[ { "path": ".gitignore", "content": "*.o\nMakefile\nMakefile.in\naclocal.m4\nautom4te.cache\nbuild-aux\nconfig.h\nconfig.h.in\nconfig.log\nconfig.status\nconfigure\n.deps\nMakefile.in\ncompress_gtf\ncuffcompare\ncuffdiff\ncufflinks\ncuffnorm\ncuffquant\ngffread\ngtf_to_sam\n*.a\nstamp-h1\n" }, { "path": "AUTHORS", "content": "Cufflinks authors\nPrimary contact \n\nCole Trapnell designed and wrote Cufflinks and Cuffdiff, with substantial technical input \nfrom Geo Pertea, Brian Williams, Ali Mortazavi, Jeltje van Baren, Steven Salzberg, Barbara Wold, \nand Lior Pachter. Geo Pertea wrote Cuffcompare. Adam Roberts made substantial improvements to\nthe abundance estimation model used by Cufflinks and Cuffdiff.\n\n\"Tuxedo\" Websites:\n Cufflinks:\t\t\t http://cole-trapnell-lab.github.io/cufflinks/\n TopHat:\t\t\t http://tophat.cbcb.umd.edu\n Bowtie: http://bowtie-bio.sf.net\n\nAs of version 1.0, Cufflinks depends on and includes LOCFIT, a regression package originally written \nby Catherine Loader and Jiayang Sun. Some modifications were made to LOCFIT. Modified source\nfor LOCFIT lives in src/locfit.\n" }, { "path": "LICENSE", "content": "Copyright (C) 2003-2009 Cole Trapnell et al\n\n===========================================================================\nBoost Software License, Version 1.0\n===========================================================================\n\nPermission is hereby granted, free of charge, to any person or organization\nobtaining a copy of the software and accompanying documentation covered by\nthis license (the \"Software\") to use, reproduce, display, distribute,\nexecute, and transmit the Software, and to prepare derivative works of the\nSoftware, and to permit third-parties to whom the Software is furnished to\ndo so, all subject to the following:\n\nThe copyright notices in the Software and this entire statement, including\nthe above license grant, this restriction and the following disclaimer,\nmust be included in all copies of the Software, in whole or in part, and\nall derivative works of the Software, unless such copies or derivative\nworks are solely in the form of machine-executable object code generated by\na source language processor.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT\nSHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE\nFOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,\nARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\nDEALINGS IN THE SOFTWARE.\n\n\n" }, { "path": "Makefile.am", "content": "\nALWAYS_BUILT = src\nSUBDIRS = $(ALWAYS_BUILT)\nDIST_SUBDIRS = $(ALWAYS_BUILT) \n\nEXTRA_DIST = LICENSE\n\n.PHONY: FORCE\n" }, { "path": "README.md", "content": "\n# Cufflinks\n\nThe main *website* for cufflinks is [here](http://cole-trapnell-lab.github.io/cufflinks/)\n\n*NOTE*: If you're looking for old releases of Cufflinks, including source, you can find them [here](http://cole-trapnell-lab.github.io/cufflinks/install/).\n\nCufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one, taking into account biases in library preparation protocols. \n\nCufflinks was originally developed as part of a collaborative effort between the [Laboratory for Mathematical and Computational Biology](http://bio.math.berkeley.edu/), led by Lior Pachter at UC Berkeley, Steven Salzberg's [computational genomics group](http://ccb.jhu.edu/people/salzberg/) at the Institute of Genetic Medicine at Johns Hopkins University, and [Barbara Wold's lab](http://woldlab.caltech.edu/) at Caltech. The project is now maintained by [Cole Trapnell's](http://cole-trapnell-lab.github.io/) lab at the University of Washington.\n\nCufflinks is provided under the OSI-approved [Boost License](http://en.wikipedia.org/wiki/Boost_Software_License)\n\n# News\n\n*To get the latest updates on the Cufflinks project and the rest of the \"Tuxedo tools\", please subscribe to our [**mailing list**](https://lists.sourceforge.net/lists/listinfo/bowtie-bio-announce)* \n\n# Install quick-start\n\n## Installing a pre-compiled binary release\n\nIn order to make it easy to install Cufflinks, we provide a few binary packages [here](http://cole-trapnell-lab.github.io/cufflinks/install/) to save users from the occasionally frustrating process of building Cufflinks, which requires that you install the Boost libraries. To use the binary packages, simply download the appropriate one for your machine, untar it, and make sure the cufflinks,cuffdiff and cuffcompare binaries are in a directory in your PATH environment variable.\n\n# Building Cufflinks from source\n\nIn order to build Cufflinks, you must have the [Boost C++ libraries](http://www.boost.org/) (version 1.47 or higher) installed on your system. See below for instructions on installing Boost.\n\n## Installing Boost\n\n1. Download Boost and the bjam build engine. **WARNING:** Due to a serious issue with Boost Serlialization library introduced in version 1.56, Cufflinks currently can only be built with Boost version 1.55 or lower. The issue is expected to be fixed in the upcoming Boost v1.59.\n2. Unpack bjam and add it to your PATH.\n3. Unpack the Boost tarball and cd to the Boost source directory. This directory is called the BOOST_ROOT in some Boost installation instructions.\n4. Build Boost. Note that you can specify where to put Boost with the --prefix option. The default Boost installation directory is /usr/local. Take note of the boost installation directory, because you will need to tell the Cufflinks installer where to find Boost later on.\n\n- If you are on Mac OS X, type (all on one line): \n```bash\nbjam --prefix= --toolset=darwin architecture=x86 address_model=32_64 link=static runtime-link=static --layout=versioned stage install\n```\n\n- If you are on a 32-bit Linux system, type (all on one line): \n```bash\nbjam --prefix= --toolset=gcc architecture=x86 address_model=32 link=static runtime-link=static stage install\n```\n\n- If you are on a 64-bit Linux system, type (all on one line): \n```bash\nbjam --prefix= --toolset=gcc architecture=x86 address_model=64 link=static runtime-link=static stage install\n```\n\n## Installing the SAM tools\n\n1. [Download the SAM tools](http://samtools.sourceforge.net/)\n2. Unpack the SAM tools tarball and cd to the SAM tools source directory.\n3. Build the SAM tools by typing make at the command line.\n4. Choose a directory into which you wish to copy the SAM tools binary, the included library libbam.a, and the library headers. A common choice is /usr/local/.\n5. Copy libbam.a to the lib/ directory in the folder you've chosen above (e.g. /usr/local/lib/)\n6. Create a directory called \"bam\" in the include/ directory (e.g. /usr/local/include/bam)\n7. Copy the headers (files ending in .h) to the include/bam directory you've created above (e.g. /usr/local/include/bam)\n8. Copy the samtools binary to some directory in your PATH.\n\n## Installing the Eigen libraries\n\n1. [Download Eigen](http://eigen.tuxfamily.org/)\n2. Unpack the Eigen tarball and cd to the Eigen source directory.