component in that gene cluster or other ATPase component identified in the fitness data),general L-amino acid transport ATP-binding ABC transporter protein,"ABC-type polar amino acid transport system, ATPase component",general L-amino acid transport system ATP-binding protein,MANTATAPKLAVSTTDVAIEITNMNKWYGDFHVLRDINLRVMRGERIVVAGPSGSGKSTMIRCINRLEEHQKGKIVVDGIELTNDLKKIDEVRREVGMVFQHFNLFPHLTILENCTLAPIWVRKMPKKEAEQVAMHFLERVKIPEQALKYPGQLSGGQQQRVAIARSLCMRPKILLFDEPTSALDPEMVKEVLDTMVGLAEEGMTMICVTHEMGFARQVANRVIFMDQGQIVEQNSPAEFFDNPQHERTKLFLSQILH catabolism,Sinorhizobium meliloti 1021,Smeli,SMc02321,SMc02321,SMc02321,NP_384733.1,sp|Q92S14|RHAL_RHIME,L-rhamnose isomerase (EC 5.3.1.14),"Specifically important for: L-Rhamnose monohydrate. KEGG now annotates it as L-rhamnose isomerase, which is correct. The gene is cofit with SMc03003 (rhaK), which is probably rhamnulose kinase. It had been suggested that rhaK might be rhamnose kinase and that the isomerase might act on L-rhamnose 1-phosphate, but the S. meliloti rhaK is not a rhamnose kinase. See the PhD thesis of Damien M. R. Rivers (2015). (KEGG_correct)",sugar isomerase,,,MTLMISTSVLDAENASRRDALTRDYESLGDRLARRGIDIDAVKAKVAAYGVAVPSWGVGTGGTRFARFPGPGEPRNIFDKLEDCAVIQQLTRATPAVSLHIPWDKVSDLGALKEKGSALGLSFDAMNSNTFSDAPGQAHSYKFGSLSHTDSATRRQAIEHNLECVEIGKALGSKALTVWVGDGSNFPGQSNFTRAFERYLDSMKAVYAALPDDWRIFTEHKMFEPAFYSTVVQDWGTNYLIAQELGPKAFCLVDLGHHAPNVNIEMIVARLIQFKKLGGFHFNDSKYGDDDLDTGSIDPYRLFLVFNELVDAETRAANGFDPAHMLDQSHNVTDPIESLMTSAMEVGRAYAQALIVDRKALAGYQEENDALMASETLKTAFRTDVEPILATARLENDGAIAPVAAYRASGYRARVAAERPAVAGGGGGIV catabolism,Sinorhizobium meliloti 1021,Smeli,SMc02322,SMc02322,SMc02322,NP_384734.1,tr|Q92S13|Q92S13_RHIME,rhamnulose-1-phosphate aldolase (EC 4.1.2.19) / lactaldehyde dehydrogenase (EC 1.2.1.22),Specifically important for: L-Rhamnose monohydrate. These are the steps after the L-rhamnose 1-phosphate isomerase (SEED_correct),short chain dehydrogenase,Predicted rhamnulose-1-phosphate aldolase (EC 4.1.2.19) / Predicted lactaldehyde dehydrogenase (EC 1.2.1.22),,MLDKHQGARLANLWDDGKAAGMTEPEKLLYRSNLLGSDKRITNYGGGNTSAKVMEKDPLTGEIVEVLWVKGSGGDVGTIKMDGFSTLYMDKLRALKGIYRGVEFEDEMVGYLPHCTFNLNPRAASIDTPLHAYVPKPHVDHMHPDAIIAIAASKNSRELTSKIFGDEIGWLPWKRPGYELGLWLEKFCQENPDARGVVLESHGLFTWGDTAKEAYETTIEIINRAIAWFETENTGPAFGGRSKPVLAAADRAAIAKKLMPVIRGLISAGESKVGHFDDSQAVLDFVTSTSLEPLAALGTSCPDHFLRTKIRPLVVDFDPAQPDVGNTLAGLPEAIATYRADYAAYYERCKRADSPAMRDPNAVVYLVPGVGMITFAKDKATARISAEFYVNAINVMRGASGVSTYVGLPEQEAFDIEYWLLEEAKLQRMPKPKSLAGRIALVTGGAGGIGKATANRLMQEGACVVLADIDETALEAAQTELSTRYGKDFVRSVNMNVTSEAAVESGFGDALLAFGGLDILVSNAGLATSAAVEDTTLALWNKNMDILATGYFLVSREAFRIFRNQKAGGNVVFVASKNGLAASPGASAYCTAKAAEIHLARCLALEGASAQIRVNVVNPDAVLRGSKIWTGEWKEQRAAAYKMDVDELEAHYRERSMLKLSVFPEDIAEAIYFLASDMSAKSTGNIVNVDAGNAQSFTR catabolism,Sinorhizobium meliloti 1021,Smeli,SMc02775,SMc02775,SMc02775,NP_384125.1,tr|Q92TD8|Q92TD8_RHIME,D-arabinose 1-dehydrogenase,"# Specifically important in carbon source D-Arabinose. This is the first step in the oxidation of D-arabinose. (The fucose dehydrogenase is SM_b21109.) The product is probably the 1,5-lactone, not the 1,4-lactone, see SM_b21101.",L-fucose dehydrogenase (D-threo aldose 1-dehydrogenase) protein,L-fuco-beta-pyranose dehydrogenase (EC 1.1.1.122),D-threo-aldose 1-dehydrogenase,MQTRRIGRTALAVTEYSFGTAGLGGLYRECTREAAMATLDAAWEAGIRYFDTAPFYGLGLAERRVGDFLRDKPRDSFVLSTKVGRLLHPVPENQVPDYSYVKPLNFDVTYDYGYDAIMRSVEMSYARLGLNRIDILYVHDIGGYTHGAAKNAVYLRQLLDSGLKALDELKSSGVISAYGLGVNEVPVCLDVMRQADIDCILLAGRYTLLDRSAVAELLPLCAKKDTSLVVGGVFNSGILATGPVEGAHFDYMPATGEVRAKVAAMERIAGERGMPLAAPALQFPLANPHVASVLLGTAKPSSLTRNMELTRYGIAPEDYAAFEPHTLVAPELGPEAVRA catabolism,Sinorhizobium meliloti 1021,Smeli,SMc02776,SMc02776,SMc02776,NP_384124.1,tr|Q92TD9|Q92TD9_RHIME,D-arabinoate dehydratase (EC 4.2.1.5); also involved in L-fucose catabolism,"# Specifically important in carbon source D-Arabinose; carbon source L-Fucose. SMc02776 is 36% identical to D-galactarate dehydratase (garD) from E. coli, which is a similar reaction. The product (2-dehydro-3-deoxy-D-arabinonate) is consumed by SMa0247. The role of SMc02776 role in L-fucose catabolism is unclear because two other dehydratases (SM_b21107 and SM_b21110) are also required but the pathway requires only one dehydratase reaction (PMID:17144652). Probably two of these dehydratases act together (with one of them in reverse) to switch the stereochemistry of a chiral center.",altronate hydrolase,,altronate hydrolase,MPAPSSILLSPDDNVVVATAAIAPGDRLAGGVSAVARIEPGHKAAIRRIDVGEPVVKYGQAIGRATSPIAPGEHVHSHNLAFDQGRLAVGAAVPPEAASEADKARTFLGYRRADGRAATRNYIGIVASVNCSTTVCRAIADEANRRILPKYEGIDGFVPIVHDQGCGMSSTGDGMKNLHRTLAGYARHANFGGVLMVGLGCEVNQLTLYGQSGAGAEKRHFNIQEAGGSRRSVERALGILDEIAKEVAAARRVPIPVSEIIVGLQCGGSDGLSGITANPALGAAVDILAAAGGTAILSETSEIYGAEHLLRSRAVNETVAVKLDGLIAWWEDYVAMHGASLDNNPSPGNKRGGLTTILEKSLGAVAKGGRSPLTAVYNYAERVTEPGLVFMDTPGYDPVSATGQVAGGANVIAFTTGRGSCFGCRPAPSIKLTSNTALYRAMEEDMDIDCGVIASGETTIADLGRGIFELIIETASGRKTKSELFGYGDNEFVPWHLGATL transporters,Sinorhizobium meliloti 1021,Smeli,SMc02869,SMc02869,SMc02869,NP_384300.2,tr|Q92T01|Q92T01_RHIME,"N-Acetyl-D-glucosamine ABC transport system, ATPase component",Specific phenotype on N-Acetyl-D-Glucosamine. (SEED_correct),ABC transporter ATP-binding protein,N-Acetyl-D-glucosamine ABC transport system ATP-binding protein,multiple sugar transport system ATP-binding protein,MCAPASRSSFNPRGRHVGSLQLKTIRKAFGSHEVLKGIDLDVKDGEFVIFVGPSGCGKSTLLRTIAGLEDATSGSVQIDGVEVGHVAPAKRGIAMVFQSYALYPHLTVKDNMGLGLKQAGVPKAEIEEKVAKAAGMLSLEPYLARRPAELSGGQRQRVAIGRAIVREPKLFLFDEPLSNLDAALRVNTRLEIARLHRSLKATMIYVTHDQVEAMTLADKIVVLNAGRIEQVGSPMELYNRPANLFVAGFIGSPQMNFIEAAKLGDGEAKTIGIRPEHIGLSRESGDWKGKVIHVEHLGADTIIYIESETVGLLTVRLFGEHRYATDDIVHATPVIGSMHRFDADGRVIKSGQ transporters,Sinorhizobium meliloti 1021,Smeli,SMc02871,SMc02871,SMc02871,NP_384302.1,tr|Q92SZ9|Q92SZ9_RHIME,"ABC transporter for N-Acetyl-D-glucosamine, permease protein 2",Specific phenotypes on N-Acetyl-D-Glucosamine; N-Acetyl-D-Glucosamine. (SEED_correct),ABC transporter permease,"N-Acetyl-D-glucosamine ABC transport system, permease protein 2",multiple sugar transport system permease protein,MSKARASFMRTGAVHAALIAYTLIALFPVFLTIVNSFKSRNAIFREPLAVPTPETFSLIGYETVLKQGDFIGYFQNSIIVTVVSIALVLLFGAMAAFALSEYRFRGNTLMGLYLALGIMIPIRLGTVAILQGMVATGLVNTLTALILVYTAQGLPLAVFILSEFMRTVSDDLKNAGRIDGLSEYAIFLRLVLPLIRPAMATVAVFTMIPIWNDLWFPLILAPAEATKTVTLGSQIFIGQFVTNWNAVLSALSLAIFPVLVLYVIFSRQLIRGITAGAVK transporters,Sinorhizobium meliloti 1021,Smeli,SMc02872,SMc02872,SMc02872,NP_384303.1,tr|Q92SZ8|Q92SZ8_RHIME,"ABC transporter for N-Acetyl-D-glucosamine, permease protein 1",Specific phenotypes on N-Acetyl-D-Glucosamine; N-Acetyl-D-Glucosamine. (SEED_correct),ABC transporter permease,"N-Acetyl-D-glucosamine ABC transport system, permease protein 1",multiple sugar transport system permease protein,MNPREITTYVTEIKRPRRWHILVFLLPALVVYTAVMILPLFETLRQSFYNTVDGQLTFVGLGNFKVLFGDPRWAADFWNALKNNFVFFLIHMAVQNPIGIALAAMLSVPKLRFGAFYRTAIFLPTLLSFVIVGFIWKLILSPIWGVAPYLLDTVGLRSLFGPWLGKPDTALIAVSLISVWQYIGIPMMLIYAALLNIPDEVTEAAELDGVTGWSQFWKIKLPLILPAIGIVSILTFVGNFNAFDLIYTVQGALAGPDKSTDILGTLLYRTFFGFQLQLGDRSMGATIAAIMFLIILAGVALYLFGIQRRMRRYQF transporters,Sinorhizobium meliloti 1021,Smeli,SMc02873,SMc02873,SMc02873,NP_384304.1,tr|Q92SZ7|Q92SZ7_RHIME,"N-Acetyl-D-glucosamine ABC transport system, periplasmic substrate-binding component",Specific phenotype on N-Acetyl-D-Glucosamine. (SEED_correct),periplasmic binding (signal peptide) ABC transporter protein,"N-Acetyl-D-glucosamine ABC transport system, sugar-binding protein",multiple sugar transport system substrate-binding protein,MTRTTMKGLLLASSILGSAGLAQAQDATLTIESWRNDDLAIWQEKLIPAFEAKNPGIKVVFAPSAPTEYNAALNAKLDAGSAGDLITCRPFDASLELYNKKHLADLTGLSGMENFSDVAKSAWTTDDGKATFCVPMASVIHGFIYNKDAFDQLGLSVPATEEEFFAVLEKIKADGNYIPMAMGTKDLWEAATMGYQNIGPNYWKGEEGRLALLKGEQKLTDEPWVEPFRVLAKWKDYLGDGFEAQTYPDSQNLFTLGRAAIYPAGSWEISGFNTQAEFKMGAFPPPVKKAGDTCYISDHNDIGIGLNAKSKNADAAKTFLTWVASPEFAEIYANALPGFFSLNSTAVKMSDPLAQEFVSWREKCKPTIRSTYQILSRGTPNLENETWVMSANVINGTDTPEAAAKKLQDGLDSWFKPVK catabolism,Sinorhizobium meliloti 1021,Smeli,SMc02875,SMc02875,SMc02875,NP_384306.1,tr|Q92SZ5|Q92SZ5_RHIME,N-acetylglucosamine kinase (EC 2.7.1.59),Specifically important for: N-Acetyl-D-Glucosamine. The first step in NAG catabolism (SEED_correct),hypothetical protein,N-acetylglucosamine kinase of eukaryotic type (EC 2.7.1.59),,MTFYLIGIDGGGTSCRAAVAALDGRILGRGKAGAANILTDPETALQNITDAARDAFGDAGLDPAGIGASRAIVGVAGHNVGDAVHYVKRRLPFAQADIESDGLIALQGALGDGDGAVAILGTGTIYIARRGDEVSYVGGWGFTIGDHGSGARIGHALLQESLLAYDGIHQGSGVTDAVLAEFNDDPRDIVDFARLAKPGEFGRYAPRVFEFAERGDPVAISLLKAAAATVDEALDVVVSRGSEKLCLLGGLAPLYRRWLADRHQPRFVEARADALTGAVALAAARFGSHSGVSA catabolism,Sinorhizobium meliloti 1021,Smeli,SMc03003,SMc03003,SMc03003,NP_384741.1,tr|Q92S06|Q92S06_RHIME,Rhamnulokinase RhaK in alpha-proteobacteria (EC 2.7.1.5),Specifically important for: L-Rhamnose monohydrate. Also see comment on SMc02321; this is the first step in L-rhamnose degradation. (SEED_correct),hypothetical protein,Rhamnulokinase RhaK in alpha-proteobacteria (EC 2.7.1.5),,MMMKTVAVIDIGKTNAKVALVDLERFEEIAVRKSGNGVSDDGPYPHFDTERLWRFVLDSLAALHREHPVDAISVTTHGATAVLLDEAGELALPVLDYEFTGPDALAEEYEKARPPFTETGSARLPMGLNVGAQLFWQQRMFPEHFARVATILTYAQYWSYRLTGVRTNELTSLGCHTDLWNPKAATFSSMVDEQGWRHLFAPVRRAGDVLGPLLRQVADETGLPPETPVHCGIHDSNASLLPHLITRQAPFSVASTGTWVVMLAVGAEPVKLDERRDTLINVNALGDPVPSARFMGGRAYSLLIGDDPPTASPEAEASVLEQGHMLLPSLPGGSGPFPAARPHWTADEDKLAPAERLAVVSFHLALMTATCLDLIGARGEILVEGPFALNDAYLRMLAAATGRPVLANRLNSTGTSLGAACLVAGARVRTGAEAIPAAKPDAYAAYAEAWRRRTAAHVRSDP transporters,Sinorhizobium meliloti 1021,Smeli,SMc03061,SMc03061,SMc03061,NP_384801.1,sp|Q9Z3R5|AGLE_RHIME,"ABC transporter for D-Maltose and D-Trehalose, ATPase component","Specific phenotype on D-Maltose monohydrate; D-Trehalose dihydrate. Also mildly important for D-cellobiose utilization, which has its own ABC transporter, so the cause is unclear. (SEED_correct)",alpha-glucoside ABC transporter periplasmic-binding protein,Alpha-glucosides-binding periplasmic protein AglE precursor,alpha-glucoside transport system substrate-binding protein,MKRSLLIGVAAFALLAGTAGLAGTAGAADLKFKPGEDSRFNWASLEEFKKGHDLKGQTLTIFGPWRGEDEALFKSVYAYFVEATGVELKYSSSENYEQQIVIDTQAGSPPDVAILPQPGLIADLAAKGLLTPLGDETKQWLLDNYAAGQSWVDLSTYNGKDGTSALYAFPYKIDVKSLVWYVPENFEDAGYEVPKTMEELKALTEKIAEDGEKPWCIGLGSGGATGWPATDWVEDLMLRTQPAETYDKWVKNEIPFTDAAVTGALEEFGWFARNDAFVDGGAAAVASTDFRDSPKGLFSSPPKCYLHHQASFIPSFFPEGKVVGEDADFFYMPPYESKKELGNPVLGAGTLAMITKDTPAARAFIEFLKTPIAHEVWMAQTSFLTPYKSVNVDVYGNPPLKKQGEILLNATTFRFDGSDLMPGKIGAGAFWTGMVDFVGGKSSADVAAGVQKAWDSIK transporters,Sinorhizobium meliloti 1021,Smeli,SMc03062,SMc03062,SMc03062,NP_384802.1,sp|Q9Z3R6|AGLF_RHIME,"ABC transporter for D-Maltose and D-Trehalose, permease component 1",Specific phenotypes on D-Maltose monohydrate; D-Trehalose dihydrate. Also possibly important for cellobiose (but that has its own transporter) or raffinose; note cellobiose and maltose are beta glucosides not alpha glucosides permease protein,alpha-glucoside ABC transporter permease,"ABC alpha-glucoside transporter, inner membrane subunit AglF",alpha-glucoside transport system permease protein,MEQLIAAILTMVAGVLVCAAYFWSTNLVLDWIFPSKGKFGAVASRNLRIANSIRPWLFLAPALLALTLYLVYPVVQSVWLSLHGRGGQNFVGLSNYSWMINDGEFRQSIFNNFLWLLVVPALSTFFGLIIAALTDRIWWGNIAKTLIFMPMAISFVGAAVIWKFIYDYRAAGSEQIGLLNAIVVALGGEPQAWITLPFWNNFFLMVILIWIQTGFAMVILSAALRGIPEETIEAAVIDGANGWQIFFKIMVPQIWGTIAVVWTTITILVLKVFDIVLAMTNGQWQSQVLANLMFDWMFRGGGDFGRGAAIAVVIMILVVPIMIWNIRNATRESGGH transporters,Sinorhizobium meliloti 1021,Smeli,SMc03063,SMc03063,SMc03063,NP_384803.1,sp|Q9Z3R7|AGLG_RHIME,"ABC transporter for D-Maltose and D-Trehalose, permease component 2",Specific phenotypes on D-Maltose monohydrate; D-Trehalose dihydrate. Also possibly important for cellobiose (but that has its own transporter) or raffinose; note cellobiose and maltose are beta glucosides not alpha glucosides permease protein,alpha-glucoside ABC transporter permease,Alpha-glucoside transport system permease protein AglG,alpha-glucoside transport system permease protein,MNPSRRSPLTWAVHLSVLLLVLLWTLPTAGLLISSLRDKDQLAVSGWWTALSSSSRNAVVRAPSAEDQVERDGKFVISGNLLEGRGEVSAFGFSSREPTKFKPGETAELNDGERLTVQSDGSFEIVSDQRMEGSRGQRIFFTATTPPRFTLDNYAEVLSAAGIGRSFLNSLTVAVPSTVIPILIAAFAAYALAWMPFPGRAVLLAVVVGLLVVPLQMSLIPLLQLYNGVGAFFGVSAKTYMGIWLAHTGFGLPLAIYLLRNYMAGLPREIMESARVDGASDFDIFVKIILPLSFPALASFAIFQFLWTWNDLLVAIVFLGAGDDKLVLTGRLVNLLGSRGGNWEILTASAFITIVVPLIVFFALQRYLVRGLLAGSVKGG transporters,Sinorhizobium meliloti 1021,Smeli,SMc03065,SMc03065,SMc03065,NP_384805.1,sp|Q9Z3R9|AGLK_RHIME,"ABC transporter for D-Maltose and D-Trehalose, ATPase component","Specific phenotype on D-Trehalose dihydrate; D-Cellobiose; D-Maltose monohydrate. Mildly important for D-cellobiose utilization, which has its own ABC transporter, so the cause is unclear. (SEED_correct)",alpha-glucoside ABC transporter ATP-binding protein,Alpha-glucoside transport ATP-binding protein AglK,alpha-glucoside transport system ATP-binding protein,MTGLLLKDIRKSYGAVDVIHGIDLDIKEGEFVVFVGPSGCGKSTLLRMIAGLEEITGGDMFIDGERVNDVPPSKRGIAMVFQSYALYPHMTVYDNMAFGMRIARESKEEIDRRVRGAADMLQLTPYLDRLPKALSGGQRQRVAIGRAICRNPKVFLFDEPLSNLDAALRVATRIEIAKLSERMSDTTMIYVTHDQVEAMTLADRIVVLSAGHIEQVGAPLELYERPANLFVARFIGSPAMNVIPATITATGQQTAVSLAGGKSVTLDVPTNASENGKTASFGVRPEDLRVTEADDFLFEGTVSIVEALGEVTLLYIEGLVENEPIIAKMPGIARVGRGDKVRFTADKAKLHLFDTNGQSYRA catabolism,Sinorhizobium meliloti 1021,Smeli,SMc03162,SMc03162,SMc03162,NP_386999.1,tr|Q92LX0|Q92LX0_RHIME,epimerase involved in beta-glucoside degradation,"Specifically important for: D-Salicin; D-Cellobiose. The SEED prediction (for a step in myo-inositol degradation) is not consistent with the phenotypes on D-salicin and D-cellobiose (which are both beta-glucosides). Because it is cofit with a beta-mannosidase (SMc04255), we suspect that it converts beta-glucosides to beta-mannosides. However this pathway also seems to involve two oxidoreductases (SMc03161, SMc04253) so its exact role is uncertain.",hypothetical protein,Inosose isomerase (EC 5.3.99.-),,MKTIKGPGLFLAQFAGDAAPFNSWDSITKWAADCGYKGVQVPSWDGRLFDLKKAAESKTYCDEVAGKARDNGVEITELATHLQGQLVAAHPAYDEAFDGFAVPQVRGNPRARQEWAVEQVKLALTASKNLGLGAMASFSGALAWPFVYPWPQRPAGLVETAFDELARRWKPILDHAEDCGVDVCYEIHPGEDLHDGITYEMFLERTGNHARACMLYDPSHYVLQCLDYLENIDIYKDRIRMFHVKDAEFNPTGRQGVYGGYQSWVNRAGRFRSLGDGQVDFGAVFSKMAANDFAGWAVVEWECCLKHPEDGAREGSEFVKAHIIRVTEKAFDDFADSGTDEAANRRMLGI catabolism,Sinorhizobium meliloti 1021,Smeli,SMc03165,SMc03165,SMc03165,NP_387002.1,tr|Q92LW7|Q92LW7_RHIME,Novel Xylose regulator from LacI family,"# Specifically important in carbon source D-Xylose. Also see expression evidence for the putative ortholog from Phaeobacter, PGA1_c13990, which is 41% identical (PMC4148808). (SEED_correct)",transcriptional regulator,Novel Xylose regulator from LacI family,LacI family transcriptional regulator,MRPTVHDIAAEAGVSLATVDRVLNNRPGVRSVTRGKVERAIATLGYVRDVAAANLAKSRSYPLIFILPAGENSFMRGLEAEVRSAMSRSATERLDITILSVPVFDAPALAAALHDARERRPAGVAVVAVDAPEVTEAVKRLREDGIAVVTLVSDLPGSGRDHFAGVDNVAAGRTAGSLMGRFLGGGEGPVAVLAGSMLVRDHRDRLEGFQAVMSEDFAWRRILPVIEGQDNPSLVETLVGALLGQHPDLAGIYSLGAGNRGLVAALEKAGKGRAVCTIAHELTPHSRAGLLSGTIDALLNQNAGHEVRSAIRVLKAKADGLPVIAAQERIRIDIFLKDNLPLEQE transporters,Sinorhizobium meliloti 1021,Smeli,SMc04256,SMc04256,SMc04256,NP_386032.1,tr|Q92P67|Q92P67_RHIME,"ABC transporter for D-Cellobiose and D-Salicin, ATPase component",Specific phenotype on D-Salicin; D-Cellobiose.,ABC transporter ATP-binding protein,"Nucleoside ABC transporter, permease protein 1",multiple sugar transport system ATP-binding protein,MTSVSVRDLSLNFGAVTVLDRLNLDIDHGEFLVLLGSSGCGKSTLLNCIAGLLDVSDGQIFIKDRNVTWEEPKDRGIGMVFQSYALYPQMTVEKNLSFGLKVAKIPPAEIEKRVKRASEILQIQPLLKRKPSELSGGQRQRVAIGRALVRDVDVFLFDEPLSNLDAKLRSELRVEIKRLHQSLKNTMIYVTHDQIEALTLADRIAVMKSGVIQQLADPMTIYNAPENLFVAGFIGSPSMNFFRGEVEPKDGRSFVRAGGIAFDVTAYPAHTRLQPGQKVVLGLRPEHVKVDEARDGEPTHQAVVDIEEPMGADNLLWLTFAGQSMSVRIAGQRRYPPGSTVRLSFDMGVASIFDAESENRL transporters,Sinorhizobium meliloti 1021,Smeli,SMc04257,SMc04257,SMc04257,NP_386033.2,tr|Q92P66|Q92P66_RHIME,"ABC transporter for D-Cellobiose and D-Salicin, permease component 1",Specific phenotypes on D-Cellobiose; D-Salicin.,ABC transporter permease,"ABC-type sugar transport system, permease component",multiple sugar transport system permease protein,MAAAYETAPGGPMAGPRGRKPRRTLSRRNIIVYGTLIVVALYYLLPLYVMIVTSLKGMPEIRVGNIFAPPLEITFEPWVKAWAEACTGLNCDGLSRGFWNSVRITVPSVIISIAIASVNGYALANWRFKGADLFFTILIVGAFIPYQVMIYPIVIVLREMGVYGTLTGLIIVHTIFGMPILTLLFRNYFAGLPEELFKAARVDGAGFWTIYFKIMLPMSLPIFVVAMILQVTGIWNDFLFGVVFTRPEYYPMTVQLNNIVNSVQGVKEYNVNMAATILTGLVPLTVYFVSGRLFVRGIAAGAVKG transporters,Sinorhizobium meliloti 1021,Smeli,SMc04258,SMc04258,SMc04258,NP_386034.1,tr|Q92P65|Q92P65_RHIME,"ABC transporter for D-Cellobiose and D-Salicin, permease component 2",Specific phenotypes on D-Cellobiose; D-Salicin.,ABC transporter permease,"ABC transporter, permease protein",multiple sugar transport system permease protein,MTGLTRGSARPNQWLRNLNAKIASIPMILTAMVIFVGGTAWTVVYSFTNSKLLPRLAFVGFDQYERLWAAPRWLVSIQNLAVFGCLSLVFSLVIGFVLAALMDQKIRFENTFRTIMLYPFALSFIVTGLVWQWLLNPQYGIQSIVRSLGWTSFSFDPLYNSNIVIYGILIAALWQGTGLVMCLMLAGLRGIDEDIWKAARVDGIPMWKTYVLIIIPMMRGVFITTLVIIASGIVKVYDLVVAQTSGGPGIASEVPAKYVYDYMFQAQNLGQGFAASTMMLVTVAIIIVPWAYLEFGGGRKRG transporters,Sinorhizobium meliloti 1021,Smeli,SMc04259,SMc04259,SMc04259,NP_386035.1,tr|Q92P64|Q92P64_RHIME,"ABC transporter for D-Cellobiose and D-Salicin, periplasmic substrate-binding protein",Specific phenotype on D-Salicin; D-Cellobiose.,periplasmic binding ABC transporter signal peptide protein,"ABC-type sugar transport system, periplasmic component",multiple sugar transport system substrate-binding protein,MNLRFLAAALGATAALPFGAASATDLEVTHWWTSGGEAAAVAELAKAFDATGNKWVDGAIAGSGGTARPIMISRITGGDPMAATQFNHGRQAEELVQAGLMRDLTDIATKENWKEIVKPSSLLDSCTIEGKIYCAPVNIHSWQWLWLSNAAFKQAGVEVPKNWDEFVAAAPALEKAGIVPLAVGGQPWQANGAFDVLMVAIAGKENFEKVFAQKDEEVAAGPEIAKVFKAADDARRMSKGTNVQDWNQATNMVITGKAGGQIMGDWAQGEFQLAGQKAGVDYTCLPGLGVNEVISTGGDAFYFPLLEDEEKSKAQEVLASTLLKPETQVAFNLKKGSLPVRGDVDLAAANDCMKKGLDILAKGNVIQGTDQLLSADSQKQKEDLFSEFFANHSMTPEDAQKRFADIIAAAD catabolism,Sinorhizobium meliloti 1021,Smeli,SMc04386,SMc04386,SMc04386,NP_387397.1,sp|P58350|AATB_RHIME,2-aminoadipate:2-oxoglutarate aminotransferase (EC 2.6.1.39),Specifically important for: L-Lysine. This is required for catabolism of lysine on the way to glutarate. The amino group donor is uncertain,aspartate aminotransferase B protein,Aspartate aminotransferase (EC 2.6.1.1),aspartate aminotransferase,MTINATVKEAGFQPASRISSIGVSEILKIGARAAAMKREGKPVIILGAGEPDFDTPEHVKQAASDAIHRGETKYTALDGTPELKKAIREKFQRENGLAYELDEITVATGAKQILFNAMMASLDPGDEVIIPTPYWTSYSDIVHICEGKPVLIACDASSGFRLTAEKLEAAITPRTRWVLLNSPSNPSGAAYSAADYRPLLEVLLRHPHVWLLVDDMYEHIVYDGFRFVTPAQLEPGLKNRTLTVNGVSKAYAMTGWRIGYAGGPRELIKAMAVVQSQATSCPSSISQAASVAALNGPQDFLKERTESFQRRRDLVVNGLNAIDGLDCRVPEGAFYTFSGCAGVLGKVTPSGKRIKTDTDFCAYLLEDAHVAVVPGSAFGLSPFFRISYATSEAELKEALERIAAACDRLS transporters,Pseudomonas simiae WCS417,WCS417,GFF1004,PS417_05090,PS417_RS22700,WP_029529237.1,UPI000496EDA7,ferric citrate outer membrane transporter (fecA),Specific phenotype on citrate; 63% identical to E. coli fecA; SEED_correct,transporter,Iron(III) dicitrate transport protein FecA @ Iron siderophore receptor protein,iron complex outermembrane recepter protein,MPQQPTLLARTLRQLLLGASLSLTALPPVMAADAKPYHIAPTSLEAALNQFGREAGVLISFGSEVTAGMQSRGLSGNYGAADGLQKLLEGTGLQARAEGDNAYSLQPATAPASIELGTSSVVGDWLGDAAQTHVFEHPGARDVIRREEFERQGATQAKDVLNRIPGVNAPDNNGTGSHDMALNFGIRGLNPRLASRSTVLMDGIPVPFAPYGQPQLSFAPISMGNMDAVDVVRGGGAVRYGPQNVGGVVNFVTRAIPDAPTVKGGLQTETSPSSSHDGFKTTGNLLAGGTADNGLGGAILYSGTRGGDWRENSNTRIDDLILKGKYQLDDANSFNAMAQYYEGQADMPGGLNVADYKADPYQSTRPYDKFWGRRTMFNVGYRYEQDRREFTVNSFFTKTLRSGYLDQGTFLSLSPREYWVRGIETRFAQGFDLGPTSHEVGVGYRYINEAGHELRYRTPIAANQQIPTTNSRNDRDTRGGTEANAFFVDDRIDIGKWTITPGIRYEIIESQQTNNLTNVKYKGDYNTALPALNVLYHLTDNWNLYANTEGSFGSVQYSQMPNRVTSGEVKPEKARTWELGTRYDDGTLRAEIGAFLINFDNQYESNQTNDSVIARGETRHQGIETSINYALDGLSPALAGFDVYATYAYVDATIREDGPNKGNRVPFSSKHKGTLGVGYTEGRWKLNLDSSYQSSQFADNANTEKETANGANGRIPGYMLFSSRAAYDFGPQLSDLNVAVGVKNIFNTQYFTRSFDDNNKGKYVGEPRTVYVQTSIAF transporters,Pseudomonas simiae WCS417,WCS417,GFF1065,PS417_05405,PS417_RS22395,WP_010212811.1,UPI000299F6F4,L-alanine and D-alanine permease,"Important for utilizing both isomers of alanine, and no other transporter is apparent. Both phenotypes are conserved.",D-alanine/D-serine/glycine permease,D-serine/D-alanine/glycine transporter,"amino acid transporter, AAT family",MPVGNHLPHGETAQGGPLKRELGERHIRLMALGACIGVGLFLGSAKAIEMAGPAIMLSYIIGGLAILVIMRALGEMAVHNPVAGSFSRYAQDYLGPLAGFLTGWNYWFLWLVTCVAEITAVAVYMGVWFPDTPRWIWALAALISMGSINLIAVKAFGEFEFWFALIKIVTIIAMVIGGVGIIAFGFGNDGVALGISNLWAHGGFMPNGVQGVLMSLQMVMFAYLGVEMIGLTAGEAKNPQKTIPSAIGSVFWRILLFYVGALFVILSIYPWNEIGTQGSPFVMTFERLGIKTAAGIINFVVITAALSSCNGGIFSTGRMLYSLAQNGQAPATFAKTSSNGVPRKALLLSIFALLLGVLLNYLVPEKVFVWVTSIATFGAIWTWLMILLAQLKFRKSLSPAEQAGLKYRMWLYPVSSYLALAFLLLVVGLMAYFPDTRIALYVGPVFLVLLTVLFYVFKLQPTQVAQGEARPV transporters,Pseudomonas simiae WCS417,WCS417,GFF1579,PS417_08035,PS417_RS19860,WP_010211991.1,UPI00029A6093,adenine transporter (MFS superfamily),"specific phenotype on adenine and no other transporter was identified by the fitness data. Also mildly important on adenosine, which could reflect extracellular conversion to adenine",transporter,Putative transport protein,,MKNSFGFTWYLAGLSMLGYLAMDMYLPAFGAMGEQLQIGAGAVGASLSIFLAGFAVGQLLWGPLSDRLGRKPILLAGLSLFVLGCAGMFWVETAPQLLALRFIQAIGVCSAAVSWQALVIDRYPADKAHRVFASIMPLMSLSPALAPLLGAMVLNHFGWQAIFGVLLGVSVLLLLPTVFLRGMPKRPTEGREPVRLGYGQLLTSRVFTGNVMIFAACSASFFAWLTASPFILGGMGYSPNDIGLSYVLPTLAFLVGGYSCRSALQRFQGKTLLPWLLLAYCISMVALYLVATLTVPTLTTLLIPFCLMALCNGASYPIVVANALMPFSENSGKAAALQNTLQLGLCFLSSLVVSSMLDQPLLITAIVMLATAPLAVLGYWLARPKADNSVLAPNLK catabolism,Pseudomonas simiae WCS417,WCS417,GFF2142,PS417_10925,PS417_RS17080,WP_038453112.1,UPI00049A49C0,malonate-semialdehyde dehydrogenase (acetylating) (EC 1.2.1.18),"Specifically important for: m-Inositol. 3-oxopropionate or malonate semialdehyde is an intermediate in myo-inositol catabolism (SEED_correct, although the name and EC number are confusing)",methylmalonate-semialdehyde dehydrogenase,Methylmalonate-semialdehyde dehydrogenase [inositol] (EC 1.2.1.27),methylmalonate-semialdehyde dehydrogenase,MTTTIEHYINDQRVSRDDRYQDVFNPATGEKTGRVALASRQTVSEAVAAAQAAFAGWADTPPIRRARVLFEYLHLLRERKDDLARIIVAEHGKVFTDAQGEVDRGIDILEFACGIPNLLKGEHSDQVSRGMDNWTMRQPLGVVAGVTPFNFPVMVPMWMYPIAIAAGNTFILKPSPTDPSASLFMAELLREAGLPKGVFNVVQGDKESVDALIEHPDVKAVSFVGSTPIAQYIYETGARNGKRVQGLGGAKNHMVVMPDADIEKTVDALMGAAYGSAGERCMAISVAVLVGDVGDKVIAALTERAKHLRITDGRDLKAEMGPIVSRAALERISGYIEQGVQAGAQLLLDGRDYVPTEPGLENGFWLGATLFDHVTEEMSIYREEIFGPVLACVRVNDFAEAIKLVNAHEFGNGVSCFTRDGNIAREFARRIEVGMVGINVPISVPMAWHGFGGWKKSLFGDMHAYGTEGVRFYTKQKSIMQRWSESIEQGAEFAMPVSK catabolism,Pseudomonas simiae WCS417,WCS417,GFF2156,PS417_11000,PS417_RS17010,WP_010211220.1,UPI00029AE918,L-arabonate dehydratase (EC 4.2.1.25),Specifically important for: L-Arabinose. L-arabonate is an intermediate in the oxidation of L-arabinose (SEED_correct),dihydroxy-acid dehydratase,L-arabonate dehydratase (EC 4.2.1.25),dihydroxy-acid dehydratase,MPDKKPGLRSAQWFGTADKNGFMYRSWMKNQGIADHQFHGKPIIGICNTWSELTPCNAHFRQIAEHVKRGVIEAGGFPVEFPVFSNGESNLRPTAMLTRNLASMDVEEAIRGNPIDGVVLLTGCDKTTPALLMGAASCDVPAIVVTGGPMLNGKHKGQDIGSGTVVWQLSEQVKAGTITLDDFLAAEGGMSRSAGTCNTMGTASTMACMAEALGTSLPHNAAIPAVDARRYVLAHMSGMRAVEMVREDLKLSKILTKEAFENAIRVNAAIGGSTNAVIHLKAIAGRIGVELDLDDWTRIGRGMPTIVDLQPSGRFLMEEFYYAGGLPAVLRRLGEANLIPHPNALTVNGKSLGENTQDSPIYGQDEVIRTLDNPIRADGGICVLRGNLAPLGAVLKPSAASPALMQHRGRAVVFENFDMYKARINDPELDVDANSILVMKNCGPKGYPGMAEVGNMGLPAKLLAQGVTDMVRISDARMSGTAYGTVVLHVAPEAAAGGPLATVKEGDWIELDCANGRLHLDIPDAELAARMADLAPPQKLIVGGYRQLYIDHVLQADQGCDFDFLVGCRGAEVPRHSH catabolism,Pseudomonas simiae WCS417,WCS417,GFF2159,PS417_11015,PS417_RS16995,WP_010211217.1,UPI00029B5717,Ketoglutarate semialdehyde dehydrogenase (EC 1.2.1.26),Specifically important for: L-Arabinose. alpha-ketoglutarate semialdehyde is an intermediate in the oxidation of L-arabinose (SEED_correct),ketoglutarate semialdehyde dehydrogenase,Ketoglutarate semialdehyde dehydrogenase (EC 1.2.1.26),NADP-dependent aldehyde dehydrogenase,MTSFLGHNYIGGQRSANGSVTLQSVDATSGEALPQHFYQATPQEVDAAAKAAAQAYPAYRALSAARRAQFLDAVADELDALGDEFVELVCRETALPAARIKGERGRTSGQMRLFATVLRRGDFYGARIDKALPDRQPMPRPDLRQYRIGLGPVAVFGASNFPLAFSTAGGDTAAALAAGCPVVFKAHSGHMATAERVADAIIRAAEATEMPAGVFNMIFGGGVGEALVKHPAIQAVGFTGSLKGGRALCDMAAARPQPIPVFAEMSSINPVIVLPQALKARAESVARDLTASVVQGCGQFCTNPGLVIGVASPEFTAFTQQVAQLIGDQPAQTMLNAGTLSSYGKGLEKLLAHPGIQHLAGSQQAGNQAQPQLFKADVRLLIDGDEVLQEEVFGPTTVFVEVADQAQLSAALHGLHGQLTATIIGEPADLQQFAELTPLLEQKVGRILLNGYPTGVEVCDSMVHGGPYPATSDARGTSVGTLAIDRFLRPVCFQNYPDSLLPDALKNANPLRIQRLVDGTPSRDPL catabolism,Pseudomonas simiae WCS417,WCS417,GFF2160,PS417_11020,PS417_RS16990,WP_010211216.1,UPI00029AF246,L-arabinolactonase (EC 3.1.1.15),Specifically important for: L-Arabinose. a step in L-arabinose oxidation (SEED_correct),gluconolaconase,L-arabinolactonase (EC 3.1.1.15),,MSCIAVTPHRAQLGEGPFWDAPTQALYWVNIAGKQALRLMGGQLQVWQLPEHVSAFIPCESGDALVTLSSGVYRLDLATEALTLLCVADPQPGNRGNEARCDASGRLWLGTMQNNIGEQGEDLPITRRSGGLFRIDADAQVTPLLSGLGIPNTLLWSDDGRHVHFGDSLDGTLYRHAIQPDGQLDPAQTWFGPHERGGPDGSAMDVDGYIWNARWDGSCLLRLTPDGEVDRIVELPVSRPTSCVLGGPNLTTLYITSAASPLDHPLDGAVLAMEVDVPGKPCHRFAG catabolism,Pseudomonas simiae WCS417,WCS417,GFF2259,PS417_11520,PS417_RS16495,WP_010211099.1,UPI000299D29F,Sorbitol dehydrogenase (EC 1.1.1.14),Specifically important for: D-Sorbitol. The first step in sorbitol degradation (SEED_correct),sorbitol dehydrogenase,Sorbitol dehydrogenase (EC 1.1.1.14),,MKRLEGKSALITGSARGIGRAFAQAYIAEGATVAIADIDLQRAQATAAELGPQAYAVAMDVTDQASIDGAITAVVAQAGKLDILINNAALFDLAPIVDITRDSYDRLFSINVAGTLFTLQAAARQMIRQGHGGKIINMASQAGRRGEPLVAIYCATKAAVISLTQSAGLNLIKQGINVNAIAPGVVDGEHWDGVDALFAKHEGLAPGEKKQRVGAEVPFGRMGTAEDLTGMAIFLASKEADYVVAQTYNVDGGNWMN transporters,Pseudomonas simiae WCS417,WCS417,GFF2331,PS417_11885,PS417_RS16140,WP_010211018.1,UPI00029B1E87,Inositol transport system sugar-binding protein,specific phenotype on inositol. SEED_correct,rhizopine-binding protein,Inositol transport system sugar-binding protein,simple sugar transport system substrate-binding protein,MKTPIRFTALALSMLLASGVASAADLKIGVSMSAFDDTFLTYLREDMDKQAKSYPKGDGVQLQFEDARADVVKQLSQVENFISQKVDAIIVNPVDTASTANIIKAATAAKIPLVFVNRRPDSQTLAPGVAAVTSDDVEAGKLQMQYIAEKLGGKGNIVILLGDLANNSTTNRTKGVKEVLTKYPGIKIEQEQTGIWLRDRGMTLVNDWLTQGRDFQAVLSNNDEMAIGAAMALKSAGKKGVLIAGVDGTPDGLNAITKGDMTVSAFQDAKGQADKSVETARKMAKNEPIEQNVVIPFQLITPDNVKDFK transporters,Pseudomonas simiae WCS417,WCS417,GFF2332,PS417_11890,PS417_RS16135,WP_010211017.1,UPI00029B1B5E,Inositol transport system ATP-binding protein,specific phenotype on inositol. SEED_correct,D-ribose transporter ATP-binding protein,Inositol transport system ATP-binding protein,simple sugar transport system ATP-binding protein,MLAQATVSQPPSLQPQTLEEPYLLEIVNISKGFPGVVALADVQLRVRPGTVLALMGENGAGKSTLMKIIAGIYQPDAGEIRLRGKPIVFETPLAAQKAGIAMIHQELNLMPHMSIAENIWIGREQLNSLHMVNHREMHRCTAELLARLRINLDPEEQVGNLSIAERQMVEIAKAVSYDSDILIMDEPTSAITEKEVAHLFSIIADLKSQGKGIVYITHKMNEVFAIADEVAVFRDGHYIGLQRADSMNSDSLISMMVGRELSQLFPLRETPIGDLLLTVRDLTLDGVFKDVSFDLHAGEILGIAGLMGSGRTNVAETIFGITPSSSGQITLDGKAVRISDPHMAIEKGFALLTEDRKLSGLFPCLSVLENMEMAVLPHYTGNGFIQQKALRALCEDMCKKLRVKTPSLEQCIDTLSGGNQQKALLARWLMTNPRLLILDEPTRGIDVGAKAEIYRLIAFLASEGMAVIMISSELPEVLGMSDRVMVMHEGELMGTLDRSEATQEKVMQLASGMTAVH transporters,Pseudomonas simiae WCS417,WCS417,GFF2333,PS417_11895,PS417_RS16130,WP_010211015.1,UPI00029AEE01,Inositol transport system permease protein,specific phenotype on inositol. SEED_correct,ABC transporter,Inositol transport system permease protein,simple sugar transport system permease protein,MNAITDNKPATVPTKSRRRLPTELSIFLVLIGIGLVFELFGWIVRDQSFLMNSQRLVLMILQVSIIGLLAIGVTQVIITTGIDLSSGSVLALSAMIAASLAQTSDFSRAVFPSLTDLPVWIPVAMGLGVGLLAGAINGSIIAVTGIPPFIATLGMMVSARGLARYYTEGQPVSMLSDSYTAIGHGAMPVIIFLVVAVIFHIALRYTKYGKYTYAIGGNMQAARTSGINVKRHLIIVYSIAGLLAGLAGVVASARAATGQAGMGMSYELDAIAAAVIGGTSLAGGVGRITGTVIGALILGVMASGFTFVGVDAYIQDIIKGLIIVVAVVIDQYRNKRKLKR catabolism,Pseudomonas simiae WCS417,WCS417,GFF2378,PS417_12125,PS417_RS15905,WP_010210955.1,UPI00029B06D9,D-glucosaminate dehydratase (EC 4.3.1.9),"Specifically important for: D-Glucosamine Hydrochloride. This enzyme had not previously been linked to a gene. This is the second step in catabolism of glucosamine, and the 'beta' form of the enzyme was expected to be PLP-dependent and about this size. Iwamoto et al (2003) purified a non-specific 'alpha' enzyme for this reaction (PMID: 12686150)",amino acid deaminase,D-serine deaminase (EC 4.3.1.18),,MSAINAVEKGAAAVGAHLVRDVSLPALVLHRDALEHNIRWMQDFVSNSGAELAPHGKTSMMPALFQRQIEAGAWGITLANAVQTRAAYAGGVRRVLMANQLVGAPNMALIADLLADKDFDFHCMVDHPDNVADLGLFFAARGLRLNVMIEYGVVGGRCGCRTEQEVRDLARAIKAQPALALTGIEGYEGVIHGEHAISGIRDFAASLVRLAVDLQNNGSFDLPKPIVTASGSAWYDLIAESFEQQNAAGRFLSVLRPGSYVAHDHGIYKEAQCCVLDRRSDLNEGLRPALEVWAHVQSLPEPGFAVIALGKRDVAYDAGLPVPLKRYKAGILPAEGDDVTACKVTAVMDQHAFMTVAPGVELRIGDIISFGTSHPCLTFDKWQVGCLVDEQLQVIESLETRF transporters,Pseudomonas simiae WCS417,WCS417,GFF2490,PS417_12700,PS417_RS15345,WP_010210760.1,UPI00029A7CFC,"ABC transporter for D-Mannitol, D-Mannose, and D-Sorbitol, ATPase component",Specific phenotype on D-Sorbitol; D-Mannitol. Also important for D-mannose utilization.,ABC transporter ATP-binding protein,"Various polyols ABC transporter, ATP-binding component",sorbitol/mannitol transport system ATP-binding protein,MANLKIKNLQKGFEGFSIIKGIDLEVNDKEFVVFVGPSGCGKSTLLRLIAGLEEVSEGTIELDGRDITEVTPAKRDLAMVFQTYALYPHMSVRKNMSFALDLAGVDKKLVESKVSEAARILELGPLLERKPKQLSGGQRQRVAIGRAIVRNPKIFLFDEPLSNLDAALRVQMRLELARLHKELQATMIYVTHDQVEAMTLADKVVVLNSGRIEQVGSPLELYHQPANLFVAGFLGTPKMGFLKGKVTRVESQSCEVQLDAGTLINLPLSGATLSVGSAVTLGIRPEHLEIASPGQTTLTVTADVGERLGSDTFCHVITANGEPLTMRIRGDMASQYGETLHLHLDPAHCHLFDTDGVAVARPLRAAA transporters,Pseudomonas simiae WCS417,WCS417,GFF2491,PS417_12705,PS417_RS15340,WP_010210759.1,UPI00029ADD6A,"ABC transporter for D-Mannitol, D-Mannose, and D-Sorbitol, permease component 1",Specific phenotypes on D-Mannitol; D-Mannose; D-Mannose; D-Sorbitol; D-Sorbitol.,mannitol ABC transporter permease,"Various polyols ABC transporter, permease component 2",sorbitol/mannitol transport system permease protein,MTLQQSRRLQSLLLGTLAWAIAILIFFPIFWMVLTSFKTEIDAFATPPQFIFTPTLENYLHINERSNYFSYAWNSVLISFSATALCLLISVPAAYSMAFYETQRTKGTLLWMLSTKMLPPVGVLMPIYLLAKSFGLLDTRIALIIIYTLINLPIVVWMVYTYFKDIPKDILEAARLDGATLWQEMVRVLLPIAKGGLASTVLLSLILCWNEAFWSLNLTSSSAAPLTALIASYSSPEGLFWAKLSAVSTLACAPILIFGWISQKQLVRGLSFGAVK transporters,Pseudomonas simiae WCS417,WCS417,GFF2492,PS417_12710,PS417_RS15335,WP_029529156.1,UPI000403AE5C,"ABC transporter for D-Mannitol, D-Mannose, and D-Sorbitol, permease component 2","Very important for utilization of D-sorbitol, D-mannitol, and D-mannose.",sugar ABC transporter permease,"Various polyols ABC transporter, permease component 1",sorbitol/mannitol transport system permease protein,MNTTTPKNRLANPGWFLVSPSVALLLLWMIVPLGMTLYFSLIRYNLLYPGENEFVGLENFTYFITDSGFLPGATNTLLLVGSVLLISVVFGVLISALLEASEFLGRGLVRVLLISPFFIMPTVGALIWKNLIFHPVSGILAAVWKFFGAEPVDWLAHYPLLSIIIIVSWQWLPFAILLLMTAMQSLDQEQKEAARLDGAGAIAIFWHLTLPHLARPIAVVVMIETIFLLSVFAEIFTTTNGGPGYASTNLAYLIYNQALVQFDVGMASAGGLIAVVIANIAAIILVRMIGKNLTDKP transporters,Pseudomonas simiae WCS417,WCS417,GFF2493,PS417_12715,PS417_RS15330,WP_010210754.1,UPI00029A3EE3,"ABC transporter for D-Mannitol, D-Mannose, and D-Sorbitol, periplasmic substrate-binding protein",Specific phenotype on D-Mannose; D-Sorbitol; D-Mannitol.,sugar ABC transporter substrate-binding protein,"Various polyols ABC transporter, periplasmic substrate-binding protein",sorbitol/mannitol transport system substrate-binding protein,MKFTAKALLASTCMMTCMTLSAVSFGAQTLTIATVNNSDMIRMQKLSKTFEAEHPEIKLNWVVLEENVLRQRLTTDIATQGGQFDVLTIGMYEAALWGAKGWLEPMKDLPASYDLDDVFPSVRDGLSVKGSLYALPFYAESSITYYRTDLFKDAGLTMPEHPTWTQIGEFASKLTDKTKEQYGLCLRGKAGWGENMALITTLANGYGARWFDEKWQPEFNGPEWKDALNFYVDNMKKSGPPGASSNGFNENLALFNSGKCAIWVDASVAGSFVTDKTQSKVADHVGFTFAPHEKTDKGTSWLYSWSLAIPTSSKAKDAAKVFTSWATSKEYGELVAKTDGIANVPPGTRKSTYNDEYMKAAPFAKVTLESLKVADPTKPTLKPVPYIGIQLVTIPEFQAIGTQVGKFFSGALTGQQTVDAALTAAQTTTEREMKRAGYPK catabolism,Pseudomonas simiae WCS417,WCS417,GFF2712,PS417_13835,PS417_RS14240,WP_010210414.1,UPI00029AC8B2,3-hydroxyisobutyryl-CoA hydrolase (EC 3.1.2.4),Specifically important for: L-Valine. KEGG no longer makes any prediction (see pfo:Pfl01_2864). 3-hydroxy-isobutanoyl-CoA is an intermediate in valine degradation. (SEED_correct),crotonase,3-hydroxyisobutyryl-CoA hydrolase (EC 3.1.2.4),enoyl-CoA hydratase,MTAQVSSEASHAETLQDEVLAEVRNHIGHLTLNRPAGLNAITLNMVRRLASQLKAWADDPQVYAVVLRGAGEKAFCAGGDIRSLYDSFKNGDTLHQDFFVEEYALDLAIHHYRKPVLALMDGFVLGGGMGLVQGADLRVVTERSRLAMPEVAIGYFPDVGGSYFLPRIPGELGIYLGVTGVQIRAADALYCGLADWYLESSKLADLDNKLDRLQWHDSPLKDLQGVLAKLAVQQLPDAPLAVLRPAIDHFFALPDVPSIVEQLQQVTVADSHEWALTTAHLMQTRSPLAMAVTLEMLRRGRRLPLEQCFALELHLDRQWFERGDLIEGVRALIIDKDKAPRWNPPTLHGLALSHVESFFHNFKKVAN catabolism,Pseudomonas simiae WCS417,WCS417,GFF2713,PS417_13840,PS417_RS14235,WP_010210413.1,UPI00029B4FAD,isobutyrl-CoA dehydrogenase (EC 1.3.8.1),"Specifically important for: L-Valine. SEED annotates it as butyryl-CoA dehydrogenase, which is also expected to perform this reaction in valine catabolism",acyl-CoA dehydrogenase,Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MQDIEYTEEQVMIRDMARDFARGEIAPYAQAWEKAGWIDDALVAKMGELGLLGMVVPEEWGGTYVDYVAYALAVEEISAGDGATGALMSIHNSVGCGPILNYGTESQKQTWLADLASGQAIGCFCLTEPQAGSEAHNLRTRAELRDGHWVITGAKQFVSNGKRAKLAIVFAITDPELGKKGISAFLVPTATPGFVVDRTEHKMGIRASDTCAVTLNQCTVPEANLLGERGKGLAIALSNLEGGRIGIAAQALGIARAAFEAALAYARDRVQFDKAIIEHQSVANLLADMQTQLNAARLLILHAARLRSAGKPCLSEASQAKLFASEMAEKVCSSAMQIHGGYGYLEDYPVERYYRDARITQIYEGTSEIQRMVIARELKHYQL catabolism,Pseudomonas simiae WCS417,WCS417,GFF2715,PS417_13850,PS417_RS14225,WP_010210411.1,UPI00029AB5F1,2-methylbutanoyl-CoA dehydrogenase (EC 1.3.8.1),"Specifically important for: L-Isoleucine. SEED has it as butyryl-CoA dehydrogenase, which is also expected to perform this reaction in isoleucine catabolism",acyl-CoA dehydrogenase,Butyryl-CoA dehydrogenase (EC 1.3.99.2),,MLPNEEQLQISDAARQFAQERLKPFAAEWDREHRFPKEAIGEMAELGFFGMLVPEQWGGCDTGYLAYAMALEEIAAGDGACSTIMSVHNSVGCVPVLKFGNDQQKEQFLKPLASGAMLGAFALTEPQAGSDASSLKTRARLEGDHYVLNGCKQFITSGQNAGVVIVFAVTDPAAGKRGISAFIVPTDSPGYSVARVEDKLGQHASDTCQILFEDVKVPVANRLGEEGEGYKIALANLEGGRVGIASQAVGMARAAFEAARDYARERESFGKPIIEHQAVAFRLADMATQIAVARQMVHYAAALRDSGKPALVEASMAKLFASEMAEKVCSAALQTLGGYGYLSDFPLERIYRDVRVCQIYEGTSDIQRMVISRNL catabolism,Pseudomonas simiae WCS417,WCS417,GFF3392,PS417_17360,PS417_RS10860,WP_010209382.1,UPI000299FAF3,D-galacturonate dehydrogenase (EC 1.1.1.203),Specifically important for: D-Galacturonic Acid monohydrate. KEGG now annotates it as uronate dehydrogenase which is correct (although less specific) (KEGG_correct),NAD-dependent dehydratase,UDP-glucose 4-epimerase (EC 5.1.3.2),,MTTTPTTPVPFNRLLLTGAAGGLGKVLRERLRPYAQVLRLSDIANMAPAADASEEVQPCDLADKQAVHHLVEGVDAILHFGGVSVERSFEEVLGANISGIFHIYEAARRHGVKRVIFASSNHVIGFYKQGEKLDAHSPRRPDSYYGLSKSYGEDMASFYFDRYGIETVSIRIGSSFPEPQNRRMMHTWLSFDDLTQLLERALYTPNVGHTVVYGMSDNLDTWWDNRYAAHLGFAPKDSSEVFRAQVETQPPVSDNDPAKVYQGGAFCAAGPFGD catabolism,Pseudomonas simiae WCS417,WCS417,GFF3393,PS417_17365,PS417_RS10855,WP_010209380.1,UPI00029A69C7,"galactaro-1,5-lactonase","# Specifically important in carbon source D-Galacturonic Acid monohydrate. This is the second step after D-galacturonate dehydrogenase (PS417_17360) and forms meso-galactarate, which is the substrate of PS417_04210. The substrate is probably the 1,5-lactone, not the 1,4-lactone, given the characterization of the D-galacturonate dehydrogenase from Agrobacterium tumefaciens (PMID:24450804), which is 51% identical to PS417_17360.",gluconolactonase,Gluconolactonase (EC 3.1.1.17),,MNAELIVDARNAVGECPVWVPGENALYWVDIPKGGLQRWSAATGHVAAWTAPQMLACIARTDAGNWVAGMETGFFQLTPHNDGSLDTTLLAAVEHPRQDMRLNDGRCDRQGRFWAGSMVLNMGLNAAEGTLYRYTSGAAPHAQLDGFITLNGLAFSPDGRTMYASDSHPLVQQIWAFDYDIDTGTPSNRRVFVDMHKHLGRPDGAAVDADGCYWICANDAGLIHRFSPDGRLDRSLTVPVKKPTMCAFGGSRLDTLFVTSIRDDQSEQSLSGGVFALNPGVVGLPEPTFTL catabolism,Pseudomonas simiae WCS417,WCS417,GFF3404,PS417_17420,PS417_RS10800,WP_010209365.1,UPI00029A62DC,ethanol oxidation regulatory protein ercA,"Strongest phenotype is on ethanol. Similar to ercA (PA1991) which apparently has a regulatory role (PMCID: PMC3754586) rather than being directly involved in catabolism. Consistent with a regulatory role, this gene has pleiotropic phenotypes. The main ethanol dehydrogenase is probably PS417_17460.",alcohol dehydrogenase,Alcohol dehydrogenase (EC 1.1.1.1); Acetaldehyde dehydrogenase (EC 1.2.1.10),,MSPNLSLLRKFVSPEIIFGAGCRHNVGNYAKTFGARKVLVVSDPGVIAAGWVADVEASLQAIGIDYCLYSAVSPNPRVEEVMLGAEVYRENHCDVIVAIGGGSPMDCGKAIGIVVAHGRCILEFEGVDTIRVPSPPLILIPTTAGTSADVSQFVIISNQQERMKFSIVSKAVVPDVSLIDPETTASMDPFLSACTGIDALVHAIEAFVSTGHGPLTDPHALEAMRLINGNLVQMIANPGDIALREKIMLGSMQAGLAFSNAILGAVHAMSHSLGGFLDLPHGLCNAVLVEHVVAFNYNSAPERFKVIAETFGIDCRGLSHRQVCARLVEHLIALKHAIGFHETLGLHGVRVADIPFLSRHAMHDPCILTNPRESSQRDVEVVYGEAL catabolism,Pseudomonas simiae WCS417,WCS417,GFF3434,PS417_17580,PS417_RS10650,WP_010209327.1,UPI00029A57C7,arginine deiminase (EC 3.5.3.6),"Important on citrulline, which is catabolized via arginine by this reaction in reverse; distantly related to characterized arginine deiminases (PF02274)",amidinotransferase,,,MQTTNTVLMIRPARFAFNPDTAINNRFQRQPLDPLGAQQKALEEFDGYVDTLRRHGVEVLVVQDTPAPHTPDSIFPNNWWSSHADGSLVLYPMEGQNRRLERNKGVLQVLEQRFAINSTIDLSHLEQQNIFLEGTGSMVLDRQHRISYACHSGRTHQDALRQFAERLDYQLCVFHAVDRHHAPIYHSNVMMSVGRDLSVVCLQALPDASERQGLERSLRDTGKDILALDFDQLEGFAGNMLEIHDRDGQPLLVMSASAWGALQPAQRQHVERHTRPVVVNIDNIERIGGGSARCMLAEVHLPARATVQ catabolism,Pseudomonas simiae WCS417,WCS417,GFF3461,PS417_17720,PS417_RS10515,WP_010209291.1,UPI00029A5520,xylitol 2-dehydrogenase (EC 1.1.1.9),Specifically important for: Xylitol. This is the first step in xylitol degradation (SEED_correct),iditol 2-dehydrogenase,Xylitol dehydrogenase (EC 1.1.1.9),L-iditol 2-dehydrogenase,MDKHTEMQAVVCHGPKDYRLERIGKPQARANELVIRIAACGICASDCKCHSGAAMFWGGDNPWVKAPVVPGHEFFGYVVEAGEGAEEHFEVAVGDKVIAEQIVPCGKCRFCKSGKYWMCEVHNIFGFQREVAEGGMAQYMRIPKTAIVHKIPESVSLEDSALVEPMACSIHTVNRGDIQLDDVVVIAGAGTLGLCMVQVAALKTPKKLVVIDMVDERLELAKKFGADVVINPSRDNAREIINGLTDNYGCDVYIETTGVPAGVTQGLDLIRKLGRFVEFSVFGAETSVDWSIIGDRKELDVRGAHLGPYCYPIAIDLFERGLVTSKGIVTHDFPLDDWAEAFELANSTKSIKVLLKPVV transporters,Pseudomonas simiae WCS417,WCS417,GFF4091,PS417_20960,PS417_RS07405,WP_010208503.1,UPI00029A64B9,Nitrate-binding regulatory protein (nasS),"This protein is specifically important for the utilization of nitrate as a nitrogen source, but this may be a polar effect. It is74% identical to the nasS/PA1786 protein from P. aeruginosa (PMID:22493305) and to nasS from Azotobacter vinelandii (PMID:8748040), which binds nitrate and suppresses the activity of the transcriptional activator nasT. It is also more distantly related to the periplasmic substrate-binding components of nitrate ABC transporters (i.e., 37% identity to the N terminal part of ntrC/sll1452 and 36% identity to ntrA/sll1450), hence the misannotation as a transporter.",nitrate transporter,"Nitrate ABC transporter, nitrate-binding protein",sulfonate/nitrate/taurine transport system substrate-binding protein,MNDSDGNSPMNDPLAWVNGSDAPEKSSLDLGFMALSDCASMVVAATQGFAQPYGLTLNLKRQTSWANLRDKLVSGELDAAHSLYGLIYAVHLGIGGVAPTDMAVLMGLNQNGQSINLSRGLQQQGVITPEALDRHVHQSRTKLTFAQTFPTGTHAMWLYYWLASQGIHPLQDVDSVVVPPPQMVAHLQAGRIDGFCVGEPWCASAVKQNQGFTLATTQAIWPDHPEKVLGCTQAFVDQYPNTARVLVMAILEASRFIEESSENRRSTAQLLSGREYLDAPLDCIEPRLLGTYDDGLGNHWQDPHALRFFADGEVNLPYLSDGMWFMTQFRRWGLLREDPDYLGVARQVQQLPLYRQAAAALGIATKHPDMRSSQLIDGKVWDGSDPADYARSFRLHALADTTHRQALR catabolism,Pseudomonas simiae WCS417,WCS417,GFF4238,PS417_21710,PS417_RS06670,WP_010208316.1,UPI00029ACDDF,Succinylornithine transaminase (EC 2.6.1.81),"Specifically important for: L-Arginine. succinylornithine transaminase is involved in L-arginine degradation via N-succinylarginine. Also this gene is cofit with other putative steps in that pathway (N-succinylarginine dihydrolase, succinylglutamate-semialdehyde dehydrogenase, succinylglutamate desuccinylase). In contrast, neither of the functions proposed by KEGG have an obvious relationship to arginine catabolism. (SEED_correct)",acetylornithine aminotransferase,Succinylornithine transaminase (EC 2.6.1.81),acetylornithine/N-succinyldiaminopimelate aminotransferase,MSVEQAPVQRADFDQVMVPNYAPAAFIPVRGEGSRVWDQAGRELIDFAGGIAVNVLGHAHPALVGALTEQAHKLWHVSNVFTNEPALRLAHKLIDATFAERVFFCNSGAEANEAAFKLARRVAFDRFGSEKYEIIAALNSFHGRTLFTVNVGGQSKYSDGFGPKITGITHVPYNDLDALKAAVSDKTCAVVLEPIQGEGGVLPAELAYLQGARDLCDANNALLVFDEVQTGMGRSGHLFAYQHYGVTPDILTSAKSLGGGFPIAAMLTTEALAKHLVVGTHGTTYGGNPLACAVAEAVIDVINTPEVLAGVNAKHDLFKARLEQIGKQYGIFTEVRGMGLLLGCVLSDAFKGKAKDVFNAAEKENLMILQAGPDVVRFAPSLVVEDADIKEGLDRFERAVKALTQA transporters,Pseudomonas simiae WCS417,WCS417,GFF4244,PS417_21740,PS417_RS06640,WP_010208305.1,UPI00029A6D5A,"ABC transporter for L-Arginine, permease component 1",Specific phenotypes on L-Arginine; L-Arginine. KEGG annotation has been updated since the last public release (KEGG_correct),ABC transporter,,,MLKGYGAVILDGAWLTLQLALSSMALAIVLGLIGVALRLSPVRWLAWLGDLYSTVIRGIPDLVLILLIFYGGQDLLNRVAPMLGYDDYIDLNPLAAGIGTLGFIFGAYLSETFRGAFMAIPKGQAEAGLAYGMSSFQVFFRVMVPQMIRLAIPGFTNNWLVLTKATALISVVGLQDMMFKAKQAADATREPFTFFLAVAAMYLVITSVSLLALRHLEKRYSVGVRAADL transporters,Pseudomonas simiae WCS417,WCS417,GFF4321,PS417_22130,PS417_RS06255,WP_010208189.1,UPI00029B0C82,"ABC transporter for D-Glucose-6-Phosphate, ATPase component",Specific phenotype on D-Xylose; D-Glucose-6-Phosphate sodium salt. Xylose has its own transporter so the reason for a phenotype on xylose is unclear.,sugar ABC transporter ATPase,"Glucose ABC transporter, ATP-binding subunit (EC 3.6.3.-)",multiple sugar transport system ATP-binding protein,MATLELRNVNKTYGAGLPDTLKNIELSIKEGEFLILVGPSGCGKSTLMNCIAGLETITGGAIMIGDQDVSGMSPKDRDIAMVFQSYALYPTMSVRENIEFGLKIRKMPQADIDAEVARVAKLLQIEHLLNRKPGQLSGGQQQRVAMGRALARRPKIYLFDEPLSNLDAKLRVEMRTEMKLMHQRLKTTTVYVTHDQIEAMTLGDKVAVMKDGIIQQFGTPKEIYNNPANQFVASFIGSPPMNFVPLRLQRKDGRLVALLDSGQARCELALNTTEAGLEDRDVILGLRPEQIMLAAGEGDSASSIRAEVQVTEPTGPDTLVFVQLNDTKVCCRLAPDVAPQVGETLTLQFDPSKVLLFDANTGERLGTASSLPAQGHADNVAQFKGR transporters,Pseudomonas simiae WCS417,WCS417,GFF4322,PS417_22135,PS417_RS06250,WP_010208187.1,UPI00029A8D08,"ABC transporter for D-Glucose-6-Phosphate, permease component 1",Specific phenotypes on D-Glucose-6-Phosphate sodium salt; D-Glucose-6-Phosphate sodium salt. Also somewhat important on D-xylose but that has its own transporter so the interpretation is unclear.,sugar ABC transporter permease,"Glucose ABC transport system, inner membrane component 2",multiple sugar transport system permease protein,MTSLASKPAISLSRIAIYAVLILAVLLYLVPLVVMLLTSFKTPEDISSGNLLSWPTVVTGIGWVKAWATVDGYFWNSIKITVPAVLISTAIGALNGYVLSFWRFKGSQLFFGLLLFGCFLPFQTVLLPASFTLGKMGLASTTTGLVFVHVVYGLAFTTLFFRNYYVSIPDALIKAARLDGAGFFTIFRQIILPMSTPIIMVCLIWQFTQIWNDFLFGVVFSSGDSQPITVALNNLVNTSTGAKEYNVDMAAAMIAGLPTLLVYVIAGKYFVRGLTAGAVKG transporters,Pseudomonas simiae WCS417,WCS417,GFF4323,PS417_22140,PS417_RS06245,WP_010208185.1,UPI000299D5D4,"ABC transporter for D-Glucose-6-Phosphate, permease component 2",Specific phenotypes on D-Glucose-6-Phosphate sodium salt; D-Glucose-6-Phosphate sodium salt. Also somewhat important on D-xylose but that has its own transporter so the interpretation is unclear.,sugar ABC transporter permease,"Glucose ABC transport system, inner membrane component 1",multiple sugar transport system permease protein,MSSVAVFSKASPFDALQRWLPKLVLAPSMFIVLVGFYGYILWTFVLSFTNSTFLPTYKWAGLAQYARLFDNDRWWVASKNLAVFGGMFIGITLVIGVTLAIFLDQKIRREGFIRTIYLYPMALSMIVTGTAWKWLLNPGMGLDKLLRDWGWEGFRLDWLIDPDRVVYCLVIAAVWQASGFIMAMFLAGLRGVDQSIVRAAQIDGASMPRIYWSVVLPSLRPVFFSAVMILAHIAIKSFDLVAAMTAGGPGYSSDLPAMFMYSFTFSRGQMGMGSASAILMLGAILAIIVPYLYSELRTKRND transporters,Pseudomonas simiae WCS417,WCS417,GFF4324,PS417_22145,PS417_RS06240,WP_010208183.1,UPI00029A56A3,"ABC transporter for D-Glucose-6-Phosphate, periplasmic substrate-binding component",Specific phenotype on D-Xylose; D-Glucose-6-Phosphate sodium salt. Probably also the main transporter for D-xylose.,sugar ABC transporter substrate-binding protein,"Glucose ABC transport system, periplasmic sugar-binding protein",multiple sugar transport system substrate-binding protein,MNAINRLAVAISIASLFPLSAFAADSKGTVEVVHWWTSGGEKAAVDVLKAQVEKDGFVWKDGAVAGGGGATAMTVLKSRAVAGNPPGVAQIKGPDIQEWASTGLLDTDVLKDVAKEEKWDSLLDKKVSDTVKYEGDYVAVPVNIHRVNWLWINPEVFKKAGITKNPTTLQEFYAAGDKLKAAGFIPLAHGGQPWQDSTVFEAVVLSVMGADGYKKALVDLDNGALTGPEMVKALTELKKVATYMDVDGKGQDWNLEAGKVINGKAGMQIMGDWAKSEWTAAKKVAGKDYECVAFPGTDKAFTYNIDSLAVFKQKDKGTAAGQQDIAKVVLGENFQKVFSINKGSIPVRNDMLNKMDSYGFDSCAQTAAKDFLADAKTGGLQPSMAHNMATTLAVQGAFFDVVTNYINDPKADPADTAKKLGAAIKSAK catabolism,Pseudomonas simiae WCS417,WCS417,GFF4325,PS417_22150,PS417_RS06235,WP_038453710.1,UPI00049AFFAB,D-mannose isomerase (EC 5.3.1.7),"Specifically important for: D-Mannitol; D-Mannose. D-mannose isomerase is the first step in mannose catabolism, and the second step in mannitol catabolism. This gene is similar to the putative mannose isomerase Sama_0560 (PMID:20836887, PMCID: PMC4436071).",sugar isomerase,N-acylglucosamine 2-epimerase (EC 5.1.3.8),,MITQPLPASSWLNAPAHYVWLAAEGQRLLAFAKASRLPDGFGNLDDKGQLPADAHAETMNTARMTHSFAMAHALGLPGYAELVAHGVAALSGALRDSEHGGWFAAPHALDGNRGKAAYLHAFVALAASSAVVAGAPGASTLLNDAIHIIDHFFWSEEEGVMLESFAQDWSGVEAYRGANSNMHATEAFLALADVTGDTRWLDRALRIVERVIHTHAAGNQFMVIEHFDTHWHPLLGYNEDNPADGFRPYGITPGHGFEWARLVLHLEAARLQAGLVTPEWLVADAKRLFASACEYAWSVDGAPGIVYTLDWNHRPVVRERLHWTHAEASAAAQALLKRTGELHYETWYRRFWEFCETHFIDRLHGSWHHELSPHNQPSSNIWGGKPDLYHAWQAVLLPALPLAPSMASAIGTGRYVTNW transporters,Pseudomonas simiae WCS417,WCS417,GFF4500,PS417_23035,PS417_RS05360,WP_010207948.1,UPI00029A7B26,"D-trehalose PTS system, I, HPr, and IIA components",Specific phenotype for trehalose and no other trehalose transporter is apparent in the fitness data. The IIB and IIC components are provided by PS417_23050,PTS mannose transporter subunit IIC,"PTS system, glucose-specific IIA component (EC 2.7.1.69) / Phosphotransferase system, phosphocarrier protein HPr / Phosphoenolpyruvate-protein phosphotransferase of PTS system (EC 2.7.3.9)","PTS system, fructose-specific IIA component ; phosphotransferase system, enzyme I, PtsI ; phosphocarrier protein FPr",MTTTQPLELLAPLSGVLLALDKVPDPVFSSRLIGDGLCIDPTSQTLCAPLAGVISNIQDSGHAVSITDDNGVQVLMHIGLDTVNLAGQGFTRLVQEGQRVEAGQPLIEFDADYVALNARSLLTLMLVVSGEPFSLLADGLVETGQPLLQLSPSGAVEAVDEEEGDALFSKPLTLPNANGLHARPAAVFAQAAKGFNASIYLHKQTQSANAKSLVAIMALQTVQGDTLQVSAAGEDAEAAIKALVALLAEGCGEAVVNVAEPVATQSSATLLRGVCASPGSAFGQVVQVTDPELVITEQGTGGATERAALTRGLLAANEALQVLQDKAAGSAQAEIFRAHQELLEDPTLLEHAHRLLGEGKSAAFAWNSATLATVTLFQGLGNALIAERAADLADVGQRVLKLILGIQDSAWDLPERAILIAEQLTPSQTASLDTRKVLGFVTVAGGATSHVAILARALGLPAICGVPAQVLALANGKQVLLDADKGELHLEPNLAEIEQLEAARKHQVLRHQRDVAQASLPATTRDGHHVEVTANVASLQEVEHALTLGGEGVGLLRSEFLYLDRNRAPSPEEQAGTYTAIARALGTERNLVVRTLDVGGDKPLAYVPMDAETNPFLGLRGIRLCLERPQLLREQFRAILASAGFARLHIMLPMVSLLSELHLARKILEEEALALGLTELPKLGIMIEVPSAALMADVFAPHVDFFSIGTNDLTQYTLAMDRDHPRLANQADSFHPAVLRLIATTVKAAHAHGKWVGVCGALASEALAVPVLIGLGVDELSVSVPLIPTIKATVRELDLADCQIIARQVLGLEEAAEVREALRQYHAATVESSPVVEH transporters,Pseudomonas simiae WCS417,WCS417,GFF4501,PS417_23040,PS417_RS05355,WP_010207947.1,UPI00029ADD78,D-trehalose outer membrane porin,Specific phenotype for trehalose (and conserved),maltoporin,"Maltoporin (maltose/maltodextrin high-affinity receptor, phage lambda receptor protein)",maltoporin,MKTTIKLGLVASCLGLPFGAQALEFAGYLRSGAGTSTGSGPQQCFQLPGAQTKYRLGNECEQYAELELRQDLLTLDDGSVLSVDAMASLYNKYDRALKFQGENNGSARMPQMYAQWSNLPSLNGGSLWAGRRYYKRNDIHISDFYYWNQSATGGGIEDVLIGDLKYSYAISRKDNLYQKEYATRHDFNVAGFKTNPGGELELGLSYIEKAGGRDTNSGWALTAQHVQKPFLGGKNKFALQYGEGPGTGLGYTGNTFLDKSSKSYRAVEFFDWQVTPRFGGQIEAVYQKDIRPGSQDQTWMSLGVRPAYAISEQFKLVTELGHDQVDATGGTRKLSKFTFAPTWSPKGPEFWARPEVRLYYTYATWNKAAKRAANELAAGSALSDTGSYGTARHGSNVGVQVEYWWK catabolism,Pseudomonas simiae WCS417,WCS417,GFF4629,PS417_23685,PS417_RS04715,WP_010206526.1,UPI00029B3552,"anthranilate 1,2-dioxygenase (deaminating, decarboxylating) large subunit [EC: 1.14.12.1]",Specifically important for: L-Tryptophan. anthranilate is an intermediate in the breakdown of tryptophan (via kynurenine) (KEGG_correct),"anthranilate 1,2-dioxygenase","Benzoate 1,2-dioxygenase alpha subunit (EC 1.14.12.10)","anthranilate 1,2-dioxygenase (deaminating, decarboxylating) large subunit",MSGARSVEQWKTFIEGCLDFRPADEVFRIARDMFTEPQLFDLEMELIFEKNWIYACHESELANNHDFVTMRAGRQPMIITRDGEGQLNALINACQHRGTTLTRVGKGNQSTFTCPFHAWCYKSDGRLVKVKAPGEYPDGFDKATRGLKKARIESYKGFVFISLDVAGTDSLEDFLGDAKVFFDMMVAQSATGELEVLPGKSAYTYDGNWKLQNENGLDGYHVSTVHYNYVATVQHRQQVNTENGAGAGSTLDYSKLGAGDANTDDGWFAFNNGHSVLFSDMPNPSVRSGYATIMPRLVEEHGQQKAEWMMHRLRNLNIYPSLFFLDQISSQLRIIRPVAWNKTEIISQCLGVKGESDADRENRIRQFEDFFNVSGMGTPDDLVEFREAQRGFQGRLERWSDISRGSHRWETGPTPNSEAIGIQPAMTGTEFTHEGLYVNQHRNWQQFLLNGLDRQALTLREVK transporters,Pseudomonas simiae WCS417,WCS417,GFF4712,PS417_24105,PS417_RS04300,WP_010206633.1,UPI00029B168E,D-lactate transporter (lctP family),"specific phenotype for D-lactate and no other transporter for D-lactate was apparent in the fitness data. Also important for utilization of D,L-lactate. Not clear if it transports L-lactate as well or not, but since it is in an apparent operon with a D-lactate dehydrogenase (PS417_24110), its main role is probably for transport of D-lactate.",L-lactate permease,L-lactate permease,"lactate transporter, LctP family",MQTWQQLYSPLGSLGLSAMAAVIPIVFFFLALAVFRLKGHVAGSITLALSILVAIFAFQMPVDMALAAAGYGFAYGLWPIAWIIVAAVFLYKLTVKSGQFEIIRSSVLSITDDQRLQVLLIGFCFGAFLEGAAGFGAPVAITAALLVGLGFNPLYAAGLCLIANTAPVAFGALGIPIIVAGQVTGIDAFKIGAMTGRQLPLLSLFVPFWLVFMMDGVRGVRETWPAALVAGLSFAITQYFTSNFIGPELPDITSALASLISLTLFLKVWQPKRTAGAQIAGATSSAAVTASAGGFGLPRNTIVSPYSLGQIFKAWSPFLILTVLVTIWTLKPFKAMFAAGGSMYSWVFNFAIPHLDQLVIKVAPIVTNPTAIPAVFKLDPISATGTAIFFSALISMLVLKIDIKTGLTTLKETFYELRWPILSIGMVLAFAFVTNYSGMSSTMALVLAGTGAAFPFFSPFLGWLGVFLTGSDTSSNALFSSLQATTAHQIGVNDTLLVAANTSGGVTGKMISPQSIAVACAATGLVGKESDLFRFTLKHSLFFATIVGLITLAQAYWFTGMLVH catabolism,Pseudomonas simiae WCS417,WCS417,GFF5067,PS417_25960,PS417_RS02565,WP_010207013.1,UPI00029A3054,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Sodium octanoate. presumably part of beta oxidation, but not sure why 2 different ones are required (could relate to different chain lengths) (SEED_correct)",acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MADYKAPLRDMRFVLNEVFEVATTWAQLPALADTVDAETVEAILEEAGKVTAKSIAPLSRGGDEQGCRWDNTAVFTPDGFPQAYATYAQGGWVGVGGDPIFGGMGMPKAVSAQVEEMINSSSLAFGLYPMLTSGACVSINTHASEALKATYLPKMYSGEWSGSMCLTEAHAGTDLGIIRTKAEPQADGSYKVSGTKIFITGGEHDLTDNIIHLVLAKLPDAPAGPKGISLFLVPKFMVNADGSLGARNTVSCGSIEHKMGIQASATCVMNFDEAVGYLVGEPNRGLAAMFTMMNYERLGVGIQGLASGERSYQNAIEYARDRLQSRAPTGAQAQDKMADPIIVHPDVRRMLLTMKAANEGGRAFSTYVATQLDIAKFSDDAAARERADNLVALLTPVAKAFLSDLGLETTVLGQQVFGGHGYIREWGQEQLVRDVRITQIYEGTNGIQALDLMGRKIVGSGGAFYTLFADEIRGFIASSGTELAEFTKPLSAAVDNLDELTAWVLDRAKTNPNEIGAASVEYLHAFGYMAYAYMWARMAKAALGKESEEDFYASKLGTARFYFARLLPRIHSLSASVKAGSESLFLLDEALF