\n3. Copy the Eigen/ subdirectory someplace on your system where you keep header files (e.g. /usr/local/include)\n\n## Building Cufflinks\n\n### If you are starting from a source tarball downloaded from [here](http://cole-trapnell-lab.github.io/cufflinks/install/):\n\nUnpack the Cufflinks source tarball (in this example for version 2.2.1):\n```bash\ntar zxvf cufflinks-2.2.1.tar.gz\n```\nChange to the Cufflinks directory:\n```bash\ncd cufflinks-2.2.1\n```\n\n### If you want to clone the Cufflinks github repo:\n```bash\ngit clone https://github.com/cole-trapnell-lab/cufflinks.git\ncd cufflinks\nautoreconf --install\n```\nThe above will generate the configure script.\n\n### To configure Cufflinks prior to the build\n\nIf Boost is installed somewhere other than /usr/local, you will need to tell the installer where to find it using the --with-boost option. Specify where to install Cufflinks using the --prefix option.\n```bash\n./configure --prefix=/path/to/cufflinks/install --with-boost=/path/to/boost --with-eigen=/path/to/eigen\n```\n\nIf you see any errors during configuration, verify that you are using Boost version 1.47 or higher, and that the directory you specified via --with-boost contains the boost header files and libraries. See the Boost Getting started page for more details. If you copied the SAM tools binaries to someplace other than /usr/local/, you may need to supply the --with-bam configuration option.\nFinally, make and install Cufflinks.\n```bash\nmake\nmake install\n```\n\n## Testing the installation\n\n1. [Download](http://cufflinks.cbcb.umd.edu/downloads/test_data.sam) the test data\n2. In the directory where you placed the test file, type:\n\n```bash\ncufflinks ./test_data.sam\n```\n\nYou should see the following output:\n\n
\n[bam_header_read] EOF marker is absent. The input is probably truncated.\n[bam_header_read] invalid BAM binary header (this is not a BAM file).\nFile ./test_data.sam doesn't appear to be a valid BAM file, trying SAM...\n[13:23:15] Inspecting reads and determining fragment length distribution.\n> Processed 1 loci.                            [*************************] 100%\nWarning: Using default Gaussian distribution due to insufficient paired-end reads in open ranges.  \nIt is recommended that correct paramaters (--frag-len-mean and --frag-len-std-dev) be provided.\n> Map Properties:\n>       Total Map Mass: 102.50\n>       Read Type: 75bp x 75bp\n>       Fragment Length Distribution: Truncated Gaussian (default)\n>                     Estimated Mean: 200\n>                  Estimated Std Dev: 80\n[13:23:15] Assembling transcripts and estimating abundances.\n> Processed 1 loci.                            [*************************] 100%\n
\n\nVerify that the file transcripts.gtf is in the current directory and looks like this (your file will have GTF attributes, omitted here for clarity)\n\n
\ntest_chromosome Cufflinks       exon    53      250     1000    +       . \ntest_chromosome Cufflinks       exon    351     400     1000    +       . \ntest_chromosome Cufflinks       exon    501     550     1000    +       .\n
\t\n\n# Common uses of the Cufflinks package\n\nCufflinks includes a number of tools for analyzing RNA-Seq experiments. Some of these tools can be run on their own, while others are pieces of a larger workflow. The complexity of your workflow depends on what you want to achieve with your analysis. For a complete discussion of how Cufflinks can help you with your analysis, please [see our protocol paper](http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html). The paper includes a diagram (Figure 2) describing how the various parts of the Cufflinks package (and its companion tool TopHat) fit together. As of version 2.2.0, you can also run Cuffquant and Cuffnorm to make large scale analyses easier to handle. The figure below is an updated version of Figure 2 showing how the two utilities released after the protocol paper appeared fit into the workflow: \n\n
\n![Workflow]({{ site.url }}/images/tuxedo_workflow.png)\n
\n\nYou can use Cuffquant to pre-compute gene expression levels for each of your samples, which can save time if you have to re-run part of your analysis. Using Cuffquant also makes it easier to spread the load of computation for lots of samples across multiple computers. If you don't want to perform differential expression analysis, you can run Cuffnorm instead of Cuffdiff. Cuffnorm produces simple tables of expression values that you can look at in R (for example) to cluster samples and perform other follow up analysis.\t\n\n# Using pre-built annotation packages\n\nA number of steps in the Tuxedo package work better if you have pre-existing gene annotations. How you can use these annotations is detailed in our [protocol paper](http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html). Illumina has kindly provided a large number of annotation packages for commonly used model organisms and humans. You can find these packages [here]({{ site.url }}/igenome_table/index.html).\t\n\n# References\n\nCufflinks is an ongoing research project as well as a suite of tools. Here are the papers that describe the science behind the programs. If you use Cufflinks, please cite these papers in your work!\n\n**Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation**\nCole Trapnell, Brian Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Jeltje van Baren, Steven Salzberg, Barbara Wold, Lior Pachter.\n*Nature Biotechnology*, 2010, doi:10.1038/nbt.1621\n*Note: This is the original Cufflinks paper. Please cite this paper if you use Cufflinks in your work.*\n\n**Improving RNA-Seq expression estimates by correcting for fragment bias**\nAdam Roberts, Cole Trapnell, Julie Donaghey, John L. Rinn, Lior Pachter.\n*Genome Biology*, 2011, doi:10.1186/gb-2011-12-3-r22\n*Note: This paper describes improvements made to Cufflinks to handle bias in RNA-Seq read coverage. Please cite this paper if you use Cufflinks with the -b option in your work.*\n\n**Identification of novel transcripts in annotated genomes using RNA-Seq**\nAdam Roberts, Harold Pimentel, Cole Trapnell, Lior Pachter.\n*Bioinformatics*, 2011, doi:10.1093/bioinformatics/btr355\n*Note: This paper describes the RABT assembly algorithm. Please cite this paper if you use Cufflinks in RABT mode in your work.*\n\n**Differential analysis of gene regulation at transcript resolution with RNA-seq**\nCole Trapnell, David Hendrickson, Martin Sauvageau, Loyal Goff, John L. Rinn, Lior Pachter\n*Nature Biotechnology*, 2012, doi:10.1038/nbt.2450\n*Note: This paper describes Cuffdiff 2. Please cite this paper if you use Cuffdiff in your work.*\n\nCufflinks builds on many ideas, including some\nproposed in the following papers:\n\n1. Ali Mortazavi, Brian A Williams, Kenneth McCue, Lorian Schaeffer and Barbara \nWold, \"Mapping and quantifying mammalian transcriptomes by RNA-Seq\",Nature \nMethods, volume 5, 621 - 628 (2008)\n2. Hui Jiang and Wing Hung Wong, \"Statistical Inferences for isoform expression\", \nBioinformatics, 2009 25(8):1026-1032=\n3.Nicholas Eriksson, Lior Pachter, Yumi Mitsuya, Soo-Yon Rhee, Chunlin Wang, \nBaback Gharizadeh, Mostafa Ronaghi, Robert W. Shafer, Niko Beerenwinkel, \"Viral \npopulation estimation using pyrosequencing\", PLoS Computational Biology, \n4(5):e1000074\n" }, { "path": "autogen.sh", "content": "#!/bin/sh\n\nsubmodule_init ( ) {\n local SUBMODULES=$(git submodule | awk '{print $2}')\n for submodule in $SUBMODULES; do\n\techo \"Initializing submodule $submodule\"\n\tgit submodule init $submodule\n\tgit submodule update $submodule\n\tcd $submodule\n\tif test -f version.sh\n\tthen\n\t ./version.sh\n\tfi\n\tsubmodule_init\n\tcd ..\n done\n}\n\n./version.sh\nsubmodule_init\nautoreconf --install\n" }, { "path": "ax_bam.m4", "content": "# SYNOPSIS\n#\n# AX_BAM\n#\n# DESCRIPTION\n#\n# Test for the BAM libraries (htslib or bamlib)\n#\n# If no path to the installed bam library is given the macro searchs\n# under /usr, /usr/local, /opt and /opt/local and evaluates the\n# $BAM_ROOT environment variable.\n#\tAdapted from AX_BOOST_BASE\n#\n# This macro calls:\n#\n# AC_SUBST(BAM_CPPFLAGS) / AC_SUBST(BAM_LDFLAGS)\n#\n# And sets:\n#\n# HAVE_BAM, HAVE_HTSLIB\n#\n# LICENSE\n#\n# Copyright (c) 2010 Cole Trapnell \n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved.\n\nAC_DEFUN([AX_BAM],\n[\nAC_ARG_WITH([bam],\n\tAS_HELP_STRING([--with-bam@<:@=DIR@:>@], [use BAM libraries (default is yes) - it is possible to specify the root directory for BAM (optional)]),\n\t[\n if test \"$withval\" = \"no\"; then\n\t\twant_bam=\"no\"\n elif test \"$withval\" = \"yes\"; then\n want_bam=\"yes\"\n ac_bam_path=\"\"\n else\n\t want_bam=\"yes\"\n ac_bam_path=\"$withval\"\n\tfi\n ],\n [want_bam=\"yes\"])\n\n\nAC_ARG_WITH([bam-libdir],\n AS_HELP_STRING([--with-bam-libdir=LIB_DIR],\n [Force given directory for bam libraries. Note that this will overwrite library path detection, so use this parameter only if default library detection fails and you know exactly where your bam libraries are located.]),\n [\n if test -d $withval\n then\n ac_bam_lib_path=\"$withval\"\n else\n AC_MSG_ERROR(--with-bam-libdir expected directory name)\n fi\n ],\n [ac_bam_lib_path=\"\"]\n)\n\nif test \"x$want_bam\" = \"xyes\"; then\n#\tbam_lib_version_req=ifelse([$1], ,1.20.0,$1)\n#\tbam_lib_version_req_shorten=`expr $bam_lib_version_req : '\\([[0-9]]*\\.[[0-9]]*\\)'`\n#\tbam_lib_version_req_major=`expr $bam_lib_version_req : '\\([[0-9]]*\\)'`\n#\tbam_lib_version_req_minor=`expr $bam_lib_version_req : '[[0-9]]*\\.\\([[0-9]]*\\)'`\n#\tbam_lib_version_req_sub_minor=`expr $bam_lib_version_req : '[[0-9]]*\\.[[0-9]]*\\.\\([[0-9]]*\\)'`\n#\tif test \"x$bam_lib_version_req_sub_minor\" = \"x\" ; then\n#\t\tbam_lib_version_req_sub_minor=\"0\"\n# \tfi\n#\tWANT_BAM_VERSION=`expr $bam_lib_version_req_major \\* 100000 \\+ $bam_lib_version_req_minor \\* 100 \\+ $bam_lib_version_req_sub_minor`\n\tAC_MSG_CHECKING(for htslib or bamlib)\n\tsucceeded=no\n\n\tUSE_HTSLIB=0\n\n\tdnl first we check the system location for bam libraries\n\tSEARCH_DIRS=\"/usr /usr/local /opt /opt/local\"\n\tif test \"$ac_bam_path\" != \"\"; then\n\t SEARCH_DIRS=$ac_bam_path\n\tfi\n\tfor ac_bam_path_tmp in $SEARCH_DIRS ; do\n\t\tif test -d \"$ac_bam_path_tmp/include/htslib\" && test -r \"$ac_bam_path_tmp/include/htslib\"; then\n\t\t\tBAM_LDFLAGS=\"-L$ac_bam_path_tmp/lib\"\n\t\t\tBAM_CPPFLAGS=\"-I$ac_bam_path_tmp/include\"\n\t\t\tBAM_LIB_PARAM=\"-lhts\"\n\t\t\tUSE_HTSLIB=1\n\t\t\tAC_DEFINE(HAVE_HTSLIB,,[define if the BAM library is htslib])\n\t\t\tbreak;\n\t\tfi\n\t\tif test -d \"$ac_bam_path_tmp/include/bam\" && test -r \"$ac_bam_path_tmp/include/bam\"; then\n\t\t\tBAM_LDFLAGS=\"-L$ac_bam_path_tmp/lib\"\n\t\t\tBAM_CPPFLAGS=\"-I$ac_bam_path_tmp/include\"\n\t\t\tBAM_LIB_PARAM=\"-lbam\"\n\t\t\tbreak;\n\t\tfi\n\tdone\n\n dnl overwrite ld flags if we have required special directory with\n dnl --with-bam-libdir parameter\n if test \"$ac_bam_lib_path\" != \"\"; then\n BAM_LDFLAGS=\"-L$ac_bam_lib_path\"\n fi\n\n\tCPPFLAGS_SAVED=\"$CPPFLAGS\"\n\tCPPFLAGS=\"$CPPFLAGS $BAM_CPPFLAGS\"\n\texport CPPFLAGS\n\n\tLDFLAGS_SAVED=\"$LDFLAGS\"\n\tLDFLAGS=\"$LDFLAGS $BAM_LDFLAGS\"\n\texport LDFLAGS\n\n\tif test \"$USE_HTSLIB\" == \"0\"; then\n\n\t AC_LANG_PUSH(C++)\n\t \tAC_COMPILE_IFELSE([AC_LANG_PROGRAM([[\n\t\t\t\t\t@%:@include \n\t\t\t\t\t]], [[]])],[\n AC_MSG_RESULT(yes)\n\t succeeded=yes\n\t found_system=yes\n \t],[\n \t])\n\t AC_LANG_POP([C++])\n\n\telse\n\n\t AC_LANG_PUSH(C++)\n\t \tAC_COMPILE_IFELSE([AC_LANG_PROGRAM([[\n\t\t\t\t\t@%:@include \n\t\t\t\t\t]], [[]])],[\n AC_MSG_RESULT(yes)\n\t succeeded=yes\n\t found_system=yes\n \t],[\n \t])\n\t AC_LANG_POP([C++])\n\n fi\n\n\tif test \"$succeeded\" != \"yes\" ; then\n\t\tAC_MSG_ERROR([[We could not detect the bam libraries. Try installing htslib or libbam, or specifying a path to one with --with-bam=/some/path]])\n\telse\n\t\tBAM_LIB=\"$BAM_LIB_PARAM\"\n\t\tAC_SUBST(BAM_CPPFLAGS)\n\t\tAC_SUBST(BAM_LDFLAGS)\n\t\tAC_SUBST(BAM_LIB)\n\t\tAC_DEFINE(HAVE_BAM,,[define if the BAM library is available])\n\tfi\n\n CPPFLAGS=\"$CPPFLAGS_SAVED\"\n \tLDFLAGS=\"$LDFLAGS_SAVED\"\nfi\n\n])\n" }, { "path": "ax_boost_base.m4", "content": "# ===========================================================================\n# https://www.gnu.org/software/autoconf-archive/ax_boost_base.html\n# ===========================================================================\n#\n# SYNOPSIS\n#\n# AX_BOOST_BASE([MINIMUM-VERSION], [ACTION-IF-FOUND], [ACTION-IF-NOT-FOUND])\n#\n# DESCRIPTION\n#\n# Test for the Boost C++ libraries of a particular version (or newer)\n#\n# If no path to the installed boost library is given the macro searchs\n# under /usr, /usr/local, /opt and /opt/local and evaluates the\n# $BOOST_ROOT environment variable. Further documentation is available at\n# .\n#\n# This macro calls:\n#\n# AC_SUBST(BOOST_CPPFLAGS) / AC_SUBST(BOOST_LDFLAGS)\n#\n# And sets:\n#\n# HAVE_BOOST\n#\n# LICENSE\n#\n# Copyright (c) 2008 Thomas Porschberg \n# Copyright (c) 2009 Peter Adolphs\n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved. This file is offered as-is, without any\n# warranty.\n\n#serial 45\n\n# example boost program (need to pass version)\nm4_define([_AX_BOOST_BASE_PROGRAM],\n [AC_LANG_PROGRAM([[\n#include \n]],[[\n(void) ((void)sizeof(char[1 - 2*!!((BOOST_VERSION) < ($1))]));\n]])])\n\nAC_DEFUN([AX_BOOST_BASE],\n[\nAC_ARG_WITH([boost],\n [AS_HELP_STRING([--with-boost@<:@=ARG@:>@],\n [use Boost library from a standard location (ARG=yes),\n from the specified location (ARG=),\n or disable it (ARG=no)\n @<:@ARG=yes@:>@ ])],\n [\n AS_CASE([$withval],\n [no],[want_boost=\"no\";_AX_BOOST_BASE_boost_path=\"\"],\n [yes],[want_boost=\"yes\";_AX_BOOST_BASE_boost_path=\"\"],\n [want_boost=\"yes\";_AX_BOOST_BASE_boost_path=\"$withval\"])\n ],\n [want_boost=\"yes\"])\n\n\nAC_ARG_WITH([boost-libdir],\n [AS_HELP_STRING([--with-boost-libdir=LIB_DIR],\n [Force given directory for boost libraries.