catabolism,Pseudomonas simiae WCS417,WCS417,GFF5070,PS417_25975,PS417_RS02550,WP_010207016.1,UPI00029AE91F,Acyl-CoA dehydrogenase (EC 1.3.8.7),"Specifically important for: Sodium octanoate. presumably part of beta oxidation, but not sure why 2 different ones are required (could relate to different chain lengths) (SEED_correct)",acyl-CoA dehydrogenase,Acyl-CoA dehydrogenase (EC 1.3.8.7),,MPDYKAPLRDIRFVRDELLGYEAHYQSLPACQDATPDMVDAILEEGAKFCEQVLAPLNRVGDIEGCTWSESGVKTPTGFKEAYKQFVEGGWPSLAHDVEHGGQGLPESLGLAVSEMVGEANWSWGMYPGLSHGAMNTISEHGTPEQQEAYLTKLVSGEWTGTMCLTEPHCGTDLGMLRTKAEPQADGTYKVSGTKIFISAGEHDMADNIVHIVLARLPDAPAGTKGISLFIVPKFLPNADGSIGARNAVTCGSLEHKMGIHGNATCVMNFDAATGFLIGPANKGLNCMFTFMNTARLGTALQGLAHAEIGFQGGLKYARDRLQMRSLTGPKAPDKAADPIIVHPDVRRMLLTMKAFAEGNRAMVYFTAKQVDIVKYGTDDEAKKQADGLLAFMTPIAKAFMTEVGFESANHGVQIYGGHGFIAEWGMEQNVRDSRISMLYEGTTGIQALDLLGRKVLMTQGEALKGFTKIVHKFCQANEGNEAVKEFVAPLAALNKEWGELTMKVGMAAMKDREEVGAASVDYLMYSGYACLAYFWADMARLAAEKLAAGTTEEAFYTAKLQTARFYFQRILPRTRTHVATMLSGANNLMDMKEEDFGLAY catabolism,Pseudomonas simiae WCS417,WCS417,GFF5299,PS417_27130,PS417_RS01405,WP_010207340.1,UPI000299D157,Gamma-aminobutyrate:alpha-ketoglutarate aminotransferase (EC 2.6.1.19),"Specifically important for: Putrescine Dihydrochloride. KEGG annotates this as putrescine aminotransferase, probably based on similarity to spuC (PA0299) of P. aeruginosa, but it is believed that only the g-glutamyl- pathway operates in P. aeruginosa (X. Yao et al 2011, PMC3147493)",aminotransferase,Omega-amino acid--pyruvate aminotransferase (EC 2.6.1.18),putrescine aminotransferase,MSSNNPQTREWQALSSDHHLAPFSDFKQLKEKGPRIITKAHGVYLWDSEGNKILDGMAGLWCVAIGYGRDELADAAAKQMKELPYYNLFFQTAHPPVLELAKAISDIAPAGMNHVFFTGSGSEGNDTMLRMVRHYWAIKGQPNKKTIISRKNGYHGSTVAGASLGGMTYMHEQGDLPIPGITHIAQPYWFGEGGDMSPEEFGVWAANQLEEKILELGVDNVGAFIAEPIQGAGGVIVPPATYWPRIKEILAKYDILFIADEVICGFGRTGEWFGSDFYDLKPHMMTIAKGLTSGYIPMGGLIVRDEVVEVLNEGGDFNHGFTYSGHPVAAAVALENIRIMRDEKIVNRVHDETAPYLQKRLRELADHPLVGEVRGVGMLGAIELVQDKATRKRYEGKGVGMICRTFCFENGLIMRAVGDTMIISPPLVISKAEIDELVTKARQCLDLTLAALQG catabolism,Pseudomonas simiae WCS417,WCS417,GFF5301,PS417_27140,PS417_RS01395,WP_010207341.1,UPI00029A5BC8,Gamma-glutamyl-putrescine synthetase (EC 6.3.1.11),Specifically important for: Putrescine Dihydrochloride; L-Arginine. The first step in the gamma-glutamyl-putrescine pathway,gamma-glutamylputrescine synthetase,glutamine synthetase family protein,glutamine synthetase,MSVPPRAVQLNEANAFLKDHPEVLYVDLLIADMNGVVRGKRIERTSLHKVYEKGINLPASLFALDINGSTVESTGLGLDIGDADRICYPIPDTLCNEPWQKRPTAQLLMTMHELEGEPFFADPREVLRQVVSKFDDLGLTICAAFELEFYLIDQENVNGRPQPPRSPISGKRPHSTQVYLIDDLDEYVDCLQDILEGAKEQGIPADAIVKESAPAQFEVNLHHVADPIKACDYAVLLKRLIKNIAYDHEMDTTFMAKPYPGQAGNGLHVHISILDKDGKNIFASEDPEQNAALRHAIGGVLETLPAQMAFLCPNVNSYRRFGAQFYVPNSPCWGLDNRTVAIRVPTGSSDAVRIEHRVAGADANPYLLMASVLAGVHHGLTNKIEPNAPVEGNSYEQNEQSLPNNLRDALRELDDSEVMAKYIDPKYIDIFVACKESELEEFEHSISDLEYNWYLHTV catabolism,Pseudomonas simiae WCS417,WCS417,GFF5420,PS417_27745,PS417_RS00800,WP_010207491.1,UPI00029B3B75,gamma-glutamyl-gamma-aminobutyraldehyde dehydrogenase (EC 1.2.1.54),Specifically important for: Putrescine Dihydrochloride. This is part of the gamma-glutamylputrescine pathway. Annotated as the 4-guanidino enzyme in KEGG (based on studies of PA5312 = kauB). Since this enzyme is closely related to kauB it is probably flexible.,aldehyde dehydrogenase,Aldehyde dehydrogenase (EC 1.2.1.3),4-guanidinobutyraldehyde dehydrogenase / NAD-dependent aldehyde dehydrogenase,MTTLTRADWEQRAKDLKIEGRAYINGEYTAAVSGDTFECISPVDGRLLATVASCDAADAQRAVENARATFNSGVWSRLAPAKRKSAMLRFAALLKANAEELALLETLDMGKPISDSLNIDVPGAANALSWSGEAIDKIYDEVAATPHDQLGLVTREPVGVVGAIVPWNFPLMMACWKLGPALSTGNSVILKPSEKSPLTAIRIAALAVEAGIPKGVFNVLPGYGHTVGNALALHMDVDTLVFTGSTKIAKQLLIRSGESNMKRVWLEAGGKSPNIVFADAPDLQAAAESAAGAIAFNQGEVCTAGSRLLVERSIKDKFLPLVIEALKGWKPGNPLDPATNVGALVDTQQMNTVLSYIEAGHADGAKLVAGGKRTLEETGGTYVEPTIFDGVTNAMKIAKEEIFGPVLSVITFDSAEEAVAIANDTIYGLAAAVWTADISKAHLTAKALRAGSVWVNQYDGGDMTAPFGGFKQSGNGRDKSLHAFDKYTELKATWIKL catabolism,Pseudomonas simiae WCS417,WCS417,GFF5490,PS417_28100,PS417_RS00450,WP_010207589.1,UPI000299DEB1,glucose-6-phosphate 1-epimerase [EC: 5.1.3.15],"Specifically important for: D-Trehalose dihydrate; N-Acetyl-D-Glucosamine. NAG is phosphorylated by a PTS system, deacetylated (PS417_22985), and converted to fructose-6-phosphate by an aminotranferase (PS417_22990). One might expect the fructose-6-P to be converted to fructose-1,6-bisphosphate, but no gene for that is apparent in this organism or many other Pseudomonas (nor did the fitness data highlight any kinase). Instead, the fructose-6-phosphate is apparently converted to glucose-6-phosphate by the glucose-6-phosphate isomerase (PS417_23920) and oxidized by G6P dehydrogenase (PS417_22110). (Both of these genes are essential so we cannot confirm this.) G6P dehydrogenase is reportedly specific for the beta form, while G6P isomerase produces the alpha form of G6P, so it makes sense for a glucose-6-P epimerase to be involved (even though the reaction can occur spontaneously). This gene is also mildly important for trehalose utilization, which involves the hydrolysis of trehalose 6-phosphate to glucose-6-phosphate. Because trehalose has an alpha 1->1 linkage, the product is probably alpha-G6P. Thus, this gene's phenotypes are consistent with KEGG's annotation as glucose-6-phosphate 1-epimerase. (KEGG_correct)",aldose epimerase,Aldose 1-epimerase,glucose-6-phosphate 1-epimerase,MPTPHVETVKIDELDCWRIRHNGAELIVAQQGAHLFSYGRDGEQPLIWPNPEAVFKKGKGIRTGVPVCWPWFGVFDRNPQSVKAMRQSEQPAGAHGFVRTALWELAATELEGEALRVDLVLPVPAGGFPGWPHQVDLTLSLLLDEQLHIRLTSHNRGTDTVTLSQALHTYFAVSDVRNAQVEGLDGRAYIDTADGWTEKTQSGLLHFTAETDRIYLDTPSQLTIVDKDWQRRIQLTSEGSKSTVIWNPWTERAKAFDDMADDGWTGMLCIETANVLDDVVSLAPGESHTLGVSIAAIAL catabolism,Pseudomonas simiae WCS417,WCS417,GFF827,PS417_04200,PS417_RS23585,WP_010213153.1,UPI00029AA936,2-ketoglutaric semialdehyde dehydrogenase (EC 1.2.1.26),Specifically important for: D-Galacturonic Acid monohydrate. alpha-ketoglutarate semialdehyde is an intermediate in the oxidation of galacturonate (SEED_correct),aldehyde dehydrogenase,2-ketoglutaric semialdehyde dehydrogenase (EC 1.2.1.26),aldehyde dehydrogenase (NAD+),MSQAQRFDNYINGQWVAGADYCVNLNPSELSDVIGEYAKADVTQVNAAIDAARAAFPAWSTSGIQARHDALDKVGSEILARREELGTLLAREEGKTLPEAIGEVTRAGNIFKFFAGECLRLSGDYVPSVRPGVNVEVTREALGVVGLITPWNFPIAIPAWKIAPALAYGNCVVIKPAELVPGCAWALAEIISRAGFPAGVFNLVMGSGRVVGDVLVNSPKVDGISFTGSVGVGRQIAVSCVSRQAKVQLEMGGKNPQIILDDADLKQAVELSVQSAFYSTGQRCTASSRLIVTAGIHDQFVAAMAERMKSIKVGHALKSGTDIGPVVSQAQLDQDLKYIDIGQSEGARLVSGGGLVTCDTEGYYLAPTLFADSEAAMRISREEIFGPVANVVRVADYEAALAMANDTEFGLSAGIATTSLKYANHFKRHSQAGMVMVNLPTAGVDYHVPFGGRKGSSYGSREQGRYAQEFYTVVKTSYIGS transporters,Pseudomonas simiae WCS417,WCS417,GFF828,PS417_04205,PS417_RS23580,WP_010213152.1,UPI00029A5E75,D-galacturonate transporter (MFS superfamily),Specific phenotype on galacturonate. This phenotype is conserved. There is also another putative hexuronate transporter that has a weaker phenotype on galacturonate (PS417_14775).,glucarate transporter,D-galactarate permease,"MFS transporter, ACS family, glucarate transporter",MQATKPTHVRYLILLMLFLVTTINYADRATIAIAGSSLQKDLGIDAVTLGYIFSAFGWAYVAGQIPGGWLLDRFGSKKVYALSIFTWSLFTVLQGYVGEFGVSTAVVALFMLRFMVGLAEAPSFPGNARIVAAWFPTAERGTASAIFNSAQYFATVLFAPLMGWIVYSFGWQHVFIVMGVIGIIFSLIWLKVIHSPRQHPMINEAEFNHIAANGAMVDMDQDKGKGKKTDGPKWDYIRQLLTNRMMLGVYLGQYCINGITYFFLTWFPVYLVQDRGMTILKAGFIASLPAICGFIGGVLGGVISDYLLRKGHSLTFARKAPIIGGLLISSSIVACNYVDIEWMVVGFMALAFFGKGVGALGWAVVSDTSPKQIAGLSGGLFNTFGNLASITTPIVIGYIISTTGSFKWALVFVGANALVAVFSYLVIVGPIKRVVLKEPPTQGPELTRLTEAHS ================================================ FILE: parenthood_bin.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. r"""Writes a dictionary of all transitive labels to apply to an example. Includes EC, and GO labels (Enzyme Commission, Gene Ontology). The format is gzipped json from key (GO term, EC number) to list of all labels that should be applied to this term. For canonical labels, the values in this list contain the key itself. For non-canonical labels (e.g. obsoleted labels), the value of this map for a key may not include the key itself, thus describing a normalized form for labels. """ import gzip import io import json from typing import Text, Dict, Set from absl import app from absl import flags from absl import logging import parenthood_lib import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS flags.DEFINE_string('output_file', '/tmp/parenthood.json.gz', 'The file to which the parenthood file will be written') def _get_ec_transitive(): """Loads dictionary of label to implied labels for EC numbers.""" logging.info('Getting EC parenthood dict.') with tf.io.gfile.GFile(parenthood_lib.EC_LEAF_NODE_METADATA_PATH) as f: leaf_node_contents = f.read() with tf.io.gfile.GFile(parenthood_lib.EC_NON_LEAF_NODE_METADATA_PATH) as f: non_leaf_node_contents = f.read() ec = parenthood_lib.parse_full_ec_file_to_transitive_parenthood( leaf_node_contents, non_leaf_node_contents) return ec def _get_go_transitive(): """Loads dictionary of label to implied labels for GO terms.""" logging.info('Getting GO parenthood dict.') with tf.io.gfile.GFile(parenthood_lib.GO_METADATA_PATH) as f: whole_go_contents = f.read() go_nontransitive = parenthood_lib.parse_full_go_file(whole_go_contents) go_transitive = parenthood_lib.transitive_go_parenthood(go_nontransitive) return go_transitive def get_output_dict(): """Get output dictionary of label to set of transitive applicable labels. Returns: Dictionary from label to all labels (transitively) that should be used for an example with that label. Raises: ValueError if go and ec terms contain any shared keys. """ ec = _get_ec_transitive() go = _get_go_transitive() overlapping_keys = ( set(ec.keys()).intersection(go.keys())) if overlapping_keys: raise ValueError('There was an overlap in keys between EC/GO. ' 'Overlapping keys: {}'.format(overlapping_keys)) to_write = dict() to_write.update(ec) to_write.update(go) return to_write def write_output_dict(output_dict, output_path): """Writes `output_dict` as json to a gzipped file.""" to_write_json = json.dumps( {k: sorted(list(v)) for k, v in output_dict.items()}, sort_keys=True, ) logging.info('gzipping dictionary.') gzip_contents = io.BytesIO() with gzip.GzipFile(fileobj=gzip_contents, mode='w') as f: f.write(to_write_json.encode('utf-8')) logging.info('Writing to file.') with tf.io.gfile.GFile(output_path, 'wb') as output_file: output_file.write(gzip_contents.getvalue()) def main(_): output_dict = get_output_dict() write_output_dict(output_dict, FLAGS.output_file) logging.info('Done.') if __name__ == '__main__': app.run(main) ================================================ FILE: parenthood_lib.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Utilities for parsing EC and GO labels, and finding their parents.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import itertools import os import re import typing from typing import (Collection, Dict, FrozenSet, Iterable, List, Optional, Set, Text, Tuple) import pandas as pd import utils import tensorflow.compat.v1 as tf import tqdm # From ftp://ftp.expasy.org/databases/enzyme/enzyme.dat DATA_DIR = 'data/' EC_LEAF_NODE_METADATA_PATH = os.path.join(DATA_DIR, 'enzyme.dat') # From ftp://ftp.expasy.org/databases/enzyme/enzclass.txt EC_NON_LEAF_NODE_METADATA_PATH = os.path.join(DATA_DIR, 'enzclass.txt') # From http://purl.obolibrary.org/obo/go.obo GO_METADATA_PATH = os.path.join(DATA_DIR, 'go.obo') # Labels that are implied by other labels. # Json is a map from string key (label) to list of applicable/implied # labels (string). APPLICABLE_LABEL_JSON_PATH = os.path.join(DATA_DIR, 'parenthood.json.gz') # GO:followed by seven digits, followed by # either a space, or the end of the line. GO_TERM_REGEX = re.compile(r'(GO:\d\d\d\d\d\d\d)( |$)') # Allows numbers in all positions, or a hyphen in latter positions, or an 'n', # indicating that the number is still undergoing consideration and review: # https://www.ebi.ac.uk/ena/WebFeat/qualifiers/EC_number.html EC_NUMBER_REGEX = r'(\d+).([\d\-n]+).([\d\-n]+).([\d\-n]+)' _TOP_LEVEL_EC_CLASS_VALUE = '-.-.-.-' # Format of lines at ftp://ftp.expasy.org/databases/enzyme/enzclass.txt # that contain an EC number. _NON_LEAF_NODE_LINE_REGEX = re.compile(r'^\d\.') # The determination that these terms are either parenthood or not was made # with the help of this document: # https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_2.html _IDENTITY_TYPE_GO_RELATIONS = { # Used for obsolete terms. 'replaced_by', # Is basically a synonym. 'alt_id', } _PARENTHOOD_TYPE_GO_RELATIONS = { # Clearly a transitive parenthood relation. 'is_a', } _NON_PARENTHOOD_TYPE_GO_RELATIONS = { # -------- Tag types that are clearly not parenthood relations ----------- 'comment', 'created_by', 'creation_date', 'def', 'disjoint_from', 'id', 'is_obsolete', 'name', 'namespace', 'property_value', # -------- Tag types that seem like they'd be parenthood relations ------- # Gives a term which may be an appropriate substitute for an obsolete # term, but needs to be looked at carefully by a human expert before the # replacement is done. 'consider', # Describes subsets of go terms that may be useful for different uses. 'subset', # Contains references to databases other than GO terms. 'synonym', # As of August 10 2019, the following are the types of relationships # listed in the canonical GO ontology OBO file: # {'ends_during', # 'happens_during', # 'has_part', # 'negatively_regulates', # 'occurs_in', # 'part_of', # 'positively_regulates', # 'regulates'} # None of these qualifies for propagation to children because of "is-a" # type semantics. 'relationship', # Contains references to databases other than GO terms. 'xref', # "intersection_of" This tag indicates that this term is equivalent to the # intersection of several other terms. Many times, one of the terms # is already in an is_a relationship, and the other intersection term has # a non-parenthood type relation, e.g. has-a. For this reason, we exclude # intersection_of relationships. 'intersection_of', } _IS_NON_CANONICAL_ALT_ID_LABEL_OF = 'was_alt_id_of' TermID = Text RelationDescriptor = Text # GO is an ontology of relations of "term" "related to" "other term". # A GoAttribute describes the right-hand-side of this. GoAttribute = Tuple[RelationDescriptor, TermID] class GoTerm( typing.NamedTuple('GoTerm', (('term_id', Text), ('term_name', Optional[Text]), ('description', Optional[Text]), ('related_labels', Set[GoAttribute])))): """A Gene Ontology term. A Gene Ontology term is a term that's related to other terms via an enumerated set of relation types. Not all relations are "is-a" relations. [1] Describes the format that go terms are presented in. [1] https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_2.html Attributes: term_id: id of term. E.g. GO:0000108 term_name: value of "name:" tag in obo file. description: value of "def:" tag in obo file. related_labels: set of [relation, term_id] that also apply to this term. These labels are NOT transitive. """ @classmethod def from_string(cls, s): """Parses a Term block of OBO file, keeping identity and parent relations. https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_2.html Args: s: block of OBO file that starts with [Term] Returns: GoTerm """ lines = s.split('\n') attributes = [ _parse_go_attribute(l) for l in lines if _is_go_attribute_line(l) ] term_ids = [value for tag, value in attributes if tag == 'id'] if len(term_ids) != 1: raise ValueError(('Number of term names for term was {} ' '(expected exactly one). Term was {}.').format( len(term_ids), attributes)) term_id = term_ids[0] related_labels = set() term_description = None term_name = None for tag, value in attributes: if tag == 'name': term_name = value if tag == 'def': term_description = re.findall('"(.*)" .*', value)[0] if tag in _PARENTHOOD_TYPE_GO_RELATIONS or tag in _IDENTITY_TYPE_GO_RELATIONS: related_labels.add((tag, _get_go_term_from_text(value))) elif tag in _NON_PARENTHOOD_TYPE_GO_RELATIONS: continue else: valid_relations = _PARENTHOOD_TYPE_GO_RELATIONS.union( _NON_PARENTHOOD_TYPE_GO_RELATIONS).union( _IDENTITY_TYPE_GO_RELATIONS) raise ValueError('Term type unknown: was {} and expected one of {}. ' 'Full value was {}'.format(tag, valid_relations, s)) return GoTerm( term_id=term_id, term_name=term_name, description=term_description, related_labels=related_labels) def _is_go_attribute_line(s): return ': ' in s def _parse_go_attribute(s): split = s.split(': ') return (split[0], ''.join(split[1:])) def _get_go_term_from_text(s): matches = GO_TERM_REGEX.findall(s) if len(matches) != 1: raise ValueError( 'Expected exactly one match for a GO term in string {}. Found matches {}' .format(s, matches)) # First match, looking at the go term, not the (space or end-of-line). return matches[0][0] def _yield_terms_for_alt_ids(term): """Yields GoTerms that point to the root term for all alt_ids in `term`. Alt ids do not have their own term in the ontology, so this function is used to create these terms and to canonicalize these alternative ids to their preferred ids. Args: term: GoTerm. May or may not have alt_ids in its parents. Yields: GoTerm for each alt_id of `term`, whose parent labels are only the label of term. """ for relation, related_term_id in term.related_labels: if relation == 'alt_id': related_labels = {(_IS_NON_CANONICAL_ALT_ID_LABEL_OF, term.term_id)} non_canonical_term = GoTerm(related_term_id, term.term_name, term.description, related_labels) yield non_canonical_term yield GoTerm( term_id=term.term_id, term_name=term.term_name, description=term.description, related_labels={l for l in term.related_labels if l[0] != 'alt_id'}) def parse_full_go_file(file_contents = None): """Parses contents of OBO file containing the GO ontology. Args: file_contents: string. File contents of go file. Returns: List of GoTerm. """ if file_contents is None: with tf.io.gfile.Open(GO_METADATA_PATH) as f: file_contents = f.read() unparsed_terms = [ x for x in file_contents.split('\n\n') if x.startswith('[Term]') ] parsed_terms = [GoTerm.from_string(t) for t in unparsed_terms] with_alt_ids_itr = itertools.chain(*(_yield_terms_for_alt_ids(x) for x in parsed_terms)) return list(with_alt_ids_itr) def go_label_to_description( go_file_contents = None): return { t.term_id: f'{t.term_name}' for t in parse_full_go_file(go_file_contents) } def _go_term_applicable_labels_should_include_themselves(term): """Return whether this go term is canonical (should include itself) or not. If a term is an alt_id of something, or is obsolete (has a replaced_by relation), it is not an applicable label. Args: term: GoTerm. Returns: bool """ is_replaced_by = any(relation_type == 'replaced_by' for relation_type, _ in term.related_labels) alt_id = any(relation_type == _IS_NON_CANONICAL_ALT_ID_LABEL_OF for relation_type, _ in term.related_labels) return (not is_replaced_by) and (not alt_id) def transitive_go_parenthood(go_terms): """Converts GoTerms (no transitive relations) to include transitive parents. Includes itself as one of its parents, with the exception of alt_ids and replaced_by tags. When a node has alt ids, its only parent is the term for which it is an alt_id. Note that a term may only be an alt_id for one term [1]. When a node is obsolete, it has one or more replaced_by tags [2], and this obsolete name will not be included in the transitive right-hand-side. [1] https://github.com/geneontology/go-ontology/blob/7be0df46781f2e3a456a3e178def19dcdbb20ecf/src/util/check-obo-for-standard-release.pl#L134-L140 [2] https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_4.html Args: go_terms: List of GoTerm. Returns: Dict of term_id -> transitive set of parent term names. """ go_term_dict = { t.term_id: frozenset(label for _, label in t.related_labels) for t in go_terms } transitive_go_terms = { t.term_id: _transitive_parenthood(t.term_id, go_term_dict) for t in tqdm.tqdm(go_terms, position=0) } terms_whose_labels_should_include_themselves = frozenset( term.term_id for term in go_terms if _go_term_applicable_labels_should_include_themselves(term)) # Add in self to parents when it's not an alt_id or obsolete label. See # docstring for more information. for term_id in terms_whose_labels_should_include_themselves: transitive_go_terms[term_id].add(term_id) return transitive_go_terms def _transitive_parenthood(key, term_dict): """Finds all parents, transitively, of `key` in `term_dict`. Does not include itself in the set of parents, regardless of the type of relation. This is left to the caller to decide. Args: key: Go Term, e.g. GO:0000001 term_dict: Go term to set of parent go terms. Returns: Set of transitive parent go terms of `key`. """ running_total = set() to_examine = set(term_dict[key]) # Make a copy so we can pop from it. while len(to_examine) > 0: # pylint: disable=g-explicit-length-test cur_element = to_examine.pop() running_total.add(cur_element) for potential in term_dict[cur_element]: if potential not in running_total: to_examine.add(potential) return running_total def _replace_one_level_up_with_dash_for_ec(s): """Finds direct parent of a label. Args: s: e.g. 1.2.3.4. Values including 'n' in one of their numbers [1] are treated like every other value. Non leaf nodes (those including a hyphen) are also allowed. Returns: E.g. 1.2.-.- """ if s.count('-') == 0: return re.sub(EC_NUMBER_REGEX, '\\1.\\2.\\3.-', s) if s.count('-') == 1: return re.sub(EC_NUMBER_REGEX, '\\1.\\2.-.-', s) if s.count('-') == 2: return re.sub(EC_NUMBER_REGEX, '\\1.-.-.-', s) if s.count('-') == 3: return re.sub(EC_NUMBER_REGEX, '-.-.-.-', s) raise ValueError('Expected the number of hyphens in string to be between ' '0 and 3 (string was {}). Check that the input matches the ' 'regex {}'.format(s, EC_NUMBER_REGEX)) def _all_ec_parents_for_label(label): """Computes all parents for an EC label. Does not include top level EC (level 0) value -.-.-.- in output. Args: label: e.g. 1.2.3.4. Values including 'n' in one of their numbers [1] are treated like every other value. Non leaf nodes (those including a hyphen) are also allowed. Returns: For e.g., 1.2.3.-, returns set(1.2.3.-, 1.2.-.-, 1.-.-.-) That is, this includes both the input `label`, as well as the root node -.-.-.-. """ parent = label parents_set = set() while parent != _TOP_LEVEL_EC_CLASS_VALUE: # Exclude -.-.-.- from output. parents_set.add(parent) # First loop adds self to parenthood. parent = _replace_one_level_up_with_dash_for_ec(parent) return parents_set def _get_leaf_node_ec_labels_from_file_contents( enzyme_dat_file_contents = None): """Parses enzyme.dat file [1] into EC numbers and descriptions. [1] ftp://ftp.expasy.org/databases/enzyme/enzyme.dat [2] ftp://ftp.expasy.org/databases/enzyme/enzuser.txt Args: enzyme_dat_file_contents: Text of file at [1]. Follows format at [2]. Contains only information about leaf nodes of the EC hierarchy (labels with no hyphens; e.g. 1.2.3.4). If None, the current file is parsed from disk. Returns: List of string like "1.2.3.4", "oxalic acid oxidase". Note: ec numbers do not include the string "EC". """ if enzyme_dat_file_contents is None: with tf.io.gfile.Open(EC_LEAF_NODE_METADATA_PATH) as f: enzyme_dat_file_contents = f.read() ids_and_descriptions = [] # Beginning of EC file does not have to do with term parsing; we omit # the "0th" ID entry. See [1] in docstring for the format. id_blocks = enzyme_dat_file_contents.split('\nID')[1:] for block in id_blocks: lines_in_block = block.split('\n') term_id = re.findall(r'\s+(.*)', lines_in_block[0])[0] desc = '' for line in block.split('\n'): if line.startswith('DE'): desc += re.findall(r'DE\s+(.*)', line)[0] ids_and_descriptions.append((term_id, desc)) return ids_and_descriptions def _get_non_leaf_node_ec_labels_from_file_contents( enzyme_class_file_contents = None ): """Parses enzclass.txt file [1] into EC numbers and descriptions. [1] ftp://ftp.expasy.org/databases/enzyme/enzclass.txt Args: enzyme_class_file_contents: Text of file at [1]. Contains only information about non-leaf nodes of the EC hierarchy (e.g. 1.2.3.-). If None, the current file is parsed from disk. Returns: List of string like "1.-.-.-", "oxidoreductase". Note: ec numbers do not include the string "EC". """ if enzyme_class_file_contents is None: with tf.io.gfile.Open(EC_NON_LEAF_NODE_METADATA_PATH) as f: enzyme_class_file_contents = f.read() non_leaf_node_label_lines = [ l.strip() for l in enzyme_class_file_contents.split('\n') if _NON_LEAF_NODE_LINE_REGEX.match(l) ] terms_and_descriptions = [] for line in non_leaf_node_label_lines: term_id = ''.join(line[0:9]).replace(' ', '') term_description = re.findall(r'.*.-\s+(.