\n Note that this will override library path detection,\n so use this parameter only if default library detection fails\n and you know exactly where your boost libraries are located.])],\n [\n AS_IF([test -d \"$withval\"],\n [_AX_BOOST_BASE_boost_lib_path=\"$withval\"],\n [AC_MSG_ERROR([--with-boost-libdir expected directory name])])\n ],\n [_AX_BOOST_BASE_boost_lib_path=\"\"])\n\nBOOST_LDFLAGS=\"\"\nBOOST_CPPFLAGS=\"\"\nAS_IF([test \"x$want_boost\" = \"xyes\"],\n [_AX_BOOST_BASE_RUNDETECT([$1],[$2],[$3])])\nAC_SUBST(BOOST_CPPFLAGS)\nAC_SUBST(BOOST_LDFLAGS)\n])\n\n\n# convert a version string in $2 to numeric and affect to polymorphic var $1\nAC_DEFUN([_AX_BOOST_BASE_TONUMERICVERSION],[\n AS_IF([test \"x$2\" = \"x\"],[_AX_BOOST_BASE_TONUMERICVERSION_req=\"1.20.0\"],[_AX_BOOST_BASE_TONUMERICVERSION_req=\"$2\"])\n _AX_BOOST_BASE_TONUMERICVERSION_req_shorten=`expr $_AX_BOOST_BASE_TONUMERICVERSION_req : '\\([[0-9]]*\\.[[0-9]]*\\)'`\n _AX_BOOST_BASE_TONUMERICVERSION_req_major=`expr $_AX_BOOST_BASE_TONUMERICVERSION_req : '\\([[0-9]]*\\)'`\n AS_IF([test \"x$_AX_BOOST_BASE_TONUMERICVERSION_req_major\" = \"x\"],\n [AC_MSG_ERROR([You should at least specify libboost major version])])\n _AX_BOOST_BASE_TONUMERICVERSION_req_minor=`expr $_AX_BOOST_BASE_TONUMERICVERSION_req : '[[0-9]]*\\.\\([[0-9]]*\\)'`\n AS_IF([test \"x$_AX_BOOST_BASE_TONUMERICVERSION_req_minor\" = \"x\"],\n [_AX_BOOST_BASE_TONUMERICVERSION_req_minor=\"0\"])\n _AX_BOOST_BASE_TONUMERICVERSION_req_sub_minor=`expr $_AX_BOOST_BASE_TONUMERICVERSION_req : '[[0-9]]*\\.[[0-9]]*\\.\\([[0-9]]*\\)'`\n AS_IF([test \"X$_AX_BOOST_BASE_TONUMERICVERSION_req_sub_minor\" = \"X\"],\n [_AX_BOOST_BASE_TONUMERICVERSION_req_sub_minor=\"0\"])\n _AX_BOOST_BASE_TONUMERICVERSION_RET=`expr $_AX_BOOST_BASE_TONUMERICVERSION_req_major \\* 100000 \\+ $_AX_BOOST_BASE_TONUMERICVERSION_req_minor \\* 100 \\+ $_AX_BOOST_BASE_TONUMERICVERSION_req_sub_minor`\n AS_VAR_SET($1,$_AX_BOOST_BASE_TONUMERICVERSION_RET)\n])\n\ndnl Run the detection of boost should be run only if $want_boost\nAC_DEFUN([_AX_BOOST_BASE_RUNDETECT],[\n _AX_BOOST_BASE_TONUMERICVERSION(WANT_BOOST_VERSION,[$1])\n succeeded=no\n\n\n AC_REQUIRE([AC_CANONICAL_HOST])\n dnl On 64-bit systems check for system libraries in both lib64 and lib.\n dnl The former is specified by FHS, but e.g. Debian does not adhere to\n dnl this (as it rises problems for generic multi-arch support).\n dnl The last entry in the list is chosen by default when no libraries\n dnl are found, e.g. when only header-only libraries are installed!\n AS_CASE([${host_cpu}],\n [x86_64],[libsubdirs=\"lib64 libx32 lib lib64\"],\n [ppc64|powerpc64|s390x|sparc64|aarch64|ppc64le|powerpc64le|riscv64],[libsubdirs=\"lib64 lib lib64\"],\n [libsubdirs=\"lib\"]\n )\n\n dnl allow for real multi-arch paths e.g. /usr/lib/x86_64-linux-gnu. Give\n dnl them priority over the other paths since, if libs are found there, they\n dnl are almost assuredly the ones desired.\n AS_CASE([${host_cpu}],\n [i?86],[multiarch_libsubdir=\"lib/i386-${host_os}\"],\n [multiarch_libsubdir=\"lib/${host_cpu}-${host_os}\"]\n )\n\n dnl first we check the system location for boost libraries\n dnl this location ist chosen if boost libraries are installed with the --layout=system option\n dnl or if you install boost with RPM\n AS_IF([test \"x$_AX_BOOST_BASE_boost_path\" != \"x\"],[\n AC_MSG_CHECKING([for boostlib >= $1 ($WANT_BOOST_VERSION) includes in \"$_AX_BOOST_BASE_boost_path/include\"])\n AS_IF([test -d \"$_AX_BOOST_BASE_boost_path/include\" && test -r \"$_AX_BOOST_BASE_boost_path/include\"],[\n AC_MSG_RESULT([yes])\n BOOST_CPPFLAGS=\"-I$_AX_BOOST_BASE_boost_path/include\"\n for _AX_BOOST_BASE_boost_path_tmp in $multiarch_libsubdir $libsubdirs; do\n AC_MSG_CHECKING([for boostlib >= $1 ($WANT_BOOST_VERSION) lib path in \"$_AX_BOOST_BASE_boost_path/$_AX_BOOST_BASE_boost_path_tmp\"])\n AS_IF([test -d \"$_AX_BOOST_BASE_boost_path/$_AX_BOOST_BASE_boost_path_tmp\" && test -r \"$_AX_BOOST_BASE_boost_path/$_AX_BOOST_BASE_boost_path_tmp\" ],[\n AC_MSG_RESULT([yes])\n BOOST_LDFLAGS=\"-L$_AX_BOOST_BASE_boost_path/$_AX_BOOST_BASE_boost_path_tmp\";\n break;\n ],\n [AC_MSG_RESULT([no])])\n done],[\n AC_MSG_RESULT([no])])\n ],[\n if test X\"$cross_compiling\" = Xyes; then\n search_libsubdirs=$multiarch_libsubdir\n else\n search_libsubdirs=\"$multiarch_libsubdir $libsubdirs\"\n fi\n for _AX_BOOST_BASE_boost_path_tmp in /usr /usr/local /opt /opt/local ; do\n if test -d \"$_AX_BOOST_BASE_boost_path_tmp/include/boost\" && test -r \"$_AX_BOOST_BASE_boost_path_tmp/include/boost\" ; then\n for libsubdir in $search_libsubdirs ; do\n if ls \"$_AX_BOOST_BASE_boost_path_tmp/$libsubdir/libboost_\"* >/dev/null 2>&1 ; then break; fi\n done\n BOOST_LDFLAGS=\"-L$_AX_BOOST_BASE_boost_path_tmp/$libsubdir\"\n BOOST_CPPFLAGS=\"-I$_AX_BOOST_BASE_boost_path_tmp/include\"\n break;\n fi\n done\n ])\n\n dnl overwrite ld flags if we have required special directory with\n dnl --with-boost-libdir parameter\n AS_IF([test \"x$_AX_BOOST_BASE_boost_lib_path\" != \"x\"],\n [BOOST_LDFLAGS=\"-L$_AX_BOOST_BASE_boost_lib_path\"])\n\n AC_MSG_CHECKING([for boostlib >= $1 ($WANT_BOOST_VERSION)])\n CPPFLAGS_SAVED=\"$CPPFLAGS\"\n CPPFLAGS=\"$CPPFLAGS $BOOST_CPPFLAGS\"\n export CPPFLAGS\n\n LDFLAGS_SAVED=\"$LDFLAGS\"\n LDFLAGS=\"$LDFLAGS $BOOST_LDFLAGS\"\n export LDFLAGS\n\n AC_REQUIRE([AC_PROG_CXX])\n AC_LANG_PUSH(C++)\n AC_COMPILE_IFELSE([_AX_BOOST_BASE_PROGRAM($WANT_BOOST_VERSION)],[\n AC_MSG_RESULT(yes)\n succeeded=yes\n found_system=yes\n ],[\n ])\n AC_LANG_POP([C++])\n\n\n\n dnl if we found no boost with system layout we search for boost libraries\n dnl built and installed without the --layout=system option or for a staged(not installed) version\n if test \"x$succeeded\" != \"xyes\" ; then\n CPPFLAGS=\"$CPPFLAGS_SAVED\"\n LDFLAGS=\"$LDFLAGS_SAVED\"\n BOOST_CPPFLAGS=\n if test -z \"$_AX_BOOST_BASE_boost_lib_path\" ; then\n BOOST_LDFLAGS=\n fi\n _version=0\n if test -n \"$_AX_BOOST_BASE_boost_path\" ; then\n if test -d \"$_AX_BOOST_BASE_boost_path\" && test -r \"$_AX_BOOST_BASE_boost_path\"; then\n for i in `ls -d $_AX_BOOST_BASE_boost_path/include/boost-* 2>/dev/null`; do\n _version_tmp=`echo $i | sed \"s#$_AX_BOOST_BASE_boost_path##\" | sed 's/\\/include\\/boost-//' | sed 's/_/./'`\n V_CHECK=`expr $_version_tmp \\> $_version`\n if test \"x$V_CHECK\" = \"x1\" ; then\n _version=$_version_tmp\n fi\n VERSION_UNDERSCORE=`echo $_version | sed 's/\\./_/'`\n BOOST_CPPFLAGS=\"-I$_AX_BOOST_BASE_boost_path/include/boost-$VERSION_UNDERSCORE\"\n done\n dnl if nothing found search for layout used in Windows distributions\n if test -z \"$BOOST_CPPFLAGS\"; then\n if test -d \"$_AX_BOOST_BASE_boost_path/boost\" && test -r \"$_AX_BOOST_BASE_boost_path/boost\"; then\n BOOST_CPPFLAGS=\"-I$_AX_BOOST_BASE_boost_path\"\n fi\n fi\n dnl if we found something and BOOST_LDFLAGS was unset before\n dnl (because \"$_AX_BOOST_BASE_boost_lib_path\" = \"\"), set it here.\n if test -n \"$BOOST_CPPFLAGS\" && test -z \"$BOOST_LDFLAGS\"; then\n for libsubdir in $libsubdirs ; do\n if ls \"$_AX_BOOST_BASE_boost_path/$libsubdir/libboost_\"* >/dev/null 2>&1 ; then break; fi\n done\n BOOST_LDFLAGS=\"-L$_AX_BOOST_BASE_boost_path/$libsubdir\"\n fi\n fi\n else\n if test \"x$cross_compiling\" != \"xyes\" ; then\n for _AX_BOOST_BASE_boost_path in /usr /usr/local /opt /opt/local ; do\n if test -d \"$_AX_BOOST_BASE_boost_path\" && test -r \"$_AX_BOOST_BASE_boost_path\" ; then\n for i in `ls -d $_AX_BOOST_BASE_boost_path/include/boost-* 2>/dev/null`; do\n _version_tmp=`echo $i | sed \"s#$_AX_BOOST_BASE_boost_path##\" | sed 's/\\/include\\/boost-//' | sed 's/_/./'`\n V_CHECK=`expr $_version_tmp \\> $_version`\n if test \"x$V_CHECK\" = \"x1\" ; then\n _version=$_version_tmp\n best_path=$_AX_BOOST_BASE_boost_path\n fi\n done\n fi\n done\n\n VERSION_UNDERSCORE=`echo $_version | sed 's/\\./_/'`\n BOOST_CPPFLAGS=\"-I$best_path/include/boost-$VERSION_UNDERSCORE\"\n if test -z \"$_AX_BOOST_BASE_boost_lib_path\" ; then\n for libsubdir in $libsubdirs ; do\n if ls \"$best_path/$libsubdir/libboost_\"* >/dev/null 2>&1 ; then break; fi\n done\n BOOST_LDFLAGS=\"-L$best_path/$libsubdir\"\n fi\n fi\n\n if test -n \"$BOOST_ROOT\" ; then\n for libsubdir in $libsubdirs ; do\n if ls \"$BOOST_ROOT/stage/$libsubdir/libboost_\"* >/dev/null 2>&1 ; then break; fi\n done\n if test -d \"$BOOST_ROOT\" && test -r \"$BOOST_ROOT\" && test -d \"$BOOST_ROOT/stage/$libsubdir\" && test -r \"$BOOST_ROOT/stage/$libsubdir\"; then\n version_dir=`expr //$BOOST_ROOT : '.*/\\(.*\\)'`\n stage_version=`echo $version_dir | sed 's/boost_//' | sed 's/_/./g'`\n stage_version_shorten=`expr $stage_version : '\\([[0-9]]*\\.[[0-9]]*\\)'`\n V_CHECK=`expr $stage_version_shorten \\>\\= $_version`\n if test \"x$V_CHECK\" = \"x1\" && test -z \"$_AX_BOOST_BASE_boost_lib_path\" ; then\n AC_MSG_NOTICE(We will use a staged boost library from $BOOST_ROOT)\n BOOST_CPPFLAGS=\"-I$BOOST_ROOT\"\n BOOST_LDFLAGS=\"-L$BOOST_ROOT/stage/$libsubdir\"\n fi\n fi\n fi\n fi\n\n CPPFLAGS=\"$CPPFLAGS $BOOST_CPPFLAGS\"\n export CPPFLAGS\n LDFLAGS=\"$LDFLAGS $BOOST_LDFLAGS\"\n export LDFLAGS\n\n AC_LANG_PUSH(C++)\n AC_COMPILE_IFELSE([_AX_BOOST_BASE_PROGRAM($WANT_BOOST_VERSION)],[\n AC_MSG_RESULT(yes)\n succeeded=yes\n found_system=yes\n ],[\n ])\n AC_LANG_POP([C++])\n fi\n\n if test \"x$succeeded\" != \"xyes\" ; then\n if test \"x$_version\" = \"x0\" ; then\n AC_MSG_NOTICE([[We could not detect the boost libraries (version $1 or higher). If you have a staged boost library (still not installed) please specify \\$BOOST_ROOT in your environment and do not give a PATH to --with-boost option. If you are sure you have boost installed, then check your version number looking in . See http://randspringer.de/boost for more documentation.]])\n else\n AC_MSG_NOTICE([Your boost libraries seems to old (version $_version).])\n fi\n # execute ACTION-IF-NOT-FOUND (if present):\n ifelse([$3], , :, [$3])\n else\n AC_DEFINE(HAVE_BOOST,,[define if the Boost library is available])\n # execute ACTION-IF-FOUND (if present):\n ifelse([$2], , :, [$2])\n fi\n\n CPPFLAGS=\"$CPPFLAGS_SAVED\"\n LDFLAGS=\"$LDFLAGS_SAVED\"\n\n])\n" }, { "path": "ax_boost_filesystem.m4", "content": "# ===========================================================================\n# https://www.gnu.org/software/autoconf-archive/ax_boost_filesystem.html\n# ===========================================================================\n#\n# SYNOPSIS\n#\n# AX_BOOST_FILESYSTEM\n#\n# DESCRIPTION\n#\n# Test for Filesystem library from the Boost C++ libraries. The macro\n# requires a preceding call to AX_BOOST_BASE. Further documentation is\n# available at .\n#\n# This macro calls:\n#\n# AC_SUBST(BOOST_FILESYSTEM_LIB)\n#\n# And sets:\n#\n# HAVE_BOOST_FILESYSTEM\n#\n# LICENSE\n#\n# Copyright (c) 2009 Thomas Porschberg \n# Copyright (c) 2009 Michael Tindal\n# Copyright (c) 2009 Roman Rybalko \n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved. This file is offered as-is, without any\n# warranty.\n\n#serial 27\n\nAC_DEFUN([AX_BOOST_FILESYSTEM],\n[\n\tAC_ARG_WITH([boost-filesystem],\n\tAS_HELP_STRING([--with-boost-filesystem@<:@=special-lib@:>@],\n [use the Filesystem library from boost - it is possible to specify a certain library for the linker\n e.g. --with-boost-filesystem=boost_filesystem-gcc-mt ]),\n [\n if test \"$withval\" = \"no\"; then\n\t\t\twant_boost=\"no\"\n elif test \"$withval\" = \"yes\"; then\n want_boost=\"yes\"\n ax_boost_user_filesystem_lib=\"\"\n else\n\t\t want_boost=\"yes\"\n\t\tax_boost_user_filesystem_lib=\"$withval\"\n\t\tfi\n ],\n [want_boost=\"yes\"]\n\t)\n\n\tif test \"x$want_boost\" = \"xyes\"; then\n AC_REQUIRE([AC_PROG_CC])\n\t\tCPPFLAGS_SAVED=\"$CPPFLAGS\"\n\t\tCPPFLAGS=\"$CPPFLAGS $BOOST_CPPFLAGS\"\n\t\texport CPPFLAGS\n\n\t\tLDFLAGS_SAVED=\"$LDFLAGS\"\n\t\tLDFLAGS=\"$LDFLAGS $BOOST_LDFLAGS\"\n\t\texport LDFLAGS\n\n\t\tLIBS_SAVED=$LIBS\n\t\tLIBS=\"$LIBS $BOOST_SYSTEM_LIB\"\n\t\texport LIBS\n\n AC_CACHE_CHECK(whether the Boost::Filesystem library is available,\n\t\t\t\t\t ax_cv_boost_filesystem,\n [AC_LANG_PUSH([C++])\n AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[@%:@include ]],\n [[using namespace boost::filesystem;\n path my_path( \"foo/bar/data.txt\" );\n return 0;]])],\n\t\t\t\t\t ax_cv_boost_filesystem=yes, ax_cv_boost_filesystem=no)\n AC_LANG_POP([C++])\n\t\t])\n\t\tif test \"x$ax_cv_boost_filesystem\" = \"xyes\"; then\n\t\t\tAC_DEFINE(HAVE_BOOST_FILESYSTEM,,[define if the Boost::Filesystem library is available])\n BOOSTLIBDIR=`echo $BOOST_LDFLAGS | sed -e 's/@<:@^\\/@:>@*//'`\n if test \"x$ax_boost_user_filesystem_lib\" = \"x\"; then\n for libextension in `ls -r $BOOSTLIBDIR/libboost_filesystem* 2>/dev/null | sed 's,.*/lib,,' | sed 's,\\..*,,'` ; do\n ax_lib=${libextension}\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_FILESYSTEM_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_FILESYSTEM_LIB) link_filesystem=\"yes\"; break],\n [link_filesystem=\"no\"])\n\t\t\t\tdone\n if test \"x$link_filesystem\" != \"xyes\"; then\n for libextension in `ls -r $BOOSTLIBDIR/boost_filesystem* 2>/dev/null | sed 's,.*/,,' | sed -e 's,\\..*,,'` ; do\n ax_lib=${libextension}\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_FILESYSTEM_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_FILESYSTEM_LIB) link_filesystem=\"yes\"; break],\n [link_filesystem=\"no\"])\n\t\t\t\tdone\n\t\t fi\n else\n for ax_lib in $ax_boost_user_filesystem_lib boost_filesystem-$ax_boost_user_filesystem_lib; do\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_FILESYSTEM_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_FILESYSTEM_LIB) link_filesystem=\"yes\"; break],\n [link_filesystem=\"no\"])\n done\n\n fi\n if test \"x$ax_lib\" = \"x\"; then\n AC_MSG_ERROR(Could not find a version of the library!)