*)', line)[0] terms_and_descriptions.append((term_id, term_description)) return terms_and_descriptions def ec_label_to_description( enzyme_dat_file_contents = None, enzyme_class_file_contents = None): """Get dictionary from EC label to description. [1] ftp://ftp.expasy.org/databases/enzyme/enzyme.dat [2] ftp://ftp.expasy.org/databases/enzyme/enzuser.txt [3] ftp://ftp.expasy.org/databases/enzyme/enzclass.txt Args: enzyme_dat_file_contents: Text of file at [1]. Follows format at [2]. Contains only information about leaf nodes of the EC hierarchy (labels with no hyphens; e.g. 1.2.3.4). enzyme_class_file_contents: Text of file at [3]. Contains only information about non-leaf nodes of the EC hierarchy (e.g. 1.2.3.-). Returns: Dictionary from EC label to description. Non root-level terms DO NOT have their parents information included, for easier human-readability. """ leaves = _get_leaf_node_ec_labels_from_file_contents(enzyme_dat_file_contents) non_leaves = _get_non_leaf_node_ec_labels_from_file_contents( enzyme_class_file_contents) term_to_description = {} for term, description in non_leaves + leaves: term_to_description['EC:' + term] = description return term_to_description def parse_full_ec_file_to_transitive_parenthood( enzyme_dat_file_contents, enzyme_class_file_contents, ): """Parses enzyme.dat [1] and enzclass.txt [3] into transitive parenthood dict. [1] ftp://ftp.expasy.org/databases/enzyme/enzyme.dat [2] ftp://ftp.expasy.org/databases/enzyme/enzuser.txt [3] ftp://ftp.expasy.org/databases/enzyme/enzclass.txt Args: enzyme_dat_file_contents: Text of file at [1]. Follows format at [2]. Contains only information about leaf nodes of the EC hierarchy (labels with no hyphens; e.g. 1.2.3.4). enzyme_class_file_contents: Text of file at [3]. Contains only information about non-leaf nodes of the EC hierarchy (e.g. 1.2.3.-). Returns: Dict of all EC numbers to each of their parents. The values themselves are included in their parent set. Note that [1] only includes leaf nodes; this function includes all members of the tree as keys in the return value. Parents include a root node called -.-.-.- that indicates that this example is an enzyme. Output keys include the prefix "EC:". """ leaf_node_labels = _get_leaf_node_ec_labels_from_file_contents( enzyme_dat_file_contents) non_leaf_node_labels = _get_non_leaf_node_ec_labels_from_file_contents( enzyme_class_file_contents) id_to_transitive_parents = {} for label, _ in tqdm.tqdm(leaf_node_labels + non_leaf_node_labels): parents_of_label = _all_ec_parents_for_label(label) # Also add parents themselves as labels in the dictionary. for parent_of_label in parents_of_label: if parent_of_label not in id_to_transitive_parents: rhs = set('EC:' + x for x in _all_ec_parents_for_label(parent_of_label)) id_to_transitive_parents['EC:' + parent_of_label] = rhs return id_to_transitive_parents def get_applicable_label_dict( path = APPLICABLE_LABEL_JSON_PATH): return utils.load_gz_json(path) def reverse_map( applicable_label_dict, label_vocab = None): """Flip parenthood dict to map parents to children. Args: applicable_label_dict: e.g. output of get_applicable_label_dict. label_vocab: e.g. output of inference_lib.vocab_from_model_base_path Returns: collections.defaultdict of k, v where: k: originally the values in applicable_label_dict v: originally the keys in applicable_label_dict. The defaultdict returns an empty frozenset for keys that are not found. This behavior is desirable for lifted clan label normalizers, where keys may not imply themselves. """ # This is technically the entire transitive closure, so it is safe for DAGs # (e.g. GO labels). children = collections.defaultdict(set) for child, parents in applicable_label_dict.items(): # Avoid adding children which don't appear in the vocab. if label_vocab is None or child in label_vocab: for parent in parents: children[parent].add(child) children = {k: frozenset(v) for k, v in children.items()} return collections.defaultdict(frozenset, children.items()) def is_implied_by_something_else( current_label, reversed_normalizer, all_labels_for_protein, ): """Returns whether the current label is implied by other labels for protein. Args: current_label: label about which we're asking "is this implied by some other label for this protein?" reversed_normalizer: output of reverse_map(label_normalizer). Helps this function run fast. all_labels_for_protein: set of all labels given to protein. Returns: bool """ all_labels_for_protein_without_current = all_labels_for_protein - frozenset( [current_label]) children_of_current_label = reversed_normalizer[current_label] # Most labels imply themselves; remove. children_of_current_label = children_of_current_label - frozenset( [current_label]) return len( # pylint: disable=g-explicit-length-test children_of_current_label.intersection( all_labels_for_protein_without_current)) > 0 def _filter_label_set_to_most_specific( label_set, reversed_normalizer): """Filters label set to most specific. Args: label_set: set of all labels given to protein. reversed_normalizer: output of reverse_map(label_normalizer). Helps this function run fast. Returns: Filtered set of labels. """ return frozenset([ l for l in label_set if not is_implied_by_something_else(l, reversed_normalizer, label_set) ]) def filter_labels_to_most_specific( df, normalizer, column_to_filter = 'predicted_label', ): """Filter labels given to each protein to the most specific label. Useful for labels like GO, where we predict a ton of labels, and we only want to look at the most informative labels. Args: df: pd.DataFrame with column `column_to_filter`. normalizer: label normalizer. column_to_filter: name of column in df. Returns: pd.DataFrame with column `column_to_filter`. """ reversed_normalizer = reverse_map(normalizer) working_df = df.copy() working_df[column_to_filter] = working_df[column_to_filter].apply( lambda label_set: _filter_label_set_to_most_specific( # pylint: disable=g-long-lambda label_set, reversed_normalizer)) return working_df ================================================ FILE: parenthood_lib_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for module model_performance_analysis.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from os import path import textwrap from absl import flags from absl.testing import absltest from absl.testing import parameterized import pandas as pd import parenthood_lib import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS class ParenthoodLibTest(parameterized.TestCase): def setUp(self): self._data_path = path.join(FLAGS.test_srcdir, './testdata') super().setUp() @parameterized.named_parameters( dict( testcase_name='no relationships', input_text=""" [Term] id: GO:0000001""", expected=parenthood_lib.GoTerm( term_id='GO:0000001', term_name=None, description=None, related_labels=set()), ), dict( testcase_name='only is-a relationship', input_text=""" [Term] id: GO:0000001 is_a: GO:0048308 ! organelle inheritance""", expected=parenthood_lib.GoTerm( term_id='GO:0000001', term_name=None, description=None, related_labels={('is_a', 'GO:0048308')}), ), dict( testcase_name='intersection_of relationship', input_text=""" [Term] id: GO:0000018 name: regulation of DNA recombination intersection_of: regulates GO:0006310 ! DNA recombination """, expected=parenthood_lib.GoTerm( term_id='GO:0000018', term_name='regulation of DNA recombination', description=None, related_labels=set()), ), dict( testcase_name='replaced_by relationship', input_text=""" [Term] id: GO:0000108 name: obsolete repairosome comment: This term was made obsolete because 'repairosome' has fallen out of use in the literature, and the large complex described in the definition has not been confirmed to exist. The term has also confused annotators. synonym: "repairosome" EXACT [] is_obsolete: true replaced_by: GO:0000109""", expected=parenthood_lib.GoTerm( term_id='GO:0000108', term_name='obsolete repairosome', description=None, related_labels={('replaced_by', 'GO:0000109')}), ), dict( testcase_name='two is-a relationships', input_text=""" [Term] id: GO:0000001 is_a: GO:0048308 ! organelle inheritance is_a: GO:0048311 ! mitochondrion distribution""", expected=parenthood_lib.GoTerm( term_id='GO:0000001', term_name=None, description=None, related_labels={('is_a', 'GO:0048308'), ('is_a', 'GO:0048311')}), ), dict( testcase_name='no parent relationships, lots of others', input_text=""" [Term] id: GO:0000001 name: mitochondrion inheritance namespace: biological_process def: "The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton." [GOC:mcc, PMID:10873824, PMID:11389764] synonym: "mitochondrial inheritance" EXACT []""", expected=parenthood_lib.GoTerm( term_id='GO:0000001', term_name='mitochondrion inheritance', description='The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton.', related_labels=set()), ), ) def test_go_term_from_string(self, input_text, expected): input_text = textwrap.dedent(input_text) actual = parenthood_lib.GoTerm.from_string(input_text) self.assertEqual(actual, expected) def test_go_term_parsing_integration_test(self): gene_ontology_reference_full_file_path = path.join(self._data_path, 'go.obo') with tf.io.gfile.GFile(gene_ontology_reference_full_file_path, 'r') as f: full_go_contents = f.read() actual = parenthood_lib.parse_full_go_file(full_go_contents) expected_number_go_terms = 49933 self.assertLen(actual, expected_number_go_terms) # Assert there are no duplicate keys in the labels. self.assertLen(actual, len(set(x.term_id for x in actual))) is_obsolete = ( lambda term: any(l[0] == 'replaced_by' for l in term.related_labels)) actual_non_obsolete_go_terms = [t for t in actual if not is_obsolete(t)] self.assertLess(len(actual_non_obsolete_go_terms), len(actual)) def test_transitive_go_parenthood(self): child1 = parenthood_lib.GoTerm( term_id='child1', term_name=None, description=None, related_labels={('is_a', 'parent1'), ('is_a', 'parent2')}) parent1 = parenthood_lib.GoTerm( term_id='parent1', term_name=None, description=None, related_labels={('is_a', 'grandparent')}) parent2 = parenthood_lib.GoTerm( term_id='parent2', term_name=None, description=None, related_labels={('is_a', 'grandparent')}) grandparent = parenthood_lib.GoTerm( term_id='grandparent', term_name=None, description=None, related_labels=set()) child2 = parenthood_lib.GoTerm( term_id='child2', term_name=None, description=None, related_labels=set()) input_parenthood_data = [child1, child2, parent1, parent2, grandparent] expected = { 'child1': {'child1', 'parent1', 'parent2', 'grandparent'}, 'child2': {'child2'}, 'parent1': {'parent1', 'grandparent'}, 'parent2': {'parent2', 'grandparent'}, 'grandparent': {'grandparent'} } actual = parenthood_lib.transitive_go_parenthood(input_parenthood_data) self.assertDictEqual(actual, expected) def test_transitive_go_parenthood_with_cycle(self): loop1 = parenthood_lib.GoTerm( term_id='loop1', term_name=None, description=None, related_labels={('is_a', 'loop2')}) loop2 = parenthood_lib.GoTerm( term_id='loop2', term_name=None, description=None, related_labels={('is_a', 'loop1')}) input_parenthood_data = [loop1, loop2] expected = { 'loop1': {'loop1', 'loop2'}, 'loop2': {'loop2', 'loop1'}, } actual = parenthood_lib.transitive_go_parenthood(input_parenthood_data) self.assertDictEqual(actual, expected) @parameterized.named_parameters( dict( testcase_name='no hyphens, all single digits', label='1.2.3.4', expected={'1.2.3.4', '1.2.3.-', '1.2.-.-', '1.-.-.-'}, ), dict( testcase_name='one hyphen, all single digits', label='1.2.3.-', expected={'1.2.3.-', '1.2.-.-', '1.-.-.-'}, ), dict( testcase_name='two hyphens, all single digits', label='1.2.-.-', expected={'1.2.-.-', '1.-.-.-'}, ), dict( testcase_name='three hyphens, all single digits', label='1.-.-.-', expected={'1.-.-.-'}, ), dict( testcase_name='no hyphens, multi digit', label='1.22.3.4', expected={'1.22.3.4', '1.22.3.-', '1.22.-.-', '1.-.-.-'}, ), dict( testcase_name='one hyphen, multi digit', label='1.22.3.-', expected={'1.22.3.-', '1.22.-.-', '1.-.-.-'}, ), dict( testcase_name='two hyphens, multi digit', label='1.22.-.-', expected={'1.22.-.-', '1.-.-.-'}, ), dict( testcase_name='no hyphens, contains n, single digit', label='1.2.3.n4', expected={'1.2.3.n4', '1.2.3.-', '1.2.-.-', '1.-.-.-'}, ), dict( testcase_name='one hyphen, contains n, multi digit', label='1.22.n3.-', expected={'1.22.n3.-', '1.22.-.-', '1.-.-.-'}, ), dict( testcase_name='two hyphens, contains n, multi digit', label='1.n22.-.-', expected={'1.n22.-.-', '1.-.-.-'}, ), ) def test_all_ec_parents_for_term(self, label, expected): actual = parenthood_lib._all_ec_parents_for_label(label) self.assertEqual(actual, expected) def test_parse_full_ec_file_to_transitive_parenthood_integration_test(self): enzyme_commission_reference_dat_full_file_path = path.join( self._data_path, 'enzyme.dat') enzyme_commission_reference_class_full_file_path = path.join( self._data_path, 'enzclass.txt') # Load input data. with tf.io.gfile.GFile(enzyme_commission_reference_dat_full_file_path) as f: whole_ec_dat_contents = f.read() with tf.io.gfile.GFile( enzyme_commission_reference_class_full_file_path) as f: whole_ec_class_contents = f.read() # Compute actual output. actual = parenthood_lib.parse_full_ec_file_to_transitive_parenthood( whole_ec_dat_contents, whole_ec_class_contents) expected_to_contain = { # Chosen somewhat arbitrarily. 'EC:1.1.1.1', 'EC:1.2.3.4', 'EC:7.1.1.1', 'EC:2.7.11.n2', 'EC:1.-.-.-', # This label has no direct children in enzyme.dat, so we make sure it's # added in by the enzclass.txt file. 'EC:3.12.-.-', # This entry was previously missing due to a bug, and so it's been added # here as a regression test. 'EC:1.10.98.-', } self.assertContainsSubset(expected_to_contain, actual.keys()) expected_length = 8163 self.assertLen(actual, expected_length) expected_particular_parents = { 'EC:1.2.3.4', 'EC:1.2.3.-', 'EC:1.2.-.-', 'EC:1.-.-.-', } self.assertEqual(actual['EC:1.2.3.4'], expected_particular_parents) def test_ec_label_to_description(self): enzyme_commission_reference_dat_full_file_path = path.join( self._data_path, 'enzyme.dat') enzyme_commission_reference_class_full_file_path = path.join( self._data_path, 'enzclass.txt') # Load input data. with tf.io.gfile.GFile(enzyme_commission_reference_dat_full_file_path) as f: whole_ec_dat_contents = f.read() with tf.io.gfile.GFile( enzyme_commission_reference_class_full_file_path) as f: whole_ec_class_contents = f.read() actual = parenthood_lib.ec_label_to_description(whole_ec_dat_contents, whole_ec_class_contents) self.assertIn('EC:1.-.-.-', actual) self.assertEqual(actual['EC:1.-.-.-'], 'Oxidoreductases.') self.assertIn('EC:1.2.3.4', actual) self.assertEqual(actual['EC:1.2.3.4'], 'Oxalate oxidase.') @parameterized.named_parameters( dict( testcase_name='one alt_id', input_go_term=parenthood_lib.GoTerm( term_id='term', term_name=None, description=None, related_labels={('alt_id', 'alt')}), expected=[ parenthood_lib.GoTerm( term_id='alt', term_name=None, description=None, related_labels={ (parenthood_lib._IS_NON_CANONICAL_ALT_ID_LABEL_OF, 'term') }), parenthood_lib.GoTerm( term_id='term', term_name=None, description=None, related_labels=set()), ]), dict( testcase_name='one replaced_by', input_go_term=parenthood_lib.GoTerm( term_id='go1', term_name='term name', description='Term description.', related_labels={('replaced_by', 'go2')}), expected=[ # Should be a no-op. parenthood_lib.GoTerm( term_id='go1', term_name='term name', description='Term description.', related_labels={('replaced_by', 'go2')}), ]), dict( testcase_name='two alt_id', input_go_term=parenthood_lib.GoTerm( term_id='go1', term_name='Term name', description='Term description.', related_labels={('alt_id', 'go2'), ('alt_id', 'go3')}), expected=[ parenthood_lib.GoTerm( term_id='go2', term_name='Term name', description='Term description.', related_labels={ (parenthood_lib._IS_NON_CANONICAL_ALT_ID_LABEL_OF, 'go1') }), parenthood_lib.GoTerm( term_id='go3', term_name='Term name', description='Term description.', related_labels={ (parenthood_lib._IS_NON_CANONICAL_ALT_ID_LABEL_OF, 'go1') }), parenthood_lib.GoTerm( term_id='go1', term_name='Term name', description='Term description.', related_labels=set()), ]), dict( testcase_name='one replaced_by with other non-identity relation', input_go_term=parenthood_lib.GoTerm( term_id='child', term_name=None, description=None, related_labels={('replaced_by', 'replacement'), ('is_a', 'parent')}), expected=[ # Should be a no-op. parenthood_lib.GoTerm( term_id='child', term_name=None, description=None, related_labels={('is_a', 'parent'), ('replaced_by', 'replacement')}), ]), dict( testcase_name='one alt_id with other non-identity relation', input_go_term=parenthood_lib.GoTerm( term_id='child', term_name=None, description=None, related_labels={('alt_id', 'alt'), ('is_a', 'parent')}), expected=[ parenthood_lib.GoTerm( term_id='alt', term_name=None, description=None, related_labels={ (parenthood_lib._IS_NON_CANONICAL_ALT_ID_LABEL_OF, 'child') }), parenthood_lib.GoTerm( term_id='child', term_name=None, description=None, related_labels={('is_a', 'parent')}), ]), ) def test_yield_terms_for_alt_ids(self, input_go_term, expected): actual = list(parenthood_lib._yield_terms_for_alt_ids(input_go_term)) self.assertCountEqual(actual, expected) def test_reverse_map_filters_items(self): test_parents = {'b': ['a', 'b'], 'a': ['a'], 'c': ['c'], 'd': ['a', 'b']} test_vocab = ['a', 'b', 'c'] rev = parenthood_lib.reverse_map(test_parents, label_vocab=test_vocab) rev_map = {'a': {'a', 'b'}, 'b': {'b'}, 'c': {'c'}} self.assertEqual(rev, rev_map) def test_is_implied_by_something_else_positive_case(self): input_label = 'CL0192' input_reversed_normalizer = { 'CL0192': {'PF00001', 'PF00002'}, 'PF00002': {'PF00002'}, } input_other_labels_for_protein = {'CL0192', 'PF00002'} actual = parenthood_lib.is_implied_by_something_else( input_label, input_reversed_normalizer, input_other_labels_for_protein) expected = True self.assertEqual(actual, expected) def test_is_implied_by_something_else_negative_case(self): input_label = 'CL0192' input_reversed_normalizer = { 'CL0192': {'PF00001', 'PF00002'}, } input_other_labels_for_protein = {'CL0192'} actual = parenthood_lib.is_implied_by_something_else( input_label, input_reversed_normalizer, input_other_labels_for_protein) expected = False self.assertEqual(actual, expected) def test_filter_labels_to_most_specific(self): input_df = pd.DataFrame({ 'predicted_label': [ frozenset(['CL0192', 'PF00002']), frozenset(['CL0192']) ] }) input_normalizer = { 'PF00002': frozenset(['PF00002', 'CL0192']), 'CL0192': frozenset(['CL0192']) } actual = parenthood_lib.filter_labels_to_most_specific( input_df, input_normalizer) actual_predicted_labels = actual.predicted_label.values.tolist() expected = [frozenset(['PF00002']), frozenset(['CL0192'])] self.assertListEqual(actual_predicted_labels, expected) if __name__ == '__main__': absltest.main() ================================================ FILE: protein_dataset.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Construct a tf.data.Dataset of protein training data.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import utils import tensorflow.compat.v1 as tf from tensorflow.contrib import data as contrib_data from tensorflow.contrib import lookup as contrib_lookup # TODO(theosanderson): finalise this root path. DATA_ROOT_DIR = 'data/' TEST_FOLD = 'test' DEV_FOLD = 'dev' TRAIN_FOLD = 'train' ALL_FOLD = '*' DATA_FOLD_VALUES = [TRAIN_FOLD, DEV_FOLD, TEST_FOLD, ALL_FOLD] SEQUENCE_KEY = 'sequence' SEQUENCE_LENGTH_KEY = 'sequence_length' SEQUENCE_ID_KEY = 'id' LABEL_KEY = 'label' DATASET_FEATURES = { SEQUENCE_KEY: tf.FixedLenFeature([], tf.string), LABEL_KEY: tf.FixedLenSequenceFeature( [], dtype=tf.string, # Some sequences have no labels. allow_missing=True), SEQUENCE_ID_KEY: tf.FixedLenFeature([], tf.string) } MAX_SEQUENCE_LENGTH = 12000 BUCKET_BOUNDARIES = [1500, 3000, 6000] def _map_sequence_to_ints(example, amino_acid_table): """Take amino acids in features as strings and replaces them with ints. Args: example: dictionary from string to tensor, containing key SEQUENCE_KEY. amino_acid_table: tf.contrib.lookup.index_table_from_tensor. Returns: dict from string to tensor, where the value at SEQUENCE_KEY is converted from a np.array of string labels to a np.array of ints. """ seq = example[SEQUENCE_KEY] seq_char_by_char_sparse = tf.string_split([seq], delimiter='') seq_char_by_char = seq_char_by_char_sparse.values seq_indices = amino_acid_table.lookup(seq_char_by_char) example[SEQUENCE_KEY] = seq_indices return example def _map_labels_to_ints(example, protein_class_table): """Take labels in features as strings and replaces them with ints. Args: example: dictionary from string to tensor, containing key LABEL_KEY. protein_class_table: tf.contrib.lookup.index_table_from_tensor. Returns: dict from string to tensor, where the value at LABEL_KEY is converted from a np.array of string labels to a np.array of ints. """ label_indices = protein_class_table.lookup(example[LABEL_KEY]) # In a multilabel task there are multiple labels in the label field. # Any labels not in the vocab are mapped to -1 and we then remove them # with the below: label_mask = tf.not_equal(label_indices, -1) label_indices = tf.boolean_mask(label_indices, label_mask) example[LABEL_KEY] = label_indices return example def _to_one_hot_sequence(indexed_sequence_tensors): """Convert ints in sequence to one-hots. Turns indices (in the sequence) into one-hot vectors. Args: indexed_sequence_tensors: dict containing SEQUENCE_KEY field. For example: { 'sequence': '[1, 3, 3, 4, 12, 6]' # This is the amino acid sequence. ... } Returns: indexed_sequence_tensors with the same overall structure as the input, except that SEQUENCE_KEY field has been transformed to a one-hot encoding. For example: { # The first index in sequence is from letter C, which # is at index 1 in the amino acid vocabulary, and the second is from # E, which is at index 4. SEQUENCE_KEY: [[0, 1, 0, ...], [0, 0, 0, 1, 0, ...]