\n fi\n\t\t\tif test \"x$link_filesystem\" != \"xyes\"; then\n\t\t\t\tAC_MSG_ERROR(Could not link against $ax_lib !)\n\t\t\tfi\n\t\tfi\n\n\t\tCPPFLAGS=\"$CPPFLAGS_SAVED\"\n\t\tLDFLAGS=\"$LDFLAGS_SAVED\"\n\t\tLIBS=\"$LIBS_SAVED\"\n\tfi\n])\n" }, { "path": "ax_boost_serialization.m4", "content": "# ===========================================================================\n# https://www.gnu.org/software/autoconf-archive/ax_boost_serialization.html\n# ===========================================================================\n#\n# SYNOPSIS\n#\n# AX_BOOST_SERIALIZATION\n#\n# DESCRIPTION\n#\n# Test for Serialization library from the Boost C++ libraries. The macro\n# requires a preceding call to AX_BOOST_BASE. Further documentation is\n# available at .\n#\n# This macro calls:\n#\n# AC_SUBST(BOOST_SERIALIZATION_LIB)\n#\n# And sets:\n#\n# HAVE_BOOST_SERIALIZATION\n#\n# LICENSE\n#\n# Copyright (c) 2008 Thomas Porschberg \n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved. This file is offered as-is, without any\n# warranty.\n\n#serial 22\n\nAC_DEFUN([AX_BOOST_SERIALIZATION],\n[\n\tAC_ARG_WITH([boost-serialization],\n\tAS_HELP_STRING([--with-boost-serialization@<:@=special-lib@:>@],\n [use the Serialization library from boost - it is possible to specify a certain library for the linker\n e.g. --with-boost-serialization=boost_serialization-gcc-mt-d-1_33_1 ]),\n [\n if test \"$withval\" = \"no\"; then\n\t\t\twant_boost=\"no\"\n elif test \"$withval\" = \"yes\"; then\n want_boost=\"yes\"\n ax_boost_user_serialization_lib=\"\"\n else\n\t\t want_boost=\"yes\"\n\t\tax_boost_user_serialization_lib=\"$withval\"\n\t\tfi\n ],\n [want_boost=\"yes\"]\n\t)\n\n\tif test \"x$want_boost\" = \"xyes\"; then\n AC_REQUIRE([AC_PROG_CC])\n\t\tCPPFLAGS_SAVED=\"$CPPFLAGS\"\n\t\tCPPFLAGS=\"$CPPFLAGS $BOOST_CPPFLAGS\"\n\t AC_MSG_WARN(BOOST_CPPFLAGS $BOOST_CPPFLAGS)\n\t\texport CPPFLAGS\n\n\t\tLDFLAGS_SAVED=\"$LDFLAGS\"\n\t\tLDFLAGS=\"$LDFLAGS $BOOST_LDFLAGS\"\n\t\texport LDFLAGS\n\n AC_CACHE_CHECK(whether the Boost::Serialization library is available,\n\t\t\t\t\t ax_cv_boost_serialization,\n [AC_LANG_PUSH([C++])\n\t\t\t AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[@%:@include \n\t\t\t\t\t\t\t\t\t\t\t\t @%:@include \n @%:@include \n\t\t\t\t\t\t\t\t\t\t\t\t]],\n [[std::ofstream ofs(\"filename\");\n\t\t\t\t\t\t\t\t\tboost::archive::text_oarchive oa(ofs);\n\t\t\t\t\t\t\t\t\t return 0;\n ]])],\n ax_cv_boost_serialization=yes, ax_cv_boost_serialization=no)\n AC_LANG_POP([C++])\n\t\t])\n\t\tif test \"x$ax_cv_boost_serialization\" = \"xyes\"; then\n\t\t\tAC_DEFINE(HAVE_BOOST_SERIALIZATION,,[define if the Boost::Serialization library is available])\n BOOSTLIBDIR=`echo $BOOST_LDFLAGS | sed -e 's/@<:@^\\/@:>@*//'`\n if test \"x$ax_boost_user_serialization_lib\" = \"x\"; then\n for libextension in `ls $BOOSTLIBDIR/libboost_serialization*.so* $BOOSTLIBDIR/libboost_serialization*.dylib* $BOOSTLIBDIR/libboost_serialization*.a* 2>/dev/null | sed 's,.*/,,' | sed -e 's;^lib\\(boost_serialization.*\\)\\.so.*$;\\1;' -e 's;^lib\\(boost_serialization.*\\)\\.dylib.*$;\\1;' -e 's;^lib\\(boost_serialization.*\\)\\.a*$;\\1;'` ; do\n ax_lib=${libextension}\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_SERIALIZATION_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_SERIALIZATION_LIB) link_serialization=\"yes\"; break],\n [link_serialization=\"no\"])\n\t\t\t\tdone\n if test \"x$link_serialization\" != \"xyes\"; then\n for libextension in `ls $BOOSTLIBDIR/boost_serialization*.dll* $BOOSTLIBDIR/boost_serialization*.a* 2>/dev/null | sed 's,.*/,,' | sed -e 's;^\\(boost_serialization.*\\)\\.dll.*$;\\1;' -e 's;^\\(boost_serialization.*\\)\\.a.*$;\\1;'` ; do\n ax_lib=${libextension}\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_SERIALIZATION_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_SERIALIZATION_LIB) link_serialization=\"yes\"; break],\n [link_serialization=\"no\"])\n\t\t\t\tdone\n fi\n\n else\n for ax_lib in $ax_boost_user_serialization_lib boost_serialization-$ax_boost_user_serialization_lib; do\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_SERIALIZATION_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_SERIALIZATION_LIB) link_serialization=\"yes\"; break],\n [link_serialization=\"no\"])\n done\n\n fi\n if test \"x$ax_lib\" = \"x\"; then\n AC_MSG_ERROR(Could not find a version of the library!)\n fi\n\t\t\tif test \"x$link_serialization\" != \"xyes\"; then\n\t\t\t\tAC_MSG_ERROR(Could not link against $ax_lib !)\n\t\t\tfi\n\t\tfi\n\n\t\tCPPFLAGS=\"$CPPFLAGS_SAVED\"\n\tLDFLAGS=\"$LDFLAGS_SAVED\"\n\tfi\n])\n" }, { "path": "ax_boost_system.m4", "content": "# ===========================================================================\n# https://www.gnu.org/software/autoconf-archive/ax_boost_system.html\n# ===========================================================================\n#\n# SYNOPSIS\n#\n# AX_BOOST_SYSTEM\n#\n# DESCRIPTION\n#\n# Test for System library from the Boost C++ libraries. The macro requires\n# a preceding call to AX_BOOST_BASE. Further documentation is available at\n# .\n#\n# This macro calls:\n#\n# AC_SUBST(BOOST_SYSTEM_LIB)\n#\n# And sets:\n#\n# HAVE_BOOST_SYSTEM\n#\n# LICENSE\n#\n# Copyright (c) 2008 Thomas Porschberg \n# Copyright (c) 2008 Michael Tindal\n# Copyright (c) 2008 Daniel Casimiro \n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved. This file is offered as-is, without any\n# warranty.\n\n#serial 19\n\nAC_DEFUN([AX_BOOST_SYSTEM],\n[\n\tAC_ARG_WITH([boost-system],\n\tAS_HELP_STRING([--with-boost-system@<:@=special-lib@:>@],\n [use the System library from boost - it is possible to specify a certain library for the linker\n e.g. --with-boost-system=boost_system-gcc-mt ]),\n [\n if test \"$withval\" = \"no\"; then\n\t\t\twant_boost=\"no\"\n elif test \"$withval\" = \"yes\"; then\n want_boost=\"yes\"\n ax_boost_user_system_lib=\"\"\n else\n\t\t want_boost=\"yes\"\n\t\tax_boost_user_system_lib=\"$withval\"\n\t\tfi\n ],\n [want_boost=\"yes\"]\n\t)\n\n\tif test \"x$want_boost\" = \"xyes\"; then\n AC_REQUIRE([AC_PROG_CC])\n AC_REQUIRE([AC_CANONICAL_BUILD])\n\t\tCPPFLAGS_SAVED=\"$CPPFLAGS\"\n\t\tCPPFLAGS=\"$CPPFLAGS $BOOST_CPPFLAGS\"\n\t\texport CPPFLAGS\n\n\t\tLDFLAGS_SAVED=\"$LDFLAGS\"\n\t\tLDFLAGS=\"$LDFLAGS $BOOST_LDFLAGS\"\n\t\texport LDFLAGS\n\n AC_CACHE_CHECK(whether the Boost::System library is available,\n\t\t\t\t\t ax_cv_boost_system,\n [AC_LANG_PUSH([C++])\n\t\t\t CXXFLAGS_SAVE=$CXXFLAGS\n\t\t\t CXXFLAGS=\n\n\t\t\t AC_COMPILE_IFELSE([AC_LANG_PROGRAM([[@%:@include ]],\n\t\t\t\t [[boost::system::error_category *a = 0;]])],\n ax_cv_boost_system=yes, ax_cv_boost_system=no)\n\t\t\t CXXFLAGS=$CXXFLAGS_SAVE\n AC_LANG_POP([C++])\n\t\t])\n\t\tif test \"x$ax_cv_boost_system\" = \"xyes\"; then\n\t\t\tAC_SUBST(BOOST_CPPFLAGS)\n\n\t\t\tAC_DEFINE(HAVE_BOOST_SYSTEM,,[define if the Boost::System library is available])\n BOOSTLIBDIR=`echo $BOOST_LDFLAGS | sed -e 's/@<:@^\\/@:>@*//'`\n\n\t\t\tLDFLAGS_SAVE=$LDFLAGS\n if test \"x$ax_boost_user_system_lib\" = \"x\"; then\n for libextension in `ls -r $BOOSTLIBDIR/libboost_system* 2>/dev/null | sed 's,.