...] ... } """ indexed_sequence_tensors[SEQUENCE_KEY] = tf.one_hot( indices=indexed_sequence_tensors[SEQUENCE_KEY], depth=len(utils.AMINO_ACID_VOCABULARY)) return indexed_sequence_tensors def _add_sequence_length(example): example[SEQUENCE_LENGTH_KEY] = tf.strings.length(example[SEQUENCE_KEY]) return example def _is_sequence_short_enough_for_training(example): return tf.greater( tf.constant(MAX_SEQUENCE_LENGTH), example[SEQUENCE_LENGTH_KEY]) def non_batched_dataset(train_dev_or_test, label_vocab, data_root_dir=DATA_ROOT_DIR): """Constructs a dataset of examples. Args: train_dev_or_test: one of _DEV_FOLD_VALUES. The source examples to load into a dataset. label_vocab: list of string. data_root_dir: path to tfrecord examples. Returns: tf.data.Dataset, where each example is of form { SEQUENCE_KEY: one-hot of amino acid characters SEQUENCE_LENGTH_KEY: length of sequence SEQUENCE_ID_KEY: unique identifier for protein LABEL_KEY: rank-1 tensor of integer labels from label_vocab, } """ if train_dev_or_test not in DATA_FOLD_VALUES: raise ValueError(('Only train, dev, test and * are supported datasets.' ' Received {}.').format(train_dev_or_test)) dataset_files = [ os.path.join(data_root_dir, f) for f in tf.gfile.ListDirectory(data_root_dir) if train_dev_or_test in f and ".tfrecord" in f ] tfrecord_dataset = tf.data.TFRecordDataset(dataset_files) dataset = tfrecord_dataset.map(lambda record: tf.io.parse_single_example( # pylint: disable=g-long-lambda record, DATASET_FEATURES)) dataset = dataset.map(_add_sequence_length) dataset = dataset.filter(_is_sequence_short_enough_for_training) amino_acid_table = contrib_lookup.index_table_from_tensor( utils.AMINO_ACID_VOCABULARY, default_value=len(utils.AMINO_ACID_VOCABULARY)) protein_class_table = contrib_lookup.index_table_from_tensor( mapping=label_vocab) dataset = dataset.map(lambda ex: _map_sequence_to_ints(ex, amino_acid_table)) dataset = dataset.map(lambda ex: _map_labels_to_ints(ex, protein_class_table)) dataset = dataset.map(_to_one_hot_sequence) if train_dev_or_test == TRAIN_FOLD: dataset = dataset.repeat() dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE) return dataset def batched_dataset(input_dataset, batch_size, bucket_boundaries): """Batches and pads input_dataset. Args: input_dataset: tf.data.Dataset. output of _non_padded_dataset. batch_size: int. bucket_boundaries: sequence lengths at boundaries of buckets. Returns: tf.data.Dataset. Because sequences are non-uniform length, and because the number of labels for a sequence is variable, the sequence and label features are padded with 0 and -1, respectively. The batch_size varies with sequenence length, bucketed by bucket_boundaries. """ def _get_element_length(features): length = tf.shape(features[SEQUENCE_KEY])[0] return length padding_values = { SEQUENCE_KEY: tf.constant(0, tf.float32), LABEL_KEY: tf.constant(-1, tf.int64), # Padding value is unused since this is always provided upstream and is # of deterministic shape. SEQUENCE_LENGTH_KEY: tf.constant(0, tf.int32), # Padding value is unused since this is always provided upstream and is # of deterministic shape. SEQUENCE_ID_KEY: '' } bucket_batch_sizes = utils.calculate_bucket_batch_sizes( bucket_boundaries, MAX_SEQUENCE_LENGTH, batch_size) dataset = input_dataset.apply( contrib_data.bucket_by_sequence_length( element_length_func=_get_element_length, bucket_batch_sizes=bucket_batch_sizes, bucket_boundaries=bucket_boundaries, pad_to_bucket_boundary=False, padding_values=padding_values)) return dataset def make_input_fn(batch_size, data_file_pattern, train_dev_or_test, label_vocab): """Makes an input_fn, according to the `Estimator` `input_fn` interface. Args: batch_size: int. data_file_pattern: A file path pattern that has your examples. train_dev_or_test: one of _DEV_FOLD_VALUES. The source examples to load into a dataset. label_vocab: list of string. Returns: input_fn to be used by Estimator. """ def _input_fn(): """`Estimator`-compatible input_fn.""" dataset = non_batched_dataset( train_dev_or_test=train_dev_or_test, data_root_dir=data_file_pattern, label_vocab=label_vocab) dataset = batched_dataset( dataset, batch_size, bucket_boundaries=BUCKET_BOUNDARIES) itr = dataset.make_initializable_iterator() data_ops = itr.get_next() features = { SEQUENCE_KEY: data_ops[SEQUENCE_KEY], SEQUENCE_LENGTH_KEY: data_ops[SEQUENCE_LENGTH_KEY], } labels = { LABEL_KEY: data_ops[LABEL_KEY], SEQUENCE_ID_KEY: data_ops[SEQUENCE_ID_KEY] } tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, itr.initializer) return features, labels return _input_fn def yield_examples(tfrecord_path): """Reads TfRecords of protein TfExamples and yields examples as dicts. Args: tfrecord_path: path to TfRecord files of TfExamples. Yields: example dict with keys of SEQUENCE_ID_KEY, SEQUENCE_KEY and LABEL_KEY, containing those values (string, string, list of strings). """ dataset = tf.data.TFRecordDataset(tf.gfile.Glob(tfrecord_path)) oneshot = dataset.make_one_shot_iterator() iterator_get_record = oneshot.get_next() with tf.Session() as sess: while True: try: example_string = sess.run(iterator_get_record) except tf.errors.OutOfRangeError: return example_proto = tf.train.Example.FromString(example_string) example = {LABEL_KEY: []} example[SEQUENCE_ID_KEY] = example_proto.features.feature.get( SEQUENCE_ID_KEY).bytes_list.value[0] example[SEQUENCE_KEY] = example_proto.features.feature.get( SEQUENCE_KEY).bytes_list.value[0] try: example[LABEL_KEY] = example_proto.features.feature.get( LABEL_KEY).bytes_list.value except AttributeError: continue yield example ================================================ FILE: protein_dataset_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import flags from absl.testing import parameterized import numpy as np import protein_dataset import utils import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS def _numpy_one_hot(x, depth): """Convert numpy array of indexes into a full one-hot. Args: x: np.array. depth: int. maximum index in array (depth of one-hot output). Returns: np.array. """ return np.eye(depth)[x] def _dataset_iterator_to_list(itr, session): """Convert tf.data.Dataset iterator to a python list. Args: itr: tf.data.Dataset iterator. session: tf.Session. Returns: list. """ actual_examples = [] while True: try: actual_examples.append(session.run(itr.get_next())) except tf.errors.OutOfRangeError: break return actual_examples class ProteinDatasetTest(parameterized.TestCase): def test_non_padded_dataset(self): # Set up test data. test_data_directory = os.path.join( FLAGS.test_srcdir, './testdata' ) label_vocab_array = [ 'EMBL:AE017224', 'RefSeq:WP_002966386.1', 'ProteinModelPortal:P0CB34', 'SMR:P0CB34', 'EnsemblBacteria:AAX75635', 'GeneID:29595679', 'KEGG:bmb:BruAb2_0191', 'HOGENOM:HOG000133897', 'KO:K04078', 'OMA:PGRIDDN', 'Proteomes:UP000000540', 'GO:GO:0005737', 'GO:GO:0005524', 'GO:GO:0006457', 'CDD:cd00320', 'Gene3D:2.30.33.40', 'HAMAP:MF_00580', 'InterPro:IPR020818', 'InterPro:IPR037124', 'InterPro:IPR018369', 'InterPro:IPR011032', 'PANTHER:PTHR10772', 'Pfam:PF00166', 'PRINTS:PR00297', 'SMART:SM00883', 'SUPFAM:SSF50129', 'PROSITE:PS00681' ] with tf.Graph().as_default(): sess = tf.Session() dataset = protein_dataset.non_batched_dataset( # Dev fold instead of train fold because the train fold is repeated. train_dev_or_test=protein_dataset.DEV_FOLD, label_vocab=label_vocab_array, data_root_dir=test_data_directory) example_itr = dataset.make_initializable_iterator() sess.run(tf.tables_initializer()) sess.run(tf.global_variables_initializer()) sess.run(example_itr.initializer) # Compute actual output actual_examples = _dataset_iterator_to_list(example_itr, sess) expected_length = 4 # Compute expected values expected_sequence = 'MADIKFRPLHDRVVVRRVESEAKTAGGIIIPDTAKEKPQEGEVVAAGAGARDEAGKLVPLDVKAGDRVLFGKWSGTEVKIGGEDLLIMKESDILGIVG' expected_sequence_indexes = [ utils.AMINO_ACID_VOCABULARY.index(x) for x in expected_sequence ] expected_sequence_one_hot = _numpy_one_hot( expected_sequence_indexes, depth=len(utils.AMINO_ACID_VOCABULARY)) # Because the label vocab is exactly the labels in the first example, we # just get range(len(label_vocab_array)) expected_label_indexes = range(len(label_vocab_array)) expected_id = b'P0CB34' # Assert values correct self.assertLen(actual_examples, expected_length) np.testing.assert_equal(actual_examples[0][protein_dataset.SEQUENCE_KEY], expected_sequence_one_hot) np.testing.assert_equal( actual_examples[0][protein_dataset.SEQUENCE_LENGTH_KEY], len(expected_sequence)) np.testing.assert_equal(actual_examples[0][protein_dataset.LABEL_KEY], expected_label_indexes) np.testing.assert_equal(actual_examples[0][protein_dataset.SEQUENCE_ID_KEY], expected_id) def test_padded_dataset(self): # Set up test data. test_data_directory = os.path.join( FLAGS.test_srcdir, './testdata' ) label_vocab_array = ['EMBL:AE017224'] batch_size = 3 with tf.Graph().as_default(): sess = tf.Session() non_padded_dataset = protein_dataset.non_batched_dataset( # Dev fold instead of train fold because the train fold is repeated. train_dev_or_test=protein_dataset.DEV_FOLD, label_vocab=label_vocab_array, data_root_dir=test_data_directory) batched_dataset = protein_dataset.batched_dataset( non_padded_dataset, batch_size=batch_size, bucket_boundaries=[11000]) batch_itr = batched_dataset.make_initializable_iterator() sess.run(tf.tables_initializer()) sess.run(tf.global_variables_initializer()) sess.run(batch_itr.initializer) # Compute actual output actual_examples = _dataset_iterator_to_list(batch_itr, sess) # Examine correctness of first element. actual_sequence_batch_shape = actual_examples[0][ protein_dataset.SEQUENCE_KEY].shape expected_longest_sequence_len_in_first_batch = 98 expected_first_batch_sequence_shape = ( batch_size, expected_longest_sequence_len_in_first_batch, len(utils.AMINO_ACID_VOCABULARY)) self.assertEqual(actual_sequence_batch_shape, expected_first_batch_sequence_shape) actual_label_batch_shape = actual_examples[0][ protein_dataset.LABEL_KEY].shape # Because the label vocab contains the labels in the first example, we # get len(label_vocab_array) as the number of labels. expected_batch_label_shape = (batch_size, len(label_vocab_array)) self.assertEqual(actual_label_batch_shape, expected_batch_label_shape) def test_yield_examples(self): path = os.path.join( FLAGS.test_srcdir, './testdata/train*.tfrecord' ) actual_examples = list(protein_dataset.yield_examples(path)) expected_length = 4 self.assertLen(actual_examples, expected_length) self.assertEqual( set(actual_examples[0].keys()), set([ protein_dataset.SEQUENCE_KEY, protein_dataset.SEQUENCE_ID_KEY, protein_dataset.LABEL_KEY ])) if __name__ == '__main__': tf.test.main() ================================================ FILE: protein_model.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Construct model and evaluation metrics for training.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import protein_dataset import tensorflow.compat.v1 as tf from tensorflow.contrib import layers as contrib_layers from tensorflow.contrib.layers.python.layers import optimizers as optimizers_lib _THRESHOLDS_FOR_RECALL_METRIC = [2, 3, 5, 10] REPRESENTATION_KEY = 'representation' LOGITS_KEY = 'logits' def _f1_score(labels, predictions): """Computes F1 score, i.e. the harmonic mean of precision and recall.""" precision = tf.metrics.precision(labels, predictions) recall = tf.metrics.recall(labels, predictions) return (2 * precision[0] * recall[0] / (precision[0] + recall[0] + 1e-5), tf.group(precision[1], recall[1])) def _mean_examplewise_f1_score(labels, predictions): """Calculates mean example-wise F1 score (micro-F1). Args: labels: 2D tensor of one hots. predictions: 2D tensor of one hots. Returns: metric, update ops from tf.metrics.mean """ labels = tf.cast(labels, tf.float32) predictions = tf.cast(predictions, tf.float32) assert len(labels.shape) == 2 assert len(predictions.shape) == 2 true_positives = labels * predictions false_positives = predictions * (1 - labels) false_negatives = (1 - predictions) * labels true_positives = tf.reduce_sum(true_positives, axis=1) false_positives = tf.reduce_sum(false_positives, axis=1) false_negatives = tf.reduce_sum(false_negatives, axis=1) precision = true_positives / (true_positives + false_positives + 1e-5) recall = true_positives / (true_positives + false_negatives) f1 = 2 * precision * recall / (precision + recall + 1e-5) # F1 score is not defined where there are no correct labels, ignore these: well_defined = tf.greater(true_positives + false_negatives, 0) # Remove any nans (these new 0s will be ignored by the weights anyway): f1 = tf.where(well_defined, f1, tf.zeros_like(f1)) return tf.metrics.mean(f1, weights=well_defined) def _custom_recall_at_k(labels_as_multi_hot, predictions, k): """Calculates recall_at_k metric with multi-hot labels. For each example which contains at least one label, a recall-at-k is calculated by assessing what proportion of these labels are in the top k predictions. This metric is the mean of these values. Args: labels_as_multi_hot: a tensor of [batch_size, num_output_classes] where elements are zero (absent) or one (present). predictions: a tensor of [batch_size, num_output_classes] where elemenents are floats indicating the probability of class membership. k: number of top predictions to consider (must be <= num_output_classes). Returns: mean: A scalar `Tensor` representing the current mean, the value of `total` divided by `count` (of finite values). update_op: An operation that increments the `total` and `count` variables appropriately and whose (scalar) value matches the mean_value. """ labels_as_multi_hot = tf.cast(labels_as_multi_hot, tf.float32) num_output_classes = tf.shape(labels_as_multi_hot)[1] _, indices = tf.math.top_k(predictions, k=k) predictions_top_k_as_multi_hot = _indices_to_multihot(indices, num_output_classes) true_positives_tensor = tf.math.logical_and( tf.cast(labels_as_multi_hot, tf.bool), tf.cast(predictions_top_k_as_multi_hot, tf.bool)) false_negatives_tensor = tf.math.greater(labels_as_multi_hot, predictions_top_k_as_multi_hot) true_positives_per_example = tf.count_nonzero(true_positives_tensor, axis=1) false_negatives_per_example = tf.count_nonzero(false_negatives_tensor, axis=1) recall_per_example = true_positives_per_example / ( true_positives_per_example + false_negatives_per_example) is_finite = tf.is_finite(recall_per_example) # To filter out no label cases. recall_per_example_finite_only = tf.boolean_mask(recall_per_example, is_finite) return tf.metrics.mean(recall_per_example_finite_only) def _make_evaluation_metrics(labels, predictions, num_output_classes, hparams): """Construct various eval metrics. Args: labels: dict with ground truth data necessary for computing metrics. predictions: dict containing Tensors for predictions. num_output_classes: number of different labels. hparams: tf.contrib.HParams object. Returns: A dict where the values obey the tf.metrics API. """ labels_op = labels[protein_dataset.LABEL_KEY] multi_hot_labels = _indices_to_multihot(labels_op, num_output_classes) predictions_as_floats = predictions[protein_dataset.LABEL_KEY] recall_threshold = hparams.decision_threshold predictions_as_bools = tf.greater(predictions_as_floats, tf.constant(recall_threshold)) metrics = { 'precision_at_threshold': tf.metrics.precision( labels=multi_hot_labels, predictions=predictions_as_bools), 'recall_at_threshold': tf.metrics.recall( labels=multi_hot_labels, predictions=predictions_as_bools), 'f1_at_threshold': _f1_score(labels=multi_hot_labels, predictions=predictions_as_bools), 'mean_examplewise_f1_at_threshold': _mean_examplewise_f1_score( labels=multi_hot_labels, predictions=predictions_as_bools), 'true_positives': tf.metrics.true_positives( labels=multi_hot_labels, predictions=predictions_as_bools), 'false_positives': tf.metrics.false_positives( labels=multi_hot_labels, predictions=predictions_as_bools) } for k in _THRESHOLDS_FOR_RECALL_METRIC: metrics['recall@%d' % k] = _custom_recall_at_k( labels_as_multi_hot=multi_hot_labels, predictions=predictions_as_floats, k=k) return metrics def _set_padding_to_sentinel(padded_representations, sequence_lengths, sentinel): """Set padding on batch of padded representations to a sentinel value. Useful for preparing a batch of sequence representations for max or average pooling. Args: padded_representations: float32 tensor, shape (batch, longest_sequence, d), where d is some arbitrary embedding dimension. E.g. the output of tf.data.padded_batch. sequence_lengths: tensor, shape (batch,). Each entry corresponds to the original length of the sequence (before padding) of that sequence within the batch. sentinel: float32 tensor, shape: broadcastable to padded_representations. Returns: tensor of same shape as padded_representations, where all entries in the sequence dimension that came from padding (i.e. are beyond index sequence_length[i]) are set to sentinel. """ sequence_dimension = 1 embedding_dimension = 2 with tf.variable_scope('set_padding_to_sentinel', reuse=False): longest_sequence_length = tf.shape( padded_representations)[sequence_dimension] embedding_size = tf.shape(padded_representations)[embedding_dimension] seq_mask = tf.sequence_mask(sequence_lengths, longest_sequence_length) seq_mask = tf.expand_dims(seq_mask, [embedding_dimension]) is_not_padding = tf.tile(seq_mask, [1, 1, embedding_size]) full_sentinel = tf.zeros_like(padded_representations) full_sentinel = full_sentinel + tf.convert_to_tensor(sentinel) per_location_representations = tf.where( is_not_padding, padded_representations, full_sentinel) return per_location_representations def _make_per_sequence_features(per_location_representations, raw_features, hparams): """Aggregate representations across the sequence dimension.""" sequence_lengths = raw_features[protein_dataset.SEQUENCE_LENGTH_KEY] per_location_representations = _set_padding_to_sentinel( per_location_representations, sequence_lengths, tf.constant(0.)) # We average the representations across the sequence length dimension: # tf.reduce_mean(..., axis=1) is problematic, since different batches # may be dynamically padded to different lengths. Instead, we normalize # each element of the batch individually, by the length of each element's # un-normalized sequence. We raise this to a tunable power to allow the # tuner to choose between mean and sum-pooling or an intermediate type. denominator = tf.cast( tf.expand_dims( raw_features[protein_dataset.SEQUENCE_LENGTH_KEY], axis=-1), tf.float32)**hparams.denominator_power pooled_representation = tf.reduce_sum( per_location_representations, axis=1) / denominator pooled_representation = tf.identity( pooled_representation, name='pooled_representation') return pooled_representation def _convert_representation_to_prediction_ops(representation, raw_features, num_output_classes, hparams): """Map per-location features to problem-specific prediction ops. Args: representation: [batch_size, sequence_length, feature_dim] Tensor. raw_features: dictionary containing the raw input Tensors; this is the sequence, keyed by sequence_key. num_output_classes: number of different labels. hparams: tf.contrib.HParams object. Returns: predictions: dictionary containing Tensors that Estimator will return as predictions. predictions_for_loss: Tensor that make_loss() consumes. """ per_sequence_features = _make_per_sequence_features( per_location_representations=representation, raw_features=raw_features, hparams=hparams) logits = tf.layers.dense( per_sequence_features, num_output_classes, name=LOGITS_KEY) predictions = { protein_dataset.LABEL_KEY: tf.identity(tf.sigmoid(logits), name='predictions') } predictions_for_loss = logits return predictions, predictions_for_loss def _make_representation(features, hparams, mode): """Produces [batch_size, sequence_length, embedding_dim] features. Args: features: dict from str to Tensor, containing sequence and sequence length. hparams: tf.contrib.training.HParams() mode: tf.estimator.ModeKeys instance. Returns: Tensor of shape [batch_size, sequence_length, embedding_dim]. """ sequence_features = features[protein_dataset.SEQUENCE_KEY] sequence_lengths = features[protein_dataset.SEQUENCE_LENGTH_KEY] is_training = mode == tf.estimator.ModeKeys.TRAIN sequence_features = _conv_layer( sequence_features=sequence_features, sequence_lengths=sequence_lengths, num_units=hparams.filters, dilation_rate=1, kernel_size=hparams.kernel_size, ) for layer_index in range(hparams.num_layers): sequence_features = _residual_block( sequence_features=sequence_features, sequence_lengths=sequence_lengths, hparams=hparams, layer_index=layer_index, activation_fn=tf.nn.relu, is_training=is_training) return sequence_features def _make_prediction_ops(features, hparams, mode, label_vocab): """Returns (predictions, predictions_for_loss, representation).""" representation = _make_representation(features, hparams, mode) representation = tf.identity(representation, name=REPRESENTATION_KEY) # Used to save constants in the graph, e.g. for SavedModel. _ = tf.constant(label_vocab, name='label_vocab') _ = tf.constant(hparams.decision_threshold, name='decision_threshold') num_output_classes = len(label_vocab) predictions, prediction_for_loss = _convert_representation_to_prediction_ops( representation=representation, raw_features=features, num_output_classes=num_output_classes, hparams=hparams) return predictions, prediction_for_loss def _batch_norm(features, is_training): return tf.layers.batch_normalization(features, training=is_training) def _conv_layer(sequence_features, sequence_lengths, num_units, dilation_rate, kernel_size): """Return a convolution of the input features that respects sequence len.""" padding_zeroed = _set_padding_to_sentinel(sequence_features, sequence_lengths, tf.constant(0.)) conved = tf.layers.conv1d( padding_zeroed, filters=num_units, kernel_size=[kernel_size], dilation_rate=dilation_rate, padding='same') # Re-zero padding, because shorter sequences will have their padding # affected by half the width of the convolution kernel size. re_zeroed = _set_padding_to_sentinel(conved, sequence_lengths, tf.constant(0.)) return re_zeroed def _residual_block(sequence_features, sequence_lengths, hparams, layer_index, activation_fn, is_training): """Construct a single block for a residual network.""" with tf.variable_scope('residual_block_{}'.format(layer_index), reuse=False): shifted_layer_index = layer_index - hparams.first_dilated_layer + 1 dilation_rate = max(1, hparams.dilation_rate**shifted_layer_index) num_bottleneck_units = math.floor( hparams.resnet_bottleneck_factor * hparams.filters) features = _batch_norm(sequence_features, is_training) features = activation_fn(features) features = _conv_layer( sequence_features=features, sequence_lengths=sequence_lengths, num_units=num_bottleneck_units, dilation_rate=dilation_rate, kernel_size=hparams.kernel_size, ) features = _batch_norm(features, is_training=is_training) features = activation_fn(features) # The second convolution is purely local linear transformation across # feature channels, as is done in # third_party/tensorflow_models/slim/nets/resnet_v2.bottleneck residual = _conv_layer( features, sequence_lengths, num_units=hparams.filters, dilation_rate=1, kernel_size=1) with_skip_connection = sequence_features + residual return with_skip_connection def _indices_to_multihot(indices, vocab_size): """Converts [batch,n_labels] of indices to [batch,vocab_size] multihot. Indices can be padded with -1. Args: indices: dense tensor of indices [batch, arbitrary_n_labels], padded with -1 if necessary. vocab_size: integer vocab_size. Returns: Multihot float32 tensor of dimension [batch, vocab_size]. e.g. [[0,1],[2,-1]] (vocab_size:4) -> [1,1,0,0], [0,0,1,0] """ if len(indices.shape) != 2: raise ValueError( 'indices_to_multihot expects tensors of dimension 2, got shape %s' % indices.shape) sparse_indices = contrib_layers.dense_to_sparse(indices, eos_token=-1) multihot = tf.sparse.to_indicator(sparse_indices, vocab_size=vocab_size) multihot = tf.cast(multihot, tf.float32) return multihot def _make_loss(predictions_for_loss, labels, num_output_classes): """Make scalar loss.""" logits = predictions_for_loss labels_op = labels[protein_dataset.LABEL_KEY] # We need to get labels into a multi-hot format: labels_op = _indices_to_multihot(labels_op, vocab_size=num_output_classes) loss = tf.nn.sigmoid_cross_entropy_with_logits( labels=labels_op, logits=logits) loss = tf.reduce_mean(loss) return loss def _make_train_op(loss, hparams): """Create train op.""" def learning_rate_decay_fn(learning_rate, global_step): learning_rate = tf.train.exponential_decay(learning_rate, global_step, hparams.lr_decay_steps, hparams.lr_decay_rate) learning_rate = learning_rate * tf.minimum( tf.cast(global_step / hparams.lr_warmup_steps, tf.float32), tf.constant(1.)) return learning_rate return contrib_layers.optimize_loss( loss=loss, global_step=tf.train.get_global_step(), clip_gradients=optimizers_lib.adaptive_clipping_fn( decay=hparams.gradient_clipping_decay, report_summary=True, ), learning_rate=hparams.learning_rate, learning_rate_decay_fn=learning_rate_decay_fn, optimizer='Adam') def make_model_fn(label_vocab, hparams): """Returns a model function for estimator given prediction base class. Args: label_vocab: list of string. hparams: tf.contrib.HParams object. Returns: A function that returns a tf.estimator.EstimatorSpec """ def _model_fn(features, labels, params, mode=None): """Returns tf.estimator.EstimatorSpec.""" predictions, predictions_for_loss = _make_prediction_ops( features=features, hparams=params, mode=mode, label_vocab=label_vocab) evaluation_hooks = [] num_output_classes = len(label_vocab) if mode == tf.estimator.ModeKeys.TRAIN: loss = _make_loss( predictions_for_loss=predictions_for_loss, labels=labels, num_output_classes=num_output_classes) train_op = _make_train_op(loss=loss, hparams=params) eval_ops = None elif mode == tf.estimator.ModeKeys.PREDICT: loss = None train_op = None eval_ops = None else: # Eval mode. loss = _make_loss( predictions_for_loss=predictions_for_loss, labels=labels, num_output_classes=num_output_classes) train_op = None eval_ops = _make_evaluation_metrics( labels=labels, predictions=predictions, num_output_classes=num_output_classes, hparams=hparams) return tf.estimator.EstimatorSpec( mode=mode, predictions=predictions, loss=loss, train_op=train_op, eval_metric_ops=eval_ops, evaluation_hooks=evaluation_hooks, ) return _model_fn ================================================ FILE: protein_model_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for protein_model.