*/lib,,' | sed 's,\\..*,,'` ; do\n ax_lib=${libextension}\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_SYSTEM_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_SYSTEM_LIB) link_system=\"yes\"; break],\n [link_system=\"no\"])\n\t\t\t\tdone\n if test \"x$link_system\" != \"xyes\"; then\n for libextension in `ls -r $BOOSTLIBDIR/boost_system* 2>/dev/null | sed 's,.*/,,' | sed -e 's,\\..*,,'` ; do\n ax_lib=${libextension}\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_SYSTEM_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_SYSTEM_LIB) link_system=\"yes\"; break],\n [link_system=\"no\"])\n\t\t\t\tdone\n fi\n\n else\n for ax_lib in $ax_boost_user_system_lib boost_system-$ax_boost_user_system_lib; do\n\t\t\t\t AC_CHECK_LIB($ax_lib, main,\n [BOOST_SYSTEM_LIB=\"-l$ax_lib\"; AC_SUBST(BOOST_SYSTEM_LIB) link_system=\"yes\"; break],\n [link_system=\"no\"])\n done\n\n fi\n if test \"x$ax_lib\" = \"x\"; then\n AC_MSG_ERROR(Could not find a version of the library!)\n fi\n\t\t\tif test \"x$link_system\" = \"xno\"; then\n\t\t\t\tAC_MSG_ERROR(Could not link against $ax_lib !)\n\t\t\tfi\n\t\tfi\n\n\t\tCPPFLAGS=\"$CPPFLAGS_SAVED\"\n\tLDFLAGS=\"$LDFLAGS_SAVED\"\n\tfi\n])\n" }, { "path": "ax_boost_thread.m4", "content": "# ===========================================================================\n# https://www.gnu.org/software/autoconf-archive/ax_boost_thread.html\n# ===========================================================================\n#\n# SYNOPSIS\n#\n# AX_BOOST_THREAD\n#\n# DESCRIPTION\n#\n# Test for Thread library from the Boost C++ libraries. The macro requires\n# a preceding call to AX_BOOST_BASE. Further documentation is available at\n# .\n#\n# This macro calls:\n#\n# AC_SUBST(BOOST_THREAD_LIB)\n#\n# And sets:\n#\n# HAVE_BOOST_THREAD\n#\n# LICENSE\n#\n# Copyright (c) 2009 Thomas Porschberg \n# Copyright (c) 2009 Michael Tindal\n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved. This file is offered as-is, without any\n# warranty.\n\n#serial 31\n\nAC_DEFUN([AX_BOOST_THREAD],\n[\n AC_ARG_WITH([boost-thread],\n AS_HELP_STRING([--with-boost-thread@<:@=special-lib@:>@],\n [use the Thread library from boost -\n it is possible to specify a certain library for the linker\n e.g. --with-boost-thread=boost_thread-gcc-mt ]),\n [\n if test \"$withval\" = \"yes\"; then\n want_boost=\"yes\"\n ax_boost_user_thread_lib=\"\"\n else\n want_boost=\"yes\"\n ax_boost_user_thread_lib=\"$withval\"\n fi\n ],\n [want_boost=\"yes\"]\n )\n\n if test \"x$want_boost\" = \"xyes\"; then\n AC_REQUIRE([AC_PROG_CC])\n AC_REQUIRE([AC_CANONICAL_BUILD])\n CPPFLAGS_SAVED=\"$CPPFLAGS\"\n CPPFLAGS=\"$CPPFLAGS $BOOST_CPPFLAGS\"\n export CPPFLAGS\n\n LDFLAGS_SAVED=\"$LDFLAGS\"\n LDFLAGS=\"$LDFLAGS $BOOST_LDFLAGS\"\n export LDFLAGS\n\n AC_CACHE_CHECK(whether the Boost::Thread library is available,\n ax_cv_boost_thread,\n [AC_LANG_PUSH([C++])\n CXXFLAGS_SAVE=$CXXFLAGS\n\n if test \"x$host_os\" = \"xsolaris\" ; then\n CXXFLAGS=\"-pthreads $CXXFLAGS\"\n elif test \"x$host_os\" = \"xmingw32\" ; then\n CXXFLAGS=\"-mthreads $CXXFLAGS\"\n else\n CXXFLAGS=\"-pthread $CXXFLAGS\"\n fi\n AC_COMPILE_IFELSE([\n AC_LANG_PROGRAM(\n [[@%:@include ]],\n [[boost::thread_group thrds;\n return 0;]])],\n ax_cv_boost_thread=yes, ax_cv_boost_thread=no)\n CXXFLAGS=$CXXFLAGS_SAVE\n AC_LANG_POP([C++])\n ])\n if test \"x$ax_cv_boost_thread\" = \"xyes\"; then\n if test \"x$host_os\" = \"xsolaris\" ; then\n BOOST_CPPFLAGS=\"-pthreads $BOOST_CPPFLAGS\"\n elif test \"x$host_os\" = \"xmingw32\" ; then\n BOOST_CPPFLAGS=\"-mthreads $BOOST_CPPFLAGS\"\n else\n BOOST_CPPFLAGS=\"-pthread $BOOST_CPPFLAGS\"\n fi\n\n AC_SUBST(BOOST_CPPFLAGS)\n\n AC_DEFINE(HAVE_BOOST_THREAD,,\n [define if the Boost::Thread library is available])\n BOOSTLIBDIR=`echo $BOOST_LDFLAGS | sed -e 's/@<:@^\\/@:>@*//'`\n\n LDFLAGS_SAVE=$LDFLAGS\n case \"x$host_os\" in\n *bsd* )\n LDFLAGS=\"-pthread $LDFLAGS\"\n break;\n ;;\n esac\n if test \"x$ax_boost_user_thread_lib\" = \"x\"; then\n for libextension in `ls -r $BOOSTLIBDIR/libboost_thread* 2>/dev/null | sed 's,.*/lib,,' | sed 's,\\..*,,'`; do\n ax_lib=${libextension}\n AC_CHECK_LIB($ax_lib, main,\n [link_thread=\"yes\"; break],\n [link_thread=\"no\"])\n done\n if test \"x$link_thread\" != \"xyes\"; then\n for libextension in `ls -r $BOOSTLIBDIR/boost_thread* 2>/dev/null | sed 's,.*/,,' | sed 's,\\..*,,'`; do\n ax_lib=${libextension}\n AC_CHECK_LIB($ax_lib, main,\n [link_thread=\"yes\"; break],\n [link_thread=\"no\"])\n done\n fi\n\n else\n for ax_lib in $ax_boost_user_thread_lib boost_thread-$ax_boost_user_thread_lib; do\n AC_CHECK_LIB($ax_lib, main,\n [link_thread=\"yes\"; break],\n [link_thread=\"no\"])\n done\n\n fi\n if test \"x$ax_lib\" = \"x\"; then\n AC_MSG_ERROR(Could not find a version of the library!)\n fi\n if test \"x$link_thread\" = \"xno\"; then\n AC_MSG_ERROR(Could not link against $ax_lib !)\n else\n BOOST_THREAD_LIB=\"-l$ax_lib\"\n case \"x$host_os\" in\n *bsd* )\n BOOST_LDFLAGS=\"-pthread $BOOST_LDFLAGS\"\n break;\n ;;\n xsolaris )\n BOOST_THREAD_LIB=\"$BOOST_THREAD_LIB -lpthread\"\n break;\n ;;\n xmingw32 )\n break;\n ;;\n * )\n BOOST_THREAD_LIB=\"$BOOST_THREAD_LIB -lpthread\"\n break;\n ;;\n esac\n AC_SUBST(BOOST_THREAD_LIB)\n fi\n fi\n\n CPPFLAGS=\"$CPPFLAGS_SAVED\"\n LDFLAGS=\"$LDFLAGS_SAVED\"\n fi\n])\n" }, { "path": "ax_check_eigen.m4", "content": "# SYNOPSIS\n#\n# AX_EIGEN\n#\n# DESCRIPTION\n#\n# Test for the EIGEN libraries of a particular version (or newer)\n#\n# If no path to the installed eigen library is given the macro searchs\n# under /usr, /usr/local, /opt and /opt/local and evaluates the\n# $EIGEN_ROOT environment variable. \n#\tAdapted from AX_BOOST_BASE\n#\n# This macro calls:\n#\n# AC_SUBST(EIGEN_CPPFLAGS) / AC_SUBST(EIGEN_LDFLAGS)\n#\n# And sets:\n#\n# HAVE_EIGEN\n#\n# LICENSE\n#\n# Copyright (c) 2010 Cole Trapnell \n#\n# Copying and distribution of this file, with or without modification, are\n# permitted in any medium without royalty provided the copyright notice\n# and this notice are preserved.