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import flags from absl.testing import parameterized import numpy as np import protein_model import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS class ProteinModelTest(parameterized.TestCase): def testF1Score(self): """Tests the F1 score metric.""" labels = tf.constant([[1, 0], [0, 1], [0, 1], [1, 0]], dtype=tf.int32) # Sensitivity: 1/2 # Specificity: 1/2 # F1-score: 1/2 predictions = tf.constant([[1, 0], [0, 1], [1, 0], [0, 1]], dtype=tf.float32) f1, update_op = protein_model._f1_score(labels, predictions) with tf.Session() as sess: sess.run(tf.local_variables_initializer()) sess.run(update_op) self.assertAlmostEqual(1. / 2, f1.eval(), places=4) def testMeanExampleWiseF1Score(self): """Tests the F1 score metric.""" labels = tf.constant([[1, 0], [0, 1], [0, 1], [1, 0], [0, 0]], dtype=tf.int32) # Sensitivity: 1/2 # Specificity: 1/2 # F1-score: 1/2 predictions = tf.constant([[1, 0], [0, 1], [1, 0], [0, 1], [1, 1]], dtype=tf.float32) f1, update_op = protein_model._mean_examplewise_f1_score( labels, predictions) with tf.Session() as sess: sess.run(tf.local_variables_initializer()) sess.run(update_op) self.assertAlmostEqual(1. / 2, f1.eval(), places=4) def testRecallAtK(self): labels = tf.convert_to_tensor([[1, 0, 0], [0, 1, 1], [0, 0, 0]], dtype=tf.float32) predictions = tf.convert_to_tensor([[0.5, 1.0, 0.0], [0.5, 0.0, 1.0], [0.25, 0.25, 0.4]]) k_values = [0, 1, 2] expected_recall_values_for_each_k = [0, 0.25, 0.75] # For k = 0 we get 0% from both of first two (3rd example is always NA) # For k = 1 we get 0% from first example and 50% from second # For k = 2 we get 100% from first example and 50% from second values_and_updates = [ protein_model._custom_recall_at_k( labels_as_multi_hot=labels, predictions=predictions, k=k) for k in k_values ] with tf.Session() as sess: for i, value_and_update in enumerate(values_and_updates): value, update_op = value_and_update sess.run(tf.initialize_local_variables()) sess.run(update_op) actual_recall = sess.run(value) self.assertEqual(actual_recall, expected_recall_values_for_each_k[i]) @parameterized.named_parameters( dict( testcase_name='float values', padded_representations=[[[11.], [21.], [31.]], [[41.], [51.], [61.]]], sequence_lengths=[2, 3], expected=[[[11], [21], [0]], [[41], [51], [61]]], sentinel=0., ), dict( testcase_name='no padding', padded_representations=[[[11.], [21.], [31.]], [[41.], [51.], [61.]]], sequence_lengths=[3, 3], expected=[[[11.], [21.], [31.]], [[41.], [51.], [61.]]], sentinel=0., ), dict( testcase_name='all padding', padded_representations=[[[11.], [21.], [31.]], [[41.], [51.], [61.]]], sequence_lengths=[0, 0], expected=[[[0.], [0.], [0.]], [[0.], [0.], [0.]]], sentinel=0., ), dict( testcase_name='different sentinel', padded_representations=[[[11.], [21.], [31.]], [[41.], [51.], [61.]]], sequence_lengths=[0, 0], expected=[[[-99.], [-99.], [-99.]], [[-99.], [-99.], [-99.]]], sentinel=-99., ), dict( testcase_name='embedding dimension size > 1', padded_representations=[[[11., -1.], [21., -2.], [31., -3.]], [[41., -4.], [51., -5.], [61., -6.]]], sequence_lengths=[2, 3], expected=[[[11., -1.], [21., -2.], [0., 0.]], [[41., -4.], [51., -5.], [61., -6.]]], sentinel=0., ), ) def testSetPaddingToSentinel(self, padded_representations, sequence_lengths, expected, sentinel): with tf.Graph().as_default(): with tf.Session() as sess: padded_representations = tf.convert_to_tensor(padded_representations) sequence_lengths = tf.convert_to_tensor(sequence_lengths) actual = sess.run( protein_model._set_padding_to_sentinel(padded_representations, sequence_lengths, sentinel)) np.testing.assert_array_almost_equal(actual, expected) @parameterized.parameters( dict( input_array=[[0, 1], [2, 3]], vocab_size=4, expected=[[1, 1, 0, 0], [0, 0, 1, 1]]), dict( input_array=[[3, -1], [2, 3]], vocab_size=4, expected=[[0, 0, 0, 1], [0, 0, 1, 1]])) def testIndicesToMultiHot(self, input_array, vocab_size, expected): with tf.Graph().as_default(): with tf.Session() as sess: input_array = tf.convert_to_tensor(input_array) actual = sess.run( protein_model._indices_to_multihot(input_array, vocab_size)) np.testing.assert_array_almost_equal(actual, expected) if __name__ == '__main__': tf.test.main() ================================================ FILE: proteinfer.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Functionally annotate a fasta file. Write functional predictions as a TSV with columns - sequence_name (string) - predicted_label (string) - confidence (float); number between 0 and 1. An estimate of the model's confidence that the label is true. - label_description (string); a human-readable label description. """ import decimal import io import logging import os from typing import Dict, List, Text, Tuple from absl import app from absl import flags from Bio.SeqIO import FastaIO import numpy as np import pandas as pd import inference import utils os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # TF c++ logging set to ERROR import tensorflow.compat.v1 as tf # pylint: disable=g-import-not-at-top import tqdm _logger = logging.getLogger('proteinfer') FLAGS = flags.FLAGS flags.DEFINE_string('i', None, 'Input fasta file path.') flags.DEFINE_string('o', None, 'Output write path.') flags.DEFINE_integer( 'num_ensemble_elements', 1, 'In order to run with more than one ensemble element, you will need to run ' 'install_models.py --install_ensemble=true. ' 'More ensemble elements takes more time, but tends to be more accurate. ' 'Run-time scales linearly with the number of ensemble elements. ' 'Maximum value of this flag is {}.'.format( utils.MAX_NUM_ENSEMBLE_ELS_FOR_INFERENCE)) flags.DEFINE_float( 'reporting_threshold', .5, 'Number between 0 (exclusive) and 1 (inclusive). Predicted labels with ' 'confidence at least resporting_threshold will be included in the output.', lower_bound=0., upper_bound=1.) flags.DEFINE_string('model_cache_path', 'cached_models', 'Path from which to use downloaded models and metadata.') # A list of inferrers that all have the same label set. _InferrerEnsemble = List[inference.Inferrer] # (list_of_pfam_inferrers, list_of_ec_inferrers, list_of_go_inferrers) _Models = Tuple[_InferrerEnsemble, _InferrerEnsemble, _InferrerEnsemble] def _num_decimal_places(f): """Get the number of decimal places in a float.""" # https://stackoverflow.com/a/6190291/1445296 return abs(decimal.Decimal('{}'.format(f)).as_tuple().exponent) def _gcs_path_to_relative_unzipped_path(p): """Parses GCS path, to gets the last part, and removes .tar.gz.""" return os.path.join( os.path.basename(os.path.normpath(p)).replace('.tar.gz', '')) def _get_inferrer_paths(model_urls, model_cache_path): """Convert list of model GCS urls to a list of locally cached paths.""" return [ os.path.join(model_cache_path, _gcs_path_to_relative_unzipped_path(p)) for p in model_urls ] def load_models(model_cache_path, num_ensemble_elements): """Load models from cache path into inferrerLists. Args: model_cache_path: path that contains downloaded SavedModels and associated metadata. Same path that was used when installing the models via install_models. num_ensemble_elements: number of ensemble elements of each type to load. Returns: (list_of_pfam_inferrers, list_of_ec_inferrers, list_of_go_inferrers) Raises: ValueError if the models were not found. The exception message describes that install_models.py needs to be rerun. """ try: pfam_inferrer_paths = _get_inferrer_paths(utils.OSS_PFAM_ZIPPED_MODELS_URLS, model_cache_path) ec_inferrer_paths = _get_inferrer_paths(utils.OSS_EC_ZIPPED_MODELS_URLS, model_cache_path) go_inferrer_paths = _get_inferrer_paths(utils.OSS_GO_ZIPPED_MODELS_URLS, model_cache_path) to_return = [] inferrer_list_paths_for_all_models = [ pfam_inferrer_paths, ec_inferrer_paths, go_inferrer_paths ] pbar = tqdm.tqdm( desc='Loading models', position=0, total=len(inferrer_list_paths_for_all_models) * num_ensemble_elements, leave=True, dynamic_ncols=True) for inferrer_list_paths in inferrer_list_paths_for_all_models: inner_itr = inferrer_list_paths[:num_ensemble_elements] inferrer_list = [] for p in inner_itr: inferrer_list.append(inference.Inferrer(p, use_tqdm=True)) pbar.update() to_return.append(inferrer_list) pfam_inferrers = to_return[0] ec_inferrers = to_return[1] go_inferrers = to_return[2] return pfam_inferrers, ec_inferrers, go_inferrers except tf.errors.NotFoundError as exc: err_msg = 'Unable to find cached models in {}.'.format(model_cache_path) if num_ensemble_elements > 1: err_msg += ( ' Make sure you have installed the entire ensemble of models by ' 'running\n install_models.py --install_ensemble ' '--model_cache_path={}'.format(model_cache_path)) else: err_msg += ( ' Make sure you have installed the models by running\n ' 'install_models.py --model_cache_path={}'.format(model_cache_path)) err_msg += '\nThen try rerunning this script.' raise ValueError(err_msg, exc) def _assert_fasta_parsable(input_text): with io.StringIO(initial_value=input_text) as f: fasta_itr = FastaIO.FastaIterator(f) end_iteration_sentinel = object() # Avoid parsing the entire FASTA contents by using `next`. # A malformed FASTA file will have no entries in its FastaIterator. # This is unfortunate (instead of it throwing an error). if next(fasta_itr, end_iteration_sentinel) is end_iteration_sentinel: raise ValueError('Failed to parse any input from fasta file. ' 'Consider checking the formatting of your fasta file. ' 'First bit of contents from the fasta file was\n' '{}'.format(input_text.splitlines()[:3])) def parse_input_to_text(input_fasta_path): """Parses input fasta file. Args: input_fasta_path: path to FASTA file. Returns: Contents of file as a string. Raises: ValueError if parsing the FASTA file gives no records. """ _logger.info('Parsing input from %s', input_fasta_path) with tf.io.gfile.GFile(input_fasta_path, 'r') as input_file: input_text = input_file.read() _assert_fasta_parsable(input_text=input_text) return input_text def input_text_to_df(input_text): """Converts fasta contents to a df with columns sequence_name and sequence.""" with io.StringIO(initial_value=input_text) as f: fasta_records = list(FastaIO.FastaIterator(f)) fasta_df = pd.DataFrame([(f.name, str(f.seq)) for f in fasta_records], columns=['sequence_name', 'sequence']) return fasta_df def perform_inference(input_df, models, reporting_threshold): """Perform inference for Pfam, EC, and GO using given models. Args: input_df: pd.DataFrame with columns sequence_name (str) and sequence (str). models: (list_of_pfam_inferrers, list_of_ec_inferrers, list_of_go_inferrers). reporting_threshold: report labels with mean confidence across ensemble elements that exceeds this threshold. Returns: pd.DataFrame with columns sequence_name (str), label (str), confidence (float). """ predictions = [] for inferrer_list in tqdm.tqdm( models, position=1, desc='Progress', leave=True): predictions.append( inference.get_preds_at_or_above_threshold(input_df, inferrer_list, reporting_threshold)) print('\n') # Because the tqdm bar is position 1, we need to print a newline. predictions = pd.concat(predictions) return predictions def _sort_df_multiple_columns(df, key): """Sort df based on callable key. Args: df: pd.DataFrame. key: function from rows of df (namedtuples) to tuple. This is used in the builtin `sorted` method as the key. Returns: A sorted copy of df. """ # Unpack into list to take advantage of builtin sorted function. # Note that pd.DataFrame.sort_values will not work because sort_values' # sorting function is applied to each column at a time, whereas we need to # consider multiple fields at once. df_rows_sorted = sorted(df.itertuples(index=False), key=key) return pd.DataFrame(df_rows_sorted, columns=df.columns) def order_df_for_output(predictions_df): """Semantically group/sort predictions df for output. Sort order: Sort by query sequence name as they are in `predictions_df`. Put all Pfam labels first, then EC labels, then GO labels. Given that, - if it's an EC label, sort by label alphabetically. - else, sort by confidence descending. Given that, sort by description alphabetically. The reason to sort EC differently is that the alphabetic ordering of EC labels is meaningful, while the alphabetic orering of Pfam and GO labels is not. Args: predictions_df: df with columns sequence_name (str), predicted_label (str), confidence (float), description (str). Returns: df with columns sequence_name (str), predicted_label (str), confidence (float), description (str). """ seq_name_to_original_order = { item: idx for idx, item in enumerate(predictions_df.sequence_name) } def filter_by_label_type(df, label_type): return df[df.predicted_label.apply(lambda x: x.startswith(label_type))] def _orderer_pfam_and_go(df_row): """See outer function doctsring.""" confidence_sort_key = -1 * df_row.confidence return (seq_name_to_original_order[df_row.sequence_name], confidence_sort_key, df_row.description) def _orderer_ec(df_row): """See outer function doctsring.""" confidence_sort_key = -1 * df_row.confidence return (seq_name_to_original_order[df_row.sequence_name], df_row.predicted_label) pfam_df = filter_by_label_type(predictions_df, 'Pfam') ec_df = filter_by_label_type(predictions_df, 'EC') go_df = filter_by_label_type(predictions_df, 'GO') pfam_df_sorted = _sort_df_multiple_columns(pfam_df, _orderer_pfam_and_go) ec_df_sorted = _sort_df_multiple_columns(ec_df, _orderer_ec) go_df_sorted = _sort_df_multiple_columns(go_df, _orderer_pfam_and_go) return pd.concat([pfam_df_sorted, ec_df_sorted, go_df_sorted]) def _format_float_confidence_for_output(input_float, num_decimal_places): # Create a separate function so as to test it against our expectations. return np.around(input_float, num_decimal_places) def format_df_for_output(predictions_df, label_to_description, num_decimal_places): """Formats df for outputting. Args: predictions_df: df with columns sequence_name (str), predicted_label (str), confidence (float). label_to_description: contents of label_descriptions.json.gz. Map from label to a human-readable description. num_decimal_places: number of decimal places to display in the confidence output column. Returns: df with columns sequence_name (str), predicted_label (str), confidence (float), description (str). """ predictions_df = predictions_df.copy() predictions_df['description'] = predictions_df.predicted_label.apply( label_to_description.__getitem__) predictions_df['confidence'] = predictions_df.confidence.apply( lambda x: _format_float_confidence_for_output(x, num_decimal_places)) return order_df_for_output(predictions_df) def write_output(predictions_df, output_path): """Write predictions_df to tsv file.""" _logger.info('Writing output to %s', output_path) with tf.io.gfile.GFile(output_path, 'w') as f: predictions_df.to_csv(f, sep='\t', index=False) def run(input_text, models, reporting_threshold, label_to_description): """Runs inference and returns output as a pd.DataFrame. Args: input_text: contents of a fasta file. models: (list_of_pfam_inferrers, list_of_ec_inferrers, list_of_go_inferrers). reporting_threshold: report labels with mean confidence across ensemble elements that exceeds this threshold. label_to_description: contents of label_descriptions.json.gz. Map from label to a human-readable description. Returns: pd.DataFrame with columns sequence_name (str), label (str), confidence (float). """ input_df = input_text_to_df(input_text) predictions_df = perform_inference( input_df=input_df, models=models, reporting_threshold=reporting_threshold) predictions_df = format_df_for_output( predictions_df=predictions_df, label_to_description=label_to_description, num_decimal_places=max(2, _num_decimal_places(reporting_threshold))) return predictions_df def load_assets_and_run(input_fasta_path, output_path, num_ensemble_elements, model_cache_path, reporting_threshold): """Loads models/metadata, runs inference, and writes output to tsv file. Args: input_fasta_path: path to FASTA file. output_path: path to which to write a tsv of inference results. num_ensemble_elements: Number of ensemble elements to load and perform inference with. model_cache_path: path that contains downloaded SavedModels and associated metadata. Same path that was used when installing the models via install_models. reporting_threshold: report labels with mean confidence across ensemble elements that exceeds this threshold. """ _logger.info('Running with %d ensemble elements', num_ensemble_elements) input_text = parse_input_to_text(input_fasta_path) models = load_models(model_cache_path, num_ensemble_elements) label_to_description = utils.load_gz_json( os.path.join(model_cache_path, utils.INSTALLED_LABEL_DESCRIPTION_FILE_NAME)) predictions_df = run(input_text, models, reporting_threshold, label_to_description) write_output(predictions_df, output_path) def main(_): # TF logging is too noisy otherwise. tf.get_logger().setLevel(tf.logging.ERROR) if FLAGS.reporting_threshold == 0.: raise ValueError('The reporting_threshold flag was 0. Please supply a ' 'value between 0 (exclusive) and 1 (inclusive). A value ' 'of zero will report every label for every protein.') load_assets_and_run( input_fasta_path=FLAGS.i, output_path=FLAGS.o, num_ensemble_elements=FLAGS.num_ensemble_elements, model_cache_path=FLAGS.model_cache_path, reporting_threshold=FLAGS.reporting_threshold) if __name__ == '__main__': _logger.info('Process started.') flags.mark_flags_as_required(['i', 'o']) app.run(main) ================================================ FILE: proteinfer_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for proteinfer binary.""" from absl.testing import parameterized import pandas as pd import proteinfer import test_util import utils import tensorflow.compat.v1 as tf class ProteinferTest(parameterized.TestCase): def test_gcs_path_to_relative_unzipped_path(self): actual = proteinfer._gcs_path_to_relative_unzipped_path( utils.OSS_GO_ZIPPED_MODELS_URLS[0]) expected = 'noxpd2_cnn_swissprot_go_random_swiss-cnn_for_swissprot_go_random-13703706' self.assertEqual(actual, expected) def test_parse_input(self): input_file_path = self.create_tempfile(content='>SEQUENCE_NAME\nACDE') input_text = proteinfer.parse_input_to_text(input_file_path.full_path) actual_df = proteinfer.input_text_to_df(input_text) expected = pd.DataFrame({ 'sequence_name': ['SEQUENCE_NAME'], 'sequence': ['ACDE'], }) # BioPython parses sequences as Bio.Seq.Seq which can, in most cases, # act as sequences, but in others can lead to surprising behavior. Ensure # we actually have a str. self.assertEqual(type(actual_df.sequence.values[0]), str) test_util.assert_dataframes_equal(self, actual_df, expected) def test_parse_input_malformed_fasta(self): # input is missing fasta header line marker. input_file_path = self.create_tempfile(content='SEQUENCE_NAME\nACDE') with self.assertRaisesRegex(ValueError, 'Failed to parse'): proteinfer.parse_input_to_text(input_file_path.full_path) def test_format_output_adds_description_and_formats_float_confidence(self): input_df = pd.DataFrame({ 'sequence_name': ['SEQ_A'], 'predicted_label': ['Pfam:PF000042'], 'confidence': [.991] }) label_to_description = {'Pfam:PF000042': 'Oxygen carrier'} num_decimal_places = 2 actual = proteinfer.format_df_for_output(input_df, label_to_description, num_decimal_places) expected = pd.DataFrame({ 'sequence_name': ['SEQ_A'], 'predicted_label': ['Pfam:PF000042'], 'confidence': [.99], 'description': ['Oxygen carrier'] }) test_util.assert_dataframes_equal(self, actual, expected) @parameterized.parameters( dict(input_confidence=1., num_decimal_places=2, expected=1.0), dict(input_confidence=1., num_decimal_places=3, expected=1.0), dict(input_confidence=0.1, num_decimal_places=2, expected=0.1), dict(input_confidence=0.01, num_decimal_places=2, expected=0.01), dict(input_confidence=0.006, num_decimal_places=2, expected=0.01), dict(input_confidence=0.001, num_decimal_places=3, expected=0.001), ) def test_format_float_confidence(self, input_confidence, num_decimal_places, expected): actual = proteinfer._format_float_confidence_for_output( input_confidence, num_decimal_places) self.assertEqual(actual, expected) def test_load_models_raises_on_model_missing_no_ensemble(self): expected_err_contents = ('Unable to find cached models in FAKE_PATH. Make ' 'sure you have installed the models by running\n' ' install_models.py ' '--model_cache_path=FAKE_PATH\nThen try rerunning ' 'this script.') with self.assertRaises(ValueError) as exc: proteinfer.load_models( model_cache_path='FAKE_PATH', num_ensemble_elements=1) actual = exc.exception.args[0] self.assertIn(expected_err_contents, actual) def test_load_models_raises_on_model_missing_with_ensemble(self): expected_err_contents = ('Unable to find cached models in FAKE_PATH. Make ' 'sure you have installed the entire ensemble of ' 'models by running\n install_models.py ' '--install_ensemble ' '--model_cache_path=FAKE_PATH\nThen try rerunning ' 'this script.') with self.assertRaises(ValueError) as exc: proteinfer.load_models( model_cache_path='FAKE_PATH', num_ensemble_elements=3) actual = exc.exception.args[0] self.assertIn(expected_err_contents, actual) @parameterized.named_parameters( dict( testcase_name='orders by sequence name\'s original ordering', input_df=pd.DataFrame({ 'sequence_name': ['SEQ_B', 'SEQ_A'], 'predicted_label': ['Pfam:PF00001', 'Pfam:PF00002'], 'confidence': [.1, .9], 'description': ['First GPCRA', 'Second GPCR'], }), expected=pd.DataFrame({ # Note that this df is not sorted by the sequence name's column # alphabetically; instead it preserves the original ordering. 'sequence_name': ['SEQ_B', 'SEQ_A'], 'predicted_label': ['Pfam:PF00001', 'Pfam:PF00002'], 'confidence': [.1, .9], 'description': ['First GPCRA', 'Second GPCR'], }), ), dict( testcase_name='orders by confidences given same label class', input_df=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A'], 'predicted_label': ['Pfam:PF00001', 'Pfam:PF00002'], 'confidence': [.1, .9], 'description': ['First GPCRA', 'Second GPCR'], }), expected=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A'], 'predicted_label': ['Pfam:PF00002', 'Pfam:PF00001'], 'confidence': [.9, .1], 'description': ['Second GPCR', 'First GPCRA'], }), ), dict( testcase_name='orders Pfam then EC then GO', input_df=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A', 'SEQ_A'], 'predicted_label': ['GO:0123456', 'EC:1.2.3.-', 'Pfam:PF00001'], 'confidence': [.1, .9, .3], 'description': ['go label', 'ec label', 'pfam label'], }), expected=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A', 'SEQ_A'], 'predicted_label': ['Pfam:PF00001', 'EC:1.2.3.-', 'GO:0123456'], 'confidence': [.3, .9, .1], 'description': ['pfam label', 'ec label', 'go label'], }), ), dict( testcase_name='For EC, orders by label alphabetically, not description alphabetically (despite confidences)', input_df=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A'], 'predicted_label': ['EC:1.2.3.-', 'EC:1.2.-.-'], 'confidence': [1., .9], 'description': [ 'AAA alphabetically FIRST EC label', 'ZZZ alphabetically LAST EC label' ], }), expected=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A'], 'predicted_label': ['EC:1.2.-.-', 'EC:1.2.3.-'], 'confidence': [.9, 1.], 'description': [ 'ZZZ alphabetically LAST EC label', 'AAA alphabetically FIRST EC label' ], }), ), dict( testcase_name='orders by description given same label class and confidence', input_df=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A'], 'predicted_label': ['Pfam:PF00001', 'Pfam:PF00002'], 'confidence': [1., 1.], 'description': ['ZZZZ', 'AAAA'], }), expected=pd.DataFrame({ 'sequence_name': ['SEQ_A', 'SEQ_A'], 'predicted_label': ['Pfam:PF00002', 'Pfam:PF00001'], 'confidence': [1., 1.], 'description': ['AAAA', 'ZZZZ'], }), ), ) def test_order_df_for_output(self, input_df, expected): actual = proteinfer.order_df_for_output(input_df) test_util.assert_dataframes_equal(self, actual, expected) if __name__ == '__main__': tf.test.main() ================================================ FILE: requirements.txt ================================================ absl-py==0.7.1 astor==0.7.1 biopython==1.78 backports.weakref==1.0.post1 enum34==1.1.6 funcsigs==1.0.2 gast==0.2.2 grpcio==1.19.0 h5py==2.9.0 Keras-Applications==1.0.8 Keras-Preprocessing==1.0.9 Markdown==3.0.1 mock==2.0.0 numpy==1.16.5 pandas==1.1.2 pbr==5.1.3 plotly==4.11.0 plotnine==0.7.0 protobuf<4 python-dateutil==2.8.0 pytz==2018.9 scikit-learn==0.23.2 six==1.12.0 tensorflow-gpu==1.15.4 tensorflow-estimator==1.15.1 tensorflow-hub==0.9.0 termcolor==1.1.0 tqdm==4.62.2 Werkzeug==0.15.1 ================================================ FILE: test_util.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests utilities for Proteinfer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import flags from absl.testing import absltest import numpy as np FLAGS = flags.FLAGS def model_testdata_path(): return os.path.join(absltest.get_default_test_srcdir(), './') def savedmodel_path(): return os.path.join(model_testdata_path(), 'testdata/saved_model') def assert_dataframes_equal(abseil_testcase_instance, actual, expected, sort_by_column=None, nan_equals_nan=False): """Assert dataframes equal up to reordering of columns and rows. Supports non-indexable datatypes in fields, like `set` and np.ndarray. Args: abseil_testcase_instance: absltest.TestCase (or parameterized.TestCase). E.g. pass 'self' from within an absltest.TestCase. actual: pd.DataFrame. expected: pd.DataFrame. sort_by_column: optional string name of a column. This column must be sortable (e.g. an int, not an np.array). nan_equals_nan: bool. If true, then allow nan == nan. """ abseil_testcase_instance.assertEqual( len(actual), len(expected), 'Lengths were not equal: {}\nand\n{}'.format(actual, expected)) potential_error_message = 'actual:\n{}\nexpected:\n{}'.format( actual, expected) abseil_testcase_instance.assertSetEqual( set(actual.columns), set(expected.columns), potential_error_message) if len(set(actual.columns)) == 0: # pylint: disable=g-explicit-length-test # Both dataframes are empty. return # Sort rows of DFs in same way, based on just one of the columns. if sort_by_column: actual = actual.sort_values(by=sort_by_column) expected = expected.sort_values(by=sort_by_column) actual_records = actual.to_dict('records') expected_records = expected.to_dict('records') for actual_record, expected_record in zip(actual_records, expected_records): abseil_testcase_instance.assertCountEqual(actual_record.keys(), expected_record.keys(), potential_error_message) for col_name in actual_record.keys(): actual_value = actual_record[col_name] expected_value = expected_record[col_name] if isinstance(actual_value, np.ndarray): np.testing.assert_allclose( actual_value, expected_value, err_msg=potential_error_message) elif isinstance(actual_value, float) and np.isnan(actual_value): if nan_equals_nan and np.isnan(actual_value) and np.isnan( expected_value): continue else: raise AssertionError( actual_value, expected_value, 'Actual value is nan, and nan is not equal to anything. ' '{} != {}. {}'.format(actual_value, expected_value, potential_error_message)) else: abseil_testcase_instance.assertEqual(actual_value, expected_value, potential_error_message) ================================================ FILE: test_util_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Tests for proteinfer.test_util.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import absltest from absl.testing import parameterized import numpy as np import pandas as pd import test_util class TestUtilTest(parameterized.TestCase): @parameterized.named_parameters( dict( testcase_name='empty df', df1=pd.DataFrame(), df2=pd.DataFrame(), ), dict( testcase_name='one column, ints', df1=pd.DataFrame({'col1': [1, 2, 3]}), df2=pd.DataFrame({'col1': [1, 2, 3]}), ), dict( testcase_name='one column, floats', df1=pd.DataFrame({'col1': [1., 2.]}), df2=pd.DataFrame({'col1': [1., 2.]}), ), dict( testcase_name='one column, sets', df1=pd.DataFrame({'col1': [ {1., 2.}, {3., 4.}, ]}), df2=pd.DataFrame({'col1': [ {1., 2.}, {3., 4.}, ]}), ), dict( testcase_name='one column, np.arrays', df1=pd.DataFrame({'col1': [ np.array([1., 2.]), np.array([3., 4.]), ]}), df2=pd.DataFrame({'col1': [ np.array([1., 2.]), np.array([3., 4.]), ]}), ), dict( testcase_name='two columns, ints and floats', df1=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [1., 2.], }), df2=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [1., 2.], }), ), dict( testcase_name='two columns, strings and floats, reordered', df1=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [1., 2.], }), df2=pd.DataFrame({ 'col1': ['b', 'a'], 'col2': [2., 1.], }), order_by_column='col1', ), dict( testcase_name='two columns, strings and np.arrays, reordered', df1=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [ np.array([1., 2.]), np.array([3., 4.]), ], }), df2=pd.DataFrame({ 'col1': ['b', 'a'], 'col2': [ np.array([3., 4.]), np.array([1., 2.]), ], }), order_by_column='col1', ), ) def test_assert_dataframes_equal_no_error(self, df1, df2, order_by_column=None): test_util.assert_dataframes_equal(self, df1, df2, order_by_column) @parameterized.named_parameters( dict( testcase_name='empty df and nonempty df', df1=pd.DataFrame(), df2=pd.DataFrame({'col1': [1., 2., 3.]}), ), dict( testcase_name='one column, different lengths', df1=pd.DataFrame({'col1': [1, 2]}), df2=pd.DataFrame({'col1': [1, 2, 3]}), ), dict( testcase_name='one column, sets', df1=pd.DataFrame({'col1': [ {1., 2.}, {3., 4.}, ]}), df2=pd.DataFrame({'col1': [ {1., 2.}, set([]), ]}), ), dict( testcase_name='one column, np.arrays', df1=pd.DataFrame({'col1': [ np.array([1., 2.]), np.array([3., 4.]), ]}), df2=pd.DataFrame({'col1': [ np.array([1., 2.]), np.array([]), ]}), ), dict( testcase_name='two columns, ints and floats', df1=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [1., 2.], }), df2=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [1., 9999999999999.], }), ), dict( testcase_name='two columns, strings and floats, reordered', df1=pd.DataFrame({ 'col1': ['a', 'b'], 'col2': [1., 2.], }), df2=pd.DataFrame({ 'col1': ['b', 'a'], 'col2': [9999999999999., 1.], }), order_by_column='col1', ), ) def test_assert_dataframes_equal_error(self, df1, df2, order_by_column=None): with self.assertRaises(AssertionError): test_util.assert_dataframes_equal(self, df1, df2, order_by_column) def test_assert_dataframes_equal_nan_equal_nan(self): df1 = pd.DataFrame({'col1': [float('nan'),]}) test_util.assert_dataframes_equal(self, df1, df1, nan_equals_nan=True) def test_assert_dataframes_equal_nan_raises(self): df1 = pd.DataFrame({'col1': [float('nan'),]}) with self.assertRaisesRegex(AssertionError, 'nan'): test_util.assert_dataframes_equal(self, df1, df1, nan_equals_nan=False) if __name__ == '__main__': absltest.main() ================================================ FILE: testdata/blast.tsv ================================================ accession="ABC" accession="DEF" 50 10 100 20 4 5 6 7 1-e10 500 ================================================ FILE: testdata/enzclass.txt ================================================ --------------------------------------------------------------------------- ENZYME nomenclature database SIB Swiss Institute of Bioinformatics; Geneva, Switzerland --------------------------------------------------------------------------- Description: ENZYME class hierarchy Name: enzclass.txt Release: 31-Jul-2019 --------------------------------------------------------------------------- 1. -. -.- Oxidoreductases. 1. 1. -.- Acting on the CH-OH group of donors. 1. 1. 1.- With NAD(+) or NADP(+) as acceptor. 1. 1. 2.- With a cytochrome as acceptor. 1. 1. 3.- With oxygen as acceptor. 1. 1. 4.- With a disulfide as acceptor. 1. 1. 5.- With a quinone or similar compound as acceptor. 1. 1. 9.- With a copper protein as acceptor. 1. 1.98.- With other, known, acceptors. 1. 1.99.- With other acceptors. 1. 2. -.- Acting on the aldehyde or oxo group of donors. 1. 2. 1.- With NAD(+) or NADP(+) as acceptor. 1. 2. 2.- With a cytochrome as acceptor. 1. 2. 3.- With oxygen as acceptor. 1. 2. 4.- With a disulfide as acceptor. 1. 2. 5.- With a quinone or similar compound as acceptor. 1. 2. 7.- With an iron-sulfur protein as acceptor. 1. 2.98.- With other, known, acceptors. 1. 2.99.- With other acceptors. 1. 3. -.- Acting on the CH-CH group of donors. 1. 3. 1.- With NAD(+) or NADP(+) as acceptor. 1. 3. 2.- With a cytochrome as acceptor. 1. 3. 3.- With oxygen as acceptor. 1. 3. 4.- With a disulfide as acceptor. 1. 3. 5.- With a quinone or related compound as acceptor. 1. 3. 7.- With an iron-sulfur protein as acceptor. 1. 3. 8.- With a flavin as acceptor. 1. 3.98.- With other, known, acceptors. 1. 3.99.- With other acceptors. 1. 4. -.- Acting on the CH-NH(2) group of donors. 1. 4. 1.- With NAD(+) or NADP(+) as acceptor. 1. 4. 2.- With a cytochrome as acceptor. 1. 4. 3.- With oxygen as acceptor. 1. 4. 4.- With a disulfide as acceptor. 1. 4. 5.- With a quinone or similar compound as acceptor. 1. 4. 7.- With an iron-sulfur protein as acceptor. 1. 4. 9.- With a copper protein as acceptor. 1. 4.98.- With other, known, acceptors. 1. 4.99.- With other acceptors. 1. 5. -.- Acting on the CH-NH group of donors. 1. 5. 1.- With NAD(+) or NADP(+) as acceptor. 1. 5. 3.- With oxygen as acceptor. 1. 5. 4.- With a disulfide as acceptor. 1. 5. 5.- With a quinone or similar compound as acceptor. 1. 5. 7.- With an iron-sulfur protein as acceptor. 1. 5. 8.- With a flavin as acceptor. 1. 5.98.- With other, known, acceptors. 1. 5.99.- With other acceptors. 1. 6. -.- Acting on NADH or NADPH. 1. 6. 1.- With NAD(+) or NADP(+) as acceptor. 1. 6. 2.- With a heme protein as acceptor. 1. 6. 3.- With oxygen as acceptor. 1. 6. 4.- With a disulfide as acceptor. 1. 6. 5.- With a quinone or similar compound as acceptor. 1. 6. 6.- With a nitrogenous group as acceptor. 1. 6. 7.- With a iron-sulfur protein as acceptor. 1. 6. 8.- With a flavin as acceptor. 1. 6.99.- With other acceptors. 1. 7. -.- Acting on other nitrogenous compounds as donors. 1. 7. 1.- With NAD(+) or NADP(+) as acceptor. 1. 7. 2.- With a cytochrome as acceptor. 1. 7. 3.- With oxygen as acceptor. 1. 7. 5.- With a quinone or similar compound as acceptor. 1. 7. 6.- With a nitrogenous group as acceptor. 1. 7. 7.- With an iron-sulfur protein as acceptor. 1. 7. 9.- With a copper protein as acceptor. 1. 7.99.- With other acceptors. 1. 8. -.- Acting on a sulfur group of donors. 1. 8. 1.- With NAD(+) or NADP(+) as acceptor. 1. 8. 2.- With a cytochrome as acceptor. 1. 8. 3.- With oxygen as acceptor. 1. 8. 4.- With a disulfide as acceptor. 1. 8. 5.- With a quinone or similar compound as acceptor. 1. 8. 6.- With an nitrogenous group as acceptor. 1. 8. 7.- With an iron-sulfur protein as acceptor. 1. 8.98.- With other, known, acceptors. 1. 8.99.- With other acceptors. 1. 9. -.- Acting on a heme group of donors. 1. 9. 3.- With oxygen as acceptor. 1. 9. 6.- With a nitrogenous group as acceptor. 1. 9.98.- With other, known, acceptors. 1. 9.99.- With other acceptors. 1.10. -.- Acting on diphenols and related substances as donors. 1.10. 1.- With NAD(+) or NADP(+) as acceptor. 1.10. 2.- With a cytochrome as acceptor. 1.10. 3.- With oxygen as acceptor. 1.10. 5.- With a quinone or related compound as acceptor. 1.10. 9.- With a copper protein as acceptor. 1.10.98.- With other, known, acceptors. 1.10.99.- With other acceptors. 1.11. -.- Acting on a peroxide as acceptor. 1.11. 1.- Peroxidases. 1.11. 2.- With H(2)O(2) as acceptor, one oxygen atom of which is incorporated into the product. 1.12. -.- Acting on hydrogen as donors. 1.12. 1.- With NAD(+) or NADP(+) as acceptor. 1.12. 2.- With a cytochrome as acceptor. 1.12. 5.- With a quinone or similar compound as acceptor. 1.12. 7.- With an iron-sulfur protein as acceptor. 1.12.98.- With other, known, acceptors. 1.12.99.- With other acceptors. 1.13. -.- Acting on single donors with incorporation of molecular oxygen (oxygenases). The oxygen incorporated need not be derived from O(2). 1.13. 1.- With NADH or NADPH as one donor. 1.13.11.- With incorporation of two atoms of oxygen. 1.13.12.- With incorporation of one atom of oxygen (internal monooxygenases or internal mixed function oxidases). 1.13.99.- Miscellaneous. 1.14. -.- Acting on paired donors, with incorporation or reduction of molecular oxygen. The oxygen incorporated need not be derived from O(2). 1.14. 1.- With NADH or NADPH as one donor. 1.14. 2.- With ascorbate as one donor. 1.14. 3.- With reduced pteridine as one donor. 1.14.11.- With 2-oxoglutarate as one donor, and incorporation of one atom each of oxygen into both donors. 1.14.12.- With NADH or NADPH as one donor, and incorporation of two atoms of oxygen into one donor. 1.14.13.- With NADH or NADPH as one donor, and incorporation of one atom of oxygen. 1.14.14.- With reduced flavin or flavoprotein as one donor, and incorporation of one atom of oxygen. 1.14.15.- With reduced iron-sulfur protein as one donor, and incorporation of one atom of oxygen. 1.14.16.- With reduced pteridine as one donor, and incorporation of one atom of oxygen. 1.14.17.- With reduced ascorbate as one donor, and incorporation of one atom of oxygen. 1.14.18.- With another compound as one donor, and incorporation of one atom of oxygen. 1.14.19.- With oxidation of a pair of donors resulting in the reduction of molecular oxygen to two molecules of water. 1.14.20.- With 2-oxoglutarate as one donor, and the other dehydrogenated. 1.14.21.- With NADH or NADPH as one donor, and the other dehydrogenated. 1.14.99.- Miscellaneous. 1.15. -.- Acting on superoxide as acceptor. 1.15. 1.- Acting on superoxide as acceptor. 1.16. -.- Oxidizing metal ions. 1.16. 1.- With NAD(+) or NADP(+) as acceptor. 1.16. 3.- With oxygen as acceptor. 1.16. 5.- With a quinone or similar compound as acceptor. 1.16. 8.- With a flavin as acceptor. 1.16. 9.- With a copper protein as acceptor. 1.16.98.- With other known acceptors. 1.17. -.- Acting on CH or CH(2) groups. 1.17. 1.- With NAD(+) or NADP(+) as acceptor. 1.17. 2.- With a cytochrome as acceptor. 1.17. 3.- With oxygen as acceptor. 1.17. 4.- With a disulfide as acceptor. 1.17. 5.- With a quinone or similar compound as acceptor. 1.17. 7.- With an iron-sulfur protein as acceptor. 1.17. 8.- With a flavin as acceptor. 1.17. 9.- With a copper protein as acceptor. 1.17.98.- With other, known, acceptors. 1.17.99.- With other acceptors. 1.18. -.- Acting on iron-sulfur proteins as donors. 1.18. 1.- With NAD(+) or NADP(+) as acceptor. 1.18. 2.- With dinitrogen as acceptor. 1.18. 3.- With H(+) as acceptor. 1.18. 6.- With dinitrogen as acceptor. 1.18.96.- With other, known, acceptors. 1.18.99.- With H(+) as acceptor. 1.19. -.- Acting on reduced flavodoxin as donor. 1.19. 1.- With NAD(+) or NADP(+) as acceptor. 1.19. 6.- With dinitrogen as acceptor. 1.20. -.- Acting on phosphorus or arsenic in donors. 1.20. 1.- With NAD(+) or NADP(+) as acceptor. 1.20. 2.- With a cytochrome as acceptor. 1.20. 4.- With disulfide as acceptor. 1.20. 9.- With a copper protein as acceptor. 1.20.98.- With other, known acceptors. 1.20.99.- With other acceptors. 1.21. -.- Catalyzing the reaction X-H + Y-H = 'X-Y'. 1.21. 1.- With NAD(+) or NADP(+) as acceptor. 1.21. 3.- With oxygen as acceptor. 1.21. 4.- With a disulfide as acceptor. 1.21.98.- With other, known acceptors. 1.21.99.- With other acceptors. 1.22. -.- Acting on halogen in donors. 1.22. 1.- With NAD(+) or NADP(+) as acceptor. 1.23. -.- Reducing C-O-C group as acceptor. 1.23. 1.- With NADH or NADPH as donor. 1.23. 5.- With a quinone or similar compound as acceptor. 1.97. -.- Other oxidoreductases. 1.97. 1.- Other oxidoreductases. 1.98. -.- Enzymes using H(2) as reductant. 1.98. 1.- Other oxidoreductases. 1.99. -.- Other enzymes using O(2) as oxidant. 1.99. 1.- Hydroxylases. 1.99. 2.- Oxygenases. 2. -. -.- Transferases. 2. 1. -.- Transferring one-carbon groups. 2. 1. 1.- Methyltransferases. 2. 1. 2.- Hydroxymethyl-, formyl- and related transferases. 2. 1. 3.- Carboxy- and carbamoyltransferases. 2. 1. 4.- Amidinotransferases. 2. 1. 5.- Methylenetransferases. 2. 2. -.- Transferring aldehyde or ketonic groups. 2. 2. 1.- Transketolases and transaldolases. 2. 3. -.- Acyltransferases. 2. 3. 1.- Transferring groups other than amino-acyl groups. 2. 3. 2.- Aminoacyltransferases. 2. 3. 3.- Acyl groups converted into alkyl groups on transfer. 2. 4. -.- Glycosyltransferases. 2. 4. 1.- Hexosyltransferases. 2. 4. 2.- Pentosyltransferases. 2. 4.99.- Transferring other glycosyl groups. 2. 5. -.- Transferring alkyl or aryl groups, other than methyl groups. 2. 5. 1.- Transferring alkyl or aryl groups, other than methyl groups. 2. 6. -.- Transferring nitrogenous groups. 2. 6. 1.- Transaminases. 2. 6. 2.- Amidinotransferases. 2. 6. 3.- Oximinotransferases. 2. 6.99.- Transferring other nitrogenous groups. 2. 7. -.- Transferring phosphorus-containing groups. 2. 7. 1.- Phosphotransferases with an alcohol group as acceptor. 2. 7. 2.- Phosphotransferases with a carboxy group as acceptor. 2. 7. 3.- Phosphotransferases with a nitrogenous group as acceptor. 2. 7. 4.- Phosphotransferases with a phosphate group as acceptor. 2. 7. 5.- Phosphotransferases with regeneration of donors, apparently catalyzing intramolecular transfers. 2. 7. 6.- Diphosphotransferases. 2. 7. 7.- Nucleotidyltransferases. 2. 7. 8.- Transferases for other substituted phosphate groups. 2. 7. 9.- Phosphotransferases with paired acceptors. 2. 7.10.- Protein-tyrosine kinases. 2. 7.11.- Protein-serine/threonine kinases. 2. 7.12.- Dual-specificity kinases (those acting on Ser/Thr and Tyr residues). 2. 7.13.- Protein-histidine kinases. 2. 7.14.- Protein-arginine kinases. 2. 7.99.- Other protein kinases. 2. 8. -.- Transferring sulfur-containing groups. 2. 8. 1.- Sulfurtransferases. 2. 8. 2.- Sulfotransferases. 2. 8. 3.- CoA-transferases. 2. 8. 4.- Transferring alkylthio groups. 2. 8. 5.- Thiosulfotransferases. 2. 9. -.- Transferring selenium-containing groups. 2. 9. 1.- Selenotransferases. 2.10. -.- Transferring molybdenum- or tungsten-containing groups. 2.10. 1.- Molybdenumtransferases or tungstentransferases with sulfide groups as acceptors. 3. -. -.- Hydrolases. 3. 1. -.- Acting on ester bonds. 3. 1. 1.- Carboxylic ester hydrolases. 3. 1. 2.- Thiolester hydrolases. 3. 1. 3.- Phosphoric monoester hydrolases. 3. 1. 4.- Phosphoric diester hydrolases. 3. 1. 5.- Triphosphoric monoester hydrolases. 3. 1. 6.- Sulfuric ester hydrolases. 3. 1. 7.- Diphosphoric monoester hydrolases. 3. 1. 8.- Phosphoric triester hydrolases. 3. 1.11.- Exodeoxyribonucleases producing 5'-phosphomonoesters. 3. 1.12.- Exodeoxyribonucleases producing 3'-phosphomonoesters. 3. 1.13.- Exoribonucleases producing 5'-phosphomonoesters. 3. 1.14.- Exoribonucleases producing 3'-phosphomonoesters. 3. 1.15.- Exonucleases active with either ribo- or deoxyribonucleic acids and producing 5'-phosphomonoesters. 3. 1.16.- Exonucleases active with either ribo- or deoxyribonucleic acids and producing 3'-phosphomonoesters. 3. 1.21.- Endodeoxyribonucleases producing 5'-phosphomonoesters. 3. 1.22.- Endodeoxyribonucleases producing other than 5'-phosphomonoesters. 3. 1.23.- Site specific endodeoxyribonucleases: cleavage is sequence specific. 3. 1.24.- Site specific endodeoxyribonucleases: cleavage is not sequence specific. 3. 1.25.- Site-specific endodeoxyribonucleases specific for altered bases. 3. 1.26.- Endoribonucleases producing 5'-phosphomonoesters. 3. 1.27.- Endoribonucleases producing other than 5'-phosphomonoesters. 3. 1.30.- Endoribonucleases active with either ribo- or deoxyribonucleic acids and producing 5'-phosphomonoesters. 3. 1.31.- Endoribonucleases active with either ribo- or deoxyribonucleic acids and producing 3'-phosphomonoesters. 3. 2. -.- Glycosylases. 3. 2. 1.- Glycosidases, i.e. enzymes hydrolyzing O- and S-glycosyl compounds. 3. 2. 2.- Hydrolyzing N-glycosyl compounds. 3. 2. 3.- Hydrolyzing S-glycosyl compounds. 3. 3. -.- Acting on ether bonds. 3. 3. 1.- Thioether and trialkylsulfonium hydrolases. 3. 3. 2.- Ether hydrolases. 3. 4. -.- Acting on peptide bonds (peptidases). 3. 4. 1.- alpha-Amino-acyl-peptide hydrolases. 3. 4. 2.- Peptidyl-amino-acid hydrolases. 3. 4. 3.- Dipeptide hydrolases. 3. 4. 4.- Peptidyl peptide hydrolases. 3. 4.11.- Aminopeptidases. 3. 4.12.- Peptidylamino-acid hydrolases or acylamino-acid hydrolases. 3. 4.13.- Dipeptidases. 3. 4.14.- Dipeptidyl-peptidases and tripeptidyl-peptidases. 3. 4.15.- Peptidyl-dipeptidases. 3. 4.16.- Serine-type carboxypeptidases. 3. 4.17.- Metallocarboxypeptidases. 3. 4.18.- Cysteine-type carboxypeptidases. 3. 4.19.- Omega peptidases. 3. 4.21.- Serine endopeptidases. 3. 4.22.- Cysteine endopeptidases. 3. 4.23.- Aspartic endopeptidases. 3. 4.24.- Metalloendopeptidases. 3. 4.25.- Threonine endopeptidases. 3. 4.99.- Endopeptidases of unknown catalytic mechanism. 3. 5. -.- Acting on carbon-nitrogen bonds, other than peptide bonds. 3. 5. 1.- In linear amides. 3. 5. 2.- In cyclic amides. 3. 5. 3.- In linear amidines. 3. 5. 4.- In cyclic amidines. 3. 5. 5.- In nitriles. 3. 5.99.- In other compounds. 3. 6. -.- Acting on acid anhydrides. 3. 6. 1.- In phosphorus-containing anhydrides. 3. 6. 2.- In sulfonyl-containing anhydrides. 3. 6. 3.- Acting on acid anhydrides; catalyzing transmembrane movement of substances. 3. 6. 4.- Acting on ATP; involved in cellular and subcellular movement. 3. 6. 5.- Acting on GTP; involved in cellular and subcellular movement. 3. 7. -.- Acting on carbon-carbon bonds. 3. 7. 1.- In ketonic substances. 3. 8. -.- Acting on halide bonds. 3. 8. 1.- In C-halide compounds. 3. 8. 2.- In P-halide compounds. 3. 9. -.- Acting on phosphorus-nitrogen bonds. 3. 9. 1.- Acting on phosphorus-nitrogen bonds. 3.10. -.- Acting on sulfur-nitrogen bonds. 3.10. 1.- Acting on sulfur-nitrogen bonds. 3.11. -.- Acting on carbon-phosphorus bonds. 3.11. 1.- Acting on carbon-phosphorus bonds. 3.12. -.- Acting on sulfur-sulfur bonds. 3.12. 1.- Acting on sulfur-sulfur bonds. 3.13. -.- Acting on carbon-sulfur bonds. 3.13. 1.- Acting on carbon-sulfur bonds. 4. -. -.- Lyases. 4. 1. -.- Carbon-carbon lyases. 4. 1. 1.- Carboxy-lyases. 4. 1. 2.- Aldehyde-lyases. 4. 1. 3.- Oxo-acid-lyases. 4. 1.99.- Other carbon-carbon lyases. 4. 2. -.- Carbon-oxygen lyases. 4. 2. 1.- Hydro-lyases. 4. 2. 2.- Acting on polysaccharides. 4. 2. 3.- Acting on phosphates. 4. 2.99.- Other carbon-oxygen lyases. 4. 3. -.- Carbon-nitrogen lyases. 4. 3. 1.- Ammonia-lyases. 4. 3. 2.- Lyases acting on amides, amidines, etc. 4. 3. 3.- Amine-lyases. 4. 3.99.- Other carbon-nitrogen lyases. 4. 4. -.- Carbon-sulfur lyases. 4. 4. 1.- Carbon-sulfur lyases. 4. 5. -.- Carbon-halide lyases. 4. 5. 1.- Carbon-halide lyases. 4. 6. -.- Phosphorus-oxygen lyases. 4. 6. 1.- Phosphorus-oxygen lyases. 4. 7. -.- Carbon-phosphorus lyases. 4. 7. 1.- Carbon-phosphorus lyases. 4.99. -.- Other lyases. 4.99. 1.- Other lyases. 5. -. -.- Isomerases. 5. 1. -.- Racemases and epimerases. 5. 1. 1.- Acting on amino acids and derivatives. 5. 1. 2.- Acting on hydroxy acids and derivatives. 5. 1. 3.- Acting on carbohydrates and derivatives. 5. 1.99.- Acting on other compounds. 5. 2. -.- Cis-trans-isomerases. 5. 2. 1.- Cis-trans isomerases. 5. 3. -.- Intramolecular oxidoreductases. 5. 3. 1.- Interconverting aldoses and ketoses. 5. 3. 2.- Interconverting keto- and enol-groups. 5. 3. 3.- Transposing C=C bonds. 5. 3. 4.- Transposing S-S bonds. 5. 3.99.- Other intramolecular oxidoreductases. 5. 4. -.- Intramolecular transferases. 5. 4. 1.- Transferring acyl groups. 5. 4. 2.- Phosphotransferases (phosphomutases). 5. 4. 3.- Transferring amino groups. 5. 4. 4.- Transferring hydroxy groups. 5. 4.99.- Transferring other groups. 5. 5. -.- Intramolecular lyases. 5. 5. 1.- Intramolecular lyases. 5. 6. -.- Isomerases altering macromolecular conformation. 5. 6. 1.- Enzymes altering polypeptide conformation or assembly. 5. 6. 2.- Enzymes altering nucleic acid conformation. 5.99. -.- Other isomerases. 5.99. 1.- Other isomerases. 6. -. -.- Ligases. 6. 1. -.- Forming carbon-oxygen bonds. 6. 1. 1.- Ligases forming aminoacyl-tRNA and related compounds. 6. 1. 2.- Acid--alcohol ligases (ester synthases). 6. 1. 3.- Cyclo-ligases. 6. 2. -.- Forming carbon-sulfur bonds. 6. 2. 1.- Acid--thiol ligases. 6. 3. -.- Forming carbon-nitrogen bonds. 6. 3. 1.- Acid--ammonia (or amine) ligases (amide synthases). 6. 3. 2.- Acid--amino-acid ligases (peptide synthases). 6. 3. 3.- Cyclo-ligases. 6. 3. 4.- Other carbon--nitrogen ligases. 6. 3. 5.- Carbon--nitrogen ligases with glutamine as amido-N-donor. 6. 4. -.- Forming carbon-carbon bonds. 6. 4. 1.- Forming carbon-carbon bonds. 6. 5. -.- Forming phosphoric ester bonds. 6. 5. 1.- Forming phosphoric ester bonds. 6. 6. -.- Forming nitrogen-metal bonds. 6. 6. 1.- Forming coordination complexes. 7. -. -.- Translocases. 7. 1. -.- Catalysing the translocation of hydrons. 7. 1. 1.- Hydron translocation or charge separation linked to oxidoreductase reactions. 7. 1. 2.- Hydron translocation linked to the hydrolysis of a nucleoside triphosphate. 7. 1. 3.- Hydron translocation linked to the hydrolysis of diphosphate. 7. 2. -.- Catalysing the translocation of inorganic cations. 7. 2. 1.- Linked to oxidoreductase reactions. 7. 2. 2.- Linked to the hydrolysis of a nucleoside triphosphate. 7. 2. 3.- Linked to the hydrolysis of diphosphate. 7. 2. 4.- Linked to decarboxylation. 7. 3. -.- Catalysing the translocation of inorganic anions and their chelates. 7. 3. 2.- Linked to the hydrolysis of a nucleoside triphosphate. 7. 4. -.- Catalysing the translocation amino acids and peptides. 7. 4. 2.- Linked to the hydrolysis of a nucleoside triphosphate. 7. 5. -.- Catalysing the translocation carbohydrates and their derivatives. 7. 5. 2.- Linked to the hydrolysis of a nucleoside triphosphate. 7. 6. -.- Catalysing the translocation of other compounds. 7. 6. 2.- Linked to the hydrolysis of a nucleoside triphosphate. --------------------------------------------------------------------------- Copyrighted by the SIB Swiss Institute of Bioinformatics and distributed under the Creative Commons Attribution (CC BY 4.0) License --------------------------------------------------------------------------- ================================================ FILE: testdata/go.obo ================================================ [File too large to display: 32.6 MB] ================================================ FILE: testdata/label_vocab.tsv ================================================ vocab_item vocab_index GO:GO:0005737 0 GO:GO:0005524 1 GO:GO:0006457 2 GO:GO:0020038 3 GO:GO:0020039 4 GO:GO:0020040 5 GO:GO:0020041 6 GO:GO:0020042 7 GO:GO:0020043 8 GO:GO:0020044 9 GO:GO:0020045 10 ================================================ FILE: testdata/test_hemoglobin.fasta ================================================ >sp|P69891|HBG1_HUMAN Hemoglobin subunit gamma-1 OS=Homo sapiens OX=9606 GN=HBG1 PE=1 SV=2 MGHFTEEDKATITSLWGKVNVEDAGGETLGRLLVVYPWTQRFFDSFGNLSSASAIMGNPK VKAHGKKVLTSLGDAIKHLDDLKGTFAQLSELHCDKLHVDPENFKLLGNVLVTVLAIHFG KEFTPEVQASWQKMVTAVASALSSRYH >sp|Q7AP54|HBP2_LISMO Hemin/hemoglobin-binding protein 2 OS=Listeria monocytogenes serovar 1/2a (strain ATCC BAA-679 / EGD-e) OX=169963 GN=hbp2 PE=1 SV=1 MKKLWKKGLVAFLALTLIFQLIPGFASAADSRLKDGGEYQVQVNFYKDNTGKTTKESSEA DKYIDHTATIKVENGQPYMYLTITNSTWWQTMAVSKNGTRPEKPAQADVYQDRYEDVQTV STDAAKDTRVEKFKLSSLDDVIFSYMHIKVDAISYDHWYQVDLTIDPSTFKVISEPAVTT PVTLSDGIYTIPFVAKKANDDSNSSMQNYFNNPAWLKVKNGKKMVAMTVNDNKTVTALKT TLAGTLQDVKVVSEDKDANTRIVEFEVEDLNQPLAAHVNYEAPFNGSVYKGQADFRYVFD TAKATAASSYPGSDETPPVVNPGETNPPVTKPDPGTTNPPVTTPPTTPSKPAVVDPKNLL NNHTYSIDFDVFKDGTTETSMMESYVMKPALIKVENNQPYVYLTLTNSSWIKTFQYKVNG VWKDMEVVSGDINKNTRTVKYPVKDGTANTDVKTHVLIEDMPGFSYDHEYTVQVKLNAAT IKDITGKDVTLKEPVKKDILNTGNVASNNNAGPKLAKPDFDDTNSVQKTASKTEKNAKTN DSSSMVWYITLFGASFLYLAYRLKRKRLS ================================================ FILE: train.