\n\nAC_DEFUN([AX_EIGEN],\n[\nAC_ARG_WITH([eigen],\nAS_HELP_STRING([--with-eigen@<:@=DIR@:>@], [use EIGEN libraries (default is yes) - it is possible to specify the root directory for EIGEN (optional)]),\n[\nif test \"$withval\" = \"no\"; then\nwant_eigen=\"no\"\nelif test \"$withval\" = \"yes\"; then\nwant_eigen=\"yes\"\nac_eigen_path=\"\"\nelse\nwant_eigen=\"yes\"\nac_eigen_path=\"$withval\"\nfi\n],\n[want_eigen=\"yes\"])\n\nif test \"x$want_eigen\" = \"xyes\"; then\nAC_MSG_CHECKING(for eigenlib)\nsucceeded=no\n\ndnl first we check the system location for eigen libraries\nif test \"$ac_eigen_path\" != \"\"; then\nEIGEN_CPPFLAGS=\"-I$ac_eigen_path/include\"\nelse\nfor ac_eigen_path_tmp in /usr /usr/local /opt /opt/local ; do\n for ac_eigen_subdir in eigen eigen3 ; do\n ac_candidate_path=\"$ac_eigen_path_tmp/include/$ac_eigen_subdir\"\n if test -d \"$ac_candidate_path\" && test -r \"$ac_candidate_path\"; then\n EIGEN_CPPFLAGS=\"-I$ac_candidate_path\"\n break;\n fi\n done\ndone\nfi\n\nCPPFLAGS_SAVED=\"$CPPFLAGS\"\nCPPFLAGS=\"$CPPFLAGS $EIGEN_CPPFLAGS\"\nexport EIGEN_CPPFLAGS\n\nAC_LANG_PUSH(C++)\nAC_COMPILE_IFELSE([AC_LANG_PROGRAM([[\n@%:@include \n]], [[\n]])],[\nAC_MSG_RESULT(yes)\nsucceeded=yes\nfound_system=yes\n],[\nAC_MSG_ERROR([[We could not detect the Eigen3 library. Try installing libeigen3, or specifying a path with --with-eigen=/some/path]])\n])\nAC_LANG_POP([C++])\n\nCPPFLAGS=\"$CPPFLAGS $EIGEN_CPPFLAGS\"\nexport CPPFLAGS\nLDFLAGS=\"$LDFLAGS $EIGEN_LDFLAGS\"\nexport LDFLAGS\nexport EIGEN_CPPFLAGS\n\nif test \"$succeeded\" == \"yes\" ; then\nAC_SUBST(EIGEN_CPPFLAGS)\nAC_DEFINE(HAVE_EIGEN,,[define if the EIGEN library is available])\nfi\n\nCPPFLAGS=\"$CPPFLAGS_SAVED\"\nLDFLAGS=\"$LDFLAGS_SAVED\"\nfi\n\n])\n" }, { "path": "ax_check_zlib.m4", "content": "# ===========================================================================\n# http://www.gnu.org/software/autoconf-archive/ax_check_zlib.html\n# ===========================================================================\n#\n# SYNOPSIS\n#\n# AX_CHECK_ZLIB()\n#\n# DESCRIPTION\n#\n# This macro searches for an installed zlib library. If nothing was\n# specified when calling configure, it searches first in /usr/local and\n# then in /usr. If the --with-zlib=DIR is specified, it will try to find\n# it in DIR/include/zlib.h and DIR/lib/libz.a. If --without-zlib is\n# specified, the library is not searched at all.\n#\n# If either the header file (zlib.h) or the library (libz) is not found,\n# the configuration exits on error, asking for a valid zlib installation\n# directory or --without-zlib.\n#\n# The macro defines the symbol HAVE_LIBZ if the library is found. You\n# should use autoheader to include a definition for this symbol in a\n# config.h file. Sample usage in a C/C++ source is as follows:\n#\n# #ifdef HAVE_LIBZ\n# #include \n# #endif /* HAVE_LIBZ */\n#\n# LICENSE\n#\n# Copyright (c) 2008 Loic Dachary \n#\n# This program is free software; you can redistribute it and/or modify it\n# under the terms of the GNU General Public License as published by the\n# Free Software Foundation; either version 2 of the License, or (at your\n# option) any later version.\n#\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n# Public License for more details.\n#\n# You should have received a copy of the GNU General Public License along\n# with this program. If not, see .\n#\n# As a special exception, the respective Autoconf Macro's copyright owner\n# gives unlimited permission to copy, distribute and modify the configure\n# scripts that are the output of Autoconf when processing the Macro. You\n# need not follow the terms of the GNU General Public License when using\n# or distributing such scripts, even though portions of the text of the\n# Macro appear in them. The GNU General Public License (GPL) does govern\n# all other use of the material that constitutes the Autoconf Macro.\n#\n# This special exception to the GPL applies to versions of the Autoconf\n# Macro released by the Autoconf Archive. When you make and distribute a\n# modified version of the Autoconf Macro, you may extend this special\n# exception to the GPL to apply to your modified version as well.\n\n#serial 7\n\nAU_ALIAS([CHECK_ZLIB], [AX_CHECK_ZLIB])\nAC_DEFUN([AX_CHECK_ZLIB],\n#\n# Handle user hints\n#\n[AC_MSG_CHECKING(if zlib is wanted)\nAC_ARG_WITH(zlib,\n[ --with-zlib=DIR root directory path of zlib installation [defaults to\n /usr/local or /usr if not found in /usr/local]\n --without-zlib to disable zlib usage completely],\n[if test \"$withval\" != no ; then\n AC_MSG_RESULT(yes)\n if test -d \"$withval\"\n then\n ZLIB_HOME=\"$withval\"\n else\n AC_MSG_WARN([Sorry, $withval does not exist, checking usual places])\n fi\nelse\n AC_MSG_RESULT(no)\nfi],\n[AC_MSG_RESULT(yes)])\n\nZLIB_HOME=/usr/local\nif test ! -f \"${ZLIB_HOME}/include/zlib.h\"\nthen\n ZLIB_HOME=/usr\nfi\n\n#\n# Locate zlib, if wanted\n#\nif test -n \"${ZLIB_HOME}\"\nthen\n ZLIB_OLD_LDFLAGS=$LDFLAGS\n ZLIB_OLD_CPPFLAGS=$LDFLAGS\n LDFLAGS=\"$LDFLAGS -L${ZLIB_HOME}/lib\"\n CPPFLAGS=\"$CPPFLAGS -I${ZLIB_HOME}/include\"\n AC_LANG_SAVE\n AC_LANG_C\n AC_CHECK_LIB(z, inflateEnd, [zlib_cv_libz=yes], [zlib_cv_libz=no])\n AC_CHECK_HEADER(zlib.h, [zlib_cv_zlib_h=yes], [zlib_cv_zlib_h=no])\n AC_LANG_RESTORE\n if test \"$zlib_cv_libz\" = \"yes\" -a \"$zlib_cv_zlib_h\" = \"yes\"\n then\n #\n # If both library and header were found, use them\n #\n AC_CHECK_LIB(z, inflateEnd)\n AC_MSG_CHECKING(zlib in ${ZLIB_HOME})\n AC_MSG_RESULT(ok)\n\t\t\t\tZLIB=\"-lz\"\n\t\t\t\tAC_SUBST(ZLIB)\n else\n #\n # If either header or library was not found, revert and bomb\n #\n AC_MSG_CHECKING(zlib in ${ZLIB_HOME})\n LDFLAGS=\"$ZLIB_OLD_LDFLAGS\"\n CPPFLAGS=\"$ZLIB_OLD_CPPFLAGS\"\n AC_MSG_RESULT(failed)\n AC_MSG_ERROR(either specify a valid zlib installation with --with-zlib=DIR or disable zlib usage with --without-zlib)\n fi\nfi\n\n])\n" }, { "path": "ax_openmp.m4", "content": " # AC_C_OPENMP\n # -----------\n # Check which options need to be passed to the C compiler to support OpenMP.\n # Set the OPENMP_CFLAGS variable to these options.\n # The options are necessary at compile time (so the #pragmas are understood)\n # and at link time (so the appropriate library is linked with).\n # This macro takes care to not produce redundant options if $CC $CFLAGS \nalready\n # supports OpenMP. It also is careful to not pass options to compilers that\n # misinterpret them; for example, most compilers accept \"-openmp\" and create\n # an output file called 'penmp' rather than activating OpenMP support.\n AC_DEFUN([AC_C_OPENMP],\n [\n AC_MSG_CHECKING([whether to use OpenMP])\n AC_ARG_ENABLE(openmp,\n [AS_HELP_STRING([--disable-openmp], [do not use OpenMP])],\n [],\n [enable_openmp=yes])\n AC_MSG_RESULT([$enable_openmp])\n OPENMP_CFLAGS=\n if test \"$enable_openmp\" = yes; then\n AC_MSG_CHECKING([for $CC option to support OpenMP])\n AC_CACHE_VAL([ac_cv_prog_cc_openmp], [\n ac_cv_prog_cc_openmp=unsupported\n AC_LINK_IFELSE([\n #ifndef _OPENMP\n choke me\n #endif\n #include \n int main () { return omp_get_num_threads (); }\n ], [ac_cv_prog_cc_openmp=\"none needed\"])\n if test \"$ac_cv_prog_cc_openmp\" = unsupported; then\n dnl Try these flags:\n dnl GCC >= 4.2 -fopenmp\n dnl SunPRO C -xopenmp\n dnl Intel C -openmp\n dnl SGI C, PGI C -mp\n dnl Tru64 Compaq C -omp\n dnl IBM C (AIX, Linux) -qsmp=omp\n for brand in GCC SunPRO Intel SGI/PGI Compaq IBM; do\n case $brand in\n GCC)\n ac_conditional='defined __GNUC__'\n ac_option='-fopenmp' ;;\n SunPRO)\n ac_conditional='defined __SUNPRO_C || defined __SUNPRO_CC'\n ac_option='-xopenmp' ;;\n Intel)\n ac_conditional='defined __INTEL_COMPILER'\n ac_option='-openmp' ;;\n SGI/PGI)\n ac_conditional='defined __sgi || defined __PGI || defined \n__PGIC__'\n ac_option='-mp' ;;\n Compaq