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=line-too-long r"""Train a model to predict protein labels. Example use: python train.py --data_base_path=./testdata/ \ --label_vocab_path=./data/vocabs/EC.tsv \ --hparams_set=small_test_model \ --output_dir=/tmp/`date +'%s'` """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import app from absl import flags from absl import logging import pandas as pd import hparams_sets import protein_dataset import protein_model import utils import tensorflow.compat.v1 as tf flags.DEFINE_string( 'data_base_path', None, 'Directory path containing tfrecords named like "train", "dev" and "test"') flags.DEFINE_string('label_vocab_path', None, 'Relative path (from this file) to csv file of labels.') flags.DEFINE_string('output_dir', '/tmp/protein_model', 'Path to save checkpoints.') flags.DEFINE_string('hparams_set', hparams_sets.small_test_model.__name__, 'Hyperparameters to use (see hparams_sets module).') flags.DEFINE_enum( 'train_fold', protein_dataset.TRAIN_FOLD, protein_dataset.DATA_FOLD_VALUES, 'Fold to use for training data ' '(one of protein_dataset.DATA_FOLD_VALUES)') flags.DEFINE_enum( 'eval_fold', protein_dataset.TEST_FOLD, protein_dataset.DATA_FOLD_VALUES, 'Fold to use for training data ' '(one of protein_dataset.DATA_FOLD_VALUES)') FLAGS = flags.FLAGS _VOCAB_ITEM_COLUMN_NAME = 'vocab_item' _VOCAB_INDEX_COLUMN_NAME = 'vocab_index' def _make_estimator(hparams, label_vocab, output_dir): """Create a tf.estimator.Estimator. Args: hparams: tf.contrib.training.HParams. label_vocab: list of string. output_dir: str. Path to save checkpoints. Returns: tf.estimator.Estimator. """ model_fn = protein_model.make_model_fn( label_vocab=label_vocab, hparams=hparams) run_config = tf.estimator.RunConfig(model_dir=output_dir) estimator = tf.estimator.Estimator( model_fn=model_fn, params=hparams, config=run_config, ) return estimator def get_serving_input_fn(): """Create an input function for serving.""" def serving_input_fn(): """Input function for serving.""" batched_one_hot_sequences = tf.placeholder( tf.float32, shape=[None, None, len(utils.AMINO_ACID_VOCABULARY)], name='batched_one_hot_sequences_placeholder') sequence_lengths = tf.placeholder( tf.int32, shape=[None], name='sequence_length_placeholder', ) receivers = { protein_dataset.SEQUENCE_KEY: batched_one_hot_sequences, protein_dataset.SEQUENCE_LENGTH_KEY: sequence_lengths } features = { protein_dataset.SEQUENCE_KEY: batched_one_hot_sequences, protein_dataset.SEQUENCE_LENGTH_KEY: sequence_lengths } input_receiver = tf.estimator.export.ServingInputReceiver( features=features, receiver_tensors=receivers) return input_receiver return serving_input_fn def _make_estimator_and_inputs(hparams, label_vocab, data_base_path, output_dir, train_fold, eval_fold): """Makes Estimator and input_fn for train and eval. Args: hparams: tf.contrib.training.HParams. label_vocab: list of string. data_base_path: str. Directory path containing tfrecords named like "train", "dev" and "test" output_dir: str. Path to save checkpoints. train_fold: fold to use for training data (one of protein_dataset.DATA_FOLD_VALUES) eval_fold: fold to use for training data (one of protein_dataset.DATA_FOLD_VALUES) Returns: A tuple of estimator, train_spec and eval_spec """ estimator = _make_estimator( hparams=hparams, label_vocab=label_vocab, output_dir=output_dir) logging.info('Loading data from %s', data_base_path) logging.info('Writing to directory %s', output_dir) train_input_fn = protein_dataset.make_input_fn( data_file_pattern=data_base_path, batch_size=hparams.batch_size, label_vocab=label_vocab, train_dev_or_test=train_fold) train_spec = tf.estimator.TrainSpec( train_input_fn, max_steps=hparams.train_steps) eval_input_fn = protein_dataset.make_input_fn( data_file_pattern=data_base_path, batch_size=hparams.batch_size, label_vocab=label_vocab, train_dev_or_test=eval_fold) savedmodel_exporters = [ tf.estimator.LatestExporter( name='saved_model', serving_input_receiver_fn=get_serving_input_fn(), exports_to_keep=1) ] eval_spec = tf.estimator.EvalSpec( input_fn=eval_input_fn, throttle_secs=1, exporters=savedmodel_exporters, ) return estimator, train_spec, eval_spec def get_hparams(hparams_set_name): """Retrieves a tf.contrib.training.HParams from the hparam_sets module. Args: hparams_set_name: name of a function in the hparams_sets module returning a tf.contrib.training.HParams object. Returns: tf.contrib.training.HParams. """ return getattr(hparams_sets, hparams_set_name)() def parse_label_vocab(label_vocab_path): """Returns np.array of strings (labels). Args: label_vocab_path: str. Path to tsv file containing columns _VOCAB_ITEM_COLUMN_NAME and _VOCAB_INDEX_COLUMN_NAME. See testdata/label_vocab.tsv for an example. Returns: np.array of str. Labels are sorted by values in column _VOCAB_INDEX_COLUMN_NAME. """ with tf.gfile.GFile(label_vocab_path) as f: label_df = pd.read_csv(f, sep='\t') available_indexes = label_df[_VOCAB_INDEX_COLUMN_NAME].values if set(available_indexes) != set(range(len(available_indexes))): raise ValueError('Vocab indexes were not the consecutive integers between ' '0 (inclusive) and len(vocab) (exclusive). ' 'Got {}.'.format(sorted(available_indexes))) return label_df.sort_values( [_VOCAB_INDEX_COLUMN_NAME])[_VOCAB_ITEM_COLUMN_NAME].values def train(data_base_path, output_dir, label_vocab_path, hparams_set_name, train_fold, eval_fold): """Constructs trains, and evaluates a model on the given input data. Args: data_base_path: str. Directory path containing tfrecords named like "train", "dev" and "test" output_dir: str. Path to save checkpoints. label_vocab_path: str. Path to tsv file containing columns _VOCAB_ITEM_COLUMN_NAME and _VOCAB_INDEX_COLUMN_NAME. See testdata/label_vocab.tsv for an example. hparams_set_name: name of a function in the hparams module which returns a tf.contrib.training.HParams object. train_fold: fold to use for training data (one of protein_dataset.DATA_FOLD_VALUES) eval_fold: fold to use for training data (one of protein_dataset.DATA_FOLD_VALUES) Returns: A tuple of the evaluation metrics, and the exported objects from Estimator. """ hparams = get_hparams(hparams_set_name) label_vocab = parse_label_vocab(label_vocab_path) (estimator, train_spec, eval_spec) = _make_estimator_and_inputs( hparams=hparams, label_vocab=label_vocab, data_base_path=data_base_path, output_dir=output_dir, train_fold=train_fold, eval_fold=eval_fold) return tf.estimator.train_and_evaluate( estimator=estimator, train_spec=train_spec, eval_spec=eval_spec) def main(_): train( data_base_path=FLAGS.data_base_path, output_dir=FLAGS.output_dir, label_vocab_path=FLAGS.label_vocab_path, hparams_set_name=FLAGS.hparams_set, train_fold=FLAGS.train_fold, eval_fold=FLAGS.eval_fold) if __name__ == '__main__': FLAGS.alsologtostderr = True # Shows training output. flags.mark_flags_as_required(['data_base_path', 'label_vocab_path']) app.run(main) ================================================ FILE: train_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for train.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import tempfile from absl import flags from absl.testing import parameterized import numpy as np import hparams_sets import protein_dataset import train import tensorflow.compat.v1 as tf FLAGS = flags.FLAGS class TrainTest(parameterized.TestCase): def setUp(self): super(TrainTest, self).setUp() self._test_data_directory = os.path.join( FLAGS.test_srcdir, './testdata' ) def test_parse_label_vocab(self): label_vocab_path = os.path.join(self._test_data_directory, 'label_vocab.tsv') actual = train.parse_label_vocab(label_vocab_path) expected = np.array([ 'GO:GO:0005737', 'GO:GO:0005524', 'GO:GO:0006457', 'GO:GO:0020038', 'GO:GO:0020039', 'GO:GO:0020040', 'GO:GO:0020041', 'GO:GO:0020042', 'GO:GO:0020043', 'GO:GO:0020044', 'GO:GO:0020045' ]) np.testing.assert_array_equal(actual, expected) def test_train_gives_non_nan_loss(self): output_dir = tempfile.mkdtemp('test_model_output') evaluation_results, export_results = train.train( data_base_path=self._test_data_directory, output_dir=output_dir, label_vocab_path=os.path.join(self._test_data_directory, 'label_vocab.tsv'), hparams_set_name=hparams_sets.small_test_model.__name__, train_fold=protein_dataset.TRAIN_FOLD, eval_fold=protein_dataset.TEST_FOLD) self.assertTrue(np.isfinite(evaluation_results['loss'])) saved_model_path = export_results[0] # Check we can load the saved_model without exceptions: with tf.Session(graph=tf.Graph()) as sess: tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], saved_model_path) label_vocab = tf.get_default_graph().get_tensor_by_name('label_vocab:0') self.assertLen(label_vocab.shape, 1) decision_threshold = sess.run( tf.get_default_graph().get_tensor_by_name('decision_threshold:0')) self.assertGreater(decision_threshold, 0) self.assertLess(decision_threshold, 1) if __name__ == '__main__': tf.test.main() ================================================ FILE: utils.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utility functions for protein models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import gzip import json import os import tarfile from typing import (Callable, List, Optional, Text) import urllib import numpy as np import tensorflow.compat.v1 as tf # tf from tensorflow.contrib import lookup as contrib_lookup import tqdm AMINO_ACID_VOCABULARY = [ 'A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y' ] _PFAM_GAP_CHARACTER = '.' # Other characters representing amino-acids not in AMINO_ACID_VOCABULARY. _ADDITIONAL_AA_VOCABULARY = [ # Substitutions 'U', 'O', # Ambiguous Characters 'B', 'Z', 'X', # Gap Character _PFAM_GAP_CHARACTER ] # Vocab of all possible tokens in a valid input sequence FULL_RESIDUE_VOCAB = AMINO_ACID_VOCABULARY + _ADDITIONAL_AA_VOCABULARY # Map AA characters to their index in FULL_RESIDUE_VOCAB. _RESIDUE_TO_INT = {aa: idx for idx, aa in enumerate(FULL_RESIDUE_VOCAB)} OSS_ZIPPED_MODELS_ROOT_URL = 'https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/models/zipped_models/' _OSS_PFAM_ZIPPED_MODELS_URL_BASE = OSS_ZIPPED_MODELS_ROOT_URL + 'noxpd2_cnn_swissprot_pfam_random_swiss-cnn_for_swissprot_pfam_random-' _OSS_EC_ZIPPED_MODELS_URL_BASE = OSS_ZIPPED_MODELS_ROOT_URL + 'noxpd2_cnn_swissprot_ec_random_swiss-cnn_for_swissprot_ec_random-' _OSS_GO_ZIPPED_MODELS_URL_BASE = OSS_ZIPPED_MODELS_ROOT_URL + 'noxpd2_cnn_swissprot_go_random_swiss-cnn_for_swissprot_go_random-' MAX_NUM_ENSEMBLE_ELS_FOR_INFERENCE = 5 PARENTHOOD_FILE_URL = 'https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/parenthood.json.gz' LABEL_DESCRIPTION_URL = 'https://storage.googleapis.com/brain-genomics-public/research/proteins/proteinfer/colab_support/label_descriptions.json.gz' INSTALLED_PARENTHOOD_FILE_NAME = 'parenthood.json.gz' INSTALLED_LABEL_DESCRIPTION_FILE_NAME = 'label_descriptions.json.gz' # pyformat: disable PFAM_RANDOM_ENSEMBLE_ELEMENT_EXPERIMENT_IDS = [ '13703743', '13703976', '13704038', '13704097', '13704156', '13705318', '13705635', '13705680', '13705733', '13705759', '13705805', '13706336', '13707555', '13707708', '13707739', '13707862', '13708715', '13708866', '13709033', '13709258', '13709363', '13709600', '13709998', '13710430', '13711765', '13729975', '13730021', '13730128', '13730776', '13730885', '13731191', '13731551', '13731565', '13731695', '13732031', ] EC_RANDOM_ENSEMBLE_ELEMENT_EXPERIMENT_IDS = [ '13703966', '13704083', '13704104', '13704130', '13705280', '13705675', '13705786', '13705802', '13705819', '13705839', '13706239', '13706986', '13707020', '13707589', '13707925', '13708369', '13708672', '13708706', '13708740', '13708951', '13709242', '13709584', '13709983', '13710037', '13711670', '13729344', '13730041', '13730097', '13730679', '13730876', '13730909', '13731218', '13731588', '13731728', '13731976', ] GO_RANDOM_ENSEMBLE_ELEMENT_EXPERIMENT_IDS = [ '13703706', '13703742', '13703997', '13704131', '13705631', '13705668', '13705677', '13705689', '13705708', '13705728', '13706170', '13706215', '13707414', '13707438', '13707732', '13708169', '13708676', '13708925', '13708995', '13709052', '13709428', '13709589', '13710370', '13710418', '13711677', '13729352', '13730011', '13730387', '13730746', '13730766', '13730958', '13731179', '13731598', '13731645', '13732022', ] # pyformat: enable OSS_PFAM_ZIPPED_MODELS_URLS = [ '{}{}.tar.gz'.format(_OSS_PFAM_ZIPPED_MODELS_URL_BASE, p) for p in PFAM_RANDOM_ENSEMBLE_ELEMENT_EXPERIMENT_IDS ] OSS_EC_ZIPPED_MODELS_URLS = [ '{}{}.tar.gz'.format(_OSS_EC_ZIPPED_MODELS_URL_BASE, p) for p in EC_RANDOM_ENSEMBLE_ELEMENT_EXPERIMENT_IDS ] OSS_GO_ZIPPED_MODELS_URLS = [ '{}{}.tar.gz'.format(_OSS_GO_ZIPPED_MODELS_URL_BASE, p) for p in GO_RANDOM_ENSEMBLE_ELEMENT_EXPERIMENT_IDS ] def residues_to_indices(amino_acid_residues): return [_RESIDUE_TO_INT[c] for c in amino_acid_residues] def normalize_sequence_to_blosum_characters(seq): """Make substitutions, since blosum62 doesn't include amino acids U and O. We take the advice from here for the appropriate substitutions: https://www.cgl.ucsf.edu/chimera/docs/ContributedSoftware/multalignviewer/multalignviewer.html Args: seq: amino acid sequence. A string. Returns: An amino acid sequence string that's compatible with the blosum substitution matrix. """ return seq.replace('U', 'C').replace('O', 'X') @functools.lru_cache(maxsize=1) def _build_one_hot_encodings(): """Create array of one-hot embeddings. Row `i` of the returned array corresponds to the one-hot embedding of amino acid FULL_RESIDUE_VOCAB[i]. Returns: np.array of shape `[len(FULL_RESIDUE_VOCAB), 20]`. """ base_encodings = np.eye(len(AMINO_ACID_VOCABULARY)) to_aa_index = AMINO_ACID_VOCABULARY.index special_mappings = { 'B': .5 * (base_encodings[to_aa_index('D')] + base_encodings[to_aa_index('N')]), 'Z': .5 * (base_encodings[to_aa_index('E')] + base_encodings[to_aa_index('Q')]), 'X': np.ones(len(AMINO_ACID_VOCABULARY)) / len(AMINO_ACID_VOCABULARY), _PFAM_GAP_CHARACTER: np.zeros(len(AMINO_ACID_VOCABULARY)), } special_mappings['U'] = base_encodings[to_aa_index('C')] special_mappings['O'] = special_mappings['X'] special_encodings = np.array( [special_mappings[c] for c in _ADDITIONAL_AA_VOCABULARY]) return np.concatenate((base_encodings, special_encodings), axis=0) def residues_to_one_hot(amino_acid_residues): """Given a sequence of amino acids, return one hot array. Supports ambiguous amino acid characters B, Z, and X by distributing evenly over possible values, e.g. an 'X' gets mapped to [.05, .05, ... , .05]. Supports rare amino acids by appropriately substituting. See normalize_sequence_to_blosum_characters for more information. Supports gaps and pads with the '.' and '-' characters; which are mapped to the zero vector. Args: amino_acid_residues: string. consisting of characters from AMINO_ACID_VOCABULARY Returns: A numpy array of shape (len(amino_acid_residues), len(AMINO_ACID_VOCABULARY)). Raises: KeyError: if amino_acid_residues has a character not in FULL_RESIDUE_VOCAB. """ residue_encodings = _build_one_hot_encodings() int_sequence = residues_to_indices(amino_acid_residues) return residue_encodings[int_sequence] def fasta_indexer(): """Get a function for converting tokenized protein strings to indices.""" mapping = tf.constant(FULL_RESIDUE_VOCAB) table = contrib_lookup.index_table_from_tensor(mapping) def mapper(residues): return tf.ragged.map_flat_values(table.lookup, residues) return mapper def fasta_encoder(): """Get a function for converting indexed amino acids to one-hot encodings.""" encoded = residues_to_one_hot(''.join(FULL_RESIDUE_VOCAB)) one_hot_embeddings = tf.constant(encoded, dtype=tf.float32) def mapper(residues): return tf.ragged.map_flat_values( tf.gather, indices=residues, params=one_hot_embeddings) return mapper def in_graph_residues_to_onehot(residues): """Performs mapping in `residues_to_one_hot` in-graph. Args: residues: A tf.RaggedTensor with tokenized residues. Returns: A tuple of tensors (one_hots, row_lengths): `one_hots` is a Tensor that contains a one_hot encoding of the residues and pads out all the residues to the max sequence length in the batch by 0s. `row_lengths` is a Tensor with the length of the unpadded sequences from residues. Raises: tf.errors.InvalidArgumentError: if `residues` contains a token not in `FULL_RESIDUE_VOCAB`. """ ragged_one_hots = fasta_encoder()(fasta_indexer()(residues)) return (ragged_one_hots.to_tensor(default_value=0), tf.cast(ragged_one_hots.row_lengths(), dtype=tf.int32)) def calculate_bucket_batch_sizes(bucket_boundaries, max_expected_sequence_size, largest_batch_size): """Calculated batch sizes for each bucket given a set of boundaries. Sequences in the smallest sized bucket will get a batch_size of largest_batch_size and larger buckets will have smaller batch sizes in proportion to their maximum sequence length to ensure that they do not use too much memory. E.g. for bucket_boundaries of [5, 10, 20, 40], max_expected_size of 100 and largest_batch_size of 50, expected_bucket_sizes are [50, 25, 12, 6, 2]. Args: bucket_boundaries: list of positions of bucket boundaries max_expected_sequence_size: largest expected sequence, used to calculate sizes largest_batch_size: batch_size for largest batches. Returns: batch_sizes as list """ first_max_size = bucket_boundaries[0] bucket_relative_batch_sizes = [ (first_max_size / x) for x in bucket_boundaries + [max_expected_sequence_size] ] bucket_absolute_batch_sizes = [ int(x * largest_batch_size) for x in bucket_relative_batch_sizes ] if min(bucket_absolute_batch_sizes) == 0: raise ValueError( 'There would be a batch size of 0 during bucketing, which is not ' 'allowed. Bucket boundaries passed in were: %s, leading to batch sizes of: %s' % (bucket_boundaries, bucket_absolute_batch_sizes)) return bucket_absolute_batch_sizes def batch_iterable(iterable, batch_size): """Yields batches from an iterable. If the number of elements in the iterator is not a multiple of batch size, the last batch will have fewer elements. Args: iterable: a potentially infinite iterable. batch_size: the size of batches to return. Yields: array of length batch_size, containing elements, in order, from iterable. Raises: ValueError: if batch_size < 1. """ if batch_size < 1: raise ValueError( 'Cannot have a batch size of less than 1. Received: {}'.format( batch_size)) current = [] for item in iterable: if len(current) == batch_size: yield current current = [] current.append(item) # Prevent yielding an empty batch. Instead, prefer to end the generation. if current: yield current def pad_one_hot(one_hot, length): if length < one_hot.shape[0]: raise ValueError("The padding value must be longer than the one-hot's 0th " 'dimension. Padding value is ' + str(length) + ' ' 'and one-hot shape is ' + str(one_hot.shape)) padding = np.zeros((length - one_hot.shape[0], len(AMINO_ACID_VOCABULARY))) return np.append(one_hot, padding, axis=0) def make_padded_np_array(ragged_arrays): """Converts ragged array of one-hot amino acids to constant-length np.array. Args: ragged_arrays: list of list of int. Each entry in the list is a one-hot encoded protein, where each entry corresponds to an amino acid. Returns: np.array of int, shape (len(ragged_arrays), len(longest_array_in_ragged_arrays), len(AMINO_ACID_VOCABULARY)). """ max_array_length = max(len(a) for a in ragged_arrays) return np.array([ pad_one_hot(ragged_array, max_array_length) for ragged_array in ragged_arrays ]) def absolute_paths_of_files_in_dir(dir_path): files = os.listdir(dir_path) return sorted([os.path.join(dir_path, f) for f in files]) def load_gz_json(path): with open(path, 'rb') as f: with gzip.GzipFile(fileobj=f, mode='rb') as gzip_file: return json.load(gzip_file) def fetch_oss_pretrained_models( model_type, output_dir_path, num_ensemble_elements = None): """Fetch, unzip, and untar a number of models to output_dir_path. Does not store the tar.gz versions, just the unzipped ones. Args: model_type: one of Pfam, EC, or GO. output_dir_path: output directory to which ensemble elements should be written. num_ensemble_elements: number of elements to fetch. If None, fetch all available. Raises: ValueError if model_type is invalid, or num_ensemble_elements is too large. """ if model_type.lower() == 'pfam': absolute_model_urls = OSS_PFAM_ZIPPED_MODELS_URLS elif model_type.lower() == 'ec': absolute_model_urls = OSS_EC_ZIPPED_MODELS_URLS elif model_type.lower() == 'go': absolute_model_urls = OSS_GO_ZIPPED_MODELS_URLS else: raise ValueError( 'Given model type {} was not valid. Valid model types are {}'.format( model_type, ['Pfam', 'EC', 'GO'])) num_ensemble_elements = num_ensemble_elements if num_ensemble_elements is not None else len( absolute_model_urls) if num_ensemble_elements > len(absolute_model_urls): raise ValueError( 'Requested {} ensemble elements, but only {} were available.'.format( num_ensemble_elements, len(absolute_model_urls))) absolute_model_urls = absolute_model_urls[:num_ensemble_elements] for absolute_url in tqdm.tqdm( absolute_model_urls, desc='Downloading and unzipping {} models to {}'.format( model_type, output_dir_path), position=0, leave=True): # TODO(mlbileschi): consider parallelizing to make faster. relative_file_name = os.path.basename(os.path.normpath(absolute_url)) output_path = os.path.join(output_dir_path, relative_file_name) with urllib.request.urlopen(absolute_url) as url_contents: with tarfile.open(fileobj=url_contents, mode='r|gz') as tar: tar.extractall(output_dir_path) ================================================ FILE: utils_test.py ================================================ # coding=utf-8 # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for utils.py.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl.testing import parameterized import numpy as np import utils import tensorflow.compat.v1 as tf class TestTensorUtils(parameterized.TestCase): def testBucketSize(self): max_expected_size = 100 bucket_boundaries = [5, 10, 20, 40] largest_batch_size = 50 expected_bucket_sizes = [50, 25, 12, 6, 2] actual_bucket_sizes = utils.calculate_bucket_batch_sizes( bucket_boundaries, max_expected_size, largest_batch_size) self.assertEqual(expected_bucket_sizes, actual_bucket_sizes) @parameterized.parameters( dict(input_iterable=[], batch_size=1, expected=[]), dict(input_iterable=[], batch_size=2, expected=[]), dict(input_iterable=[1], batch_size=1, expected=[[1]]), dict(input_iterable=[1], batch_size=2, expected=[[1]]), dict(input_iterable=[1, 2], batch_size=1, expected=[[1], [2]]), dict(input_iterable=[1, 2], batch_size=2, expected=[[1, 2]]), dict(input_iterable=[1, 2, 3], batch_size=2, expected=[[1, 2], [3]]), dict( input_iterable=[1, 2, 3, 4], batch_size=2, expected=[[1, 2], [3, 4]]), ) def testBatchIterable(self, input_iterable, batch_size, expected): actual = list(utils.batch_iterable(input_iterable, batch_size)) self.assertEqual(actual, expected) def testSparseToOneHot(self): seq = 'AY' expected_output = [[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1. ]] self.assertListEqual(expected_output, utils.residues_to_one_hot(seq).tolist()) @parameterized.named_parameters( dict( testcase_name='pad by nothing', input_one_hot=np.array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ]]), pad_length=1, expected=np.array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ]])), dict( testcase_name='pad with one element', input_one_hot=np.array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ]]), pad_length=2, expected=np.array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ], [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0. ]])), ) def testPadOneHotSameLength(self, input_one_hot, pad_length, expected): actual = utils.pad_one_hot(input_one_hot, pad_length) self.assertTrue( np.allclose(actual, expected), msg='Actual: ' + str(actual) + '\nExpected: ' + str(expected)) def test_absolute_paths_of_files_in_dir(self): test_dir = self.create_tempdir().full_path file_to_create = os.path.join(test_dir, 'a_file.txt') with open(file_to_create, 'w'): pass expected = [file_to_create] actual = utils.absolute_paths_of_files_in_dir(test_dir) self.assertCountEqual(actual, expected) if __name__ == '__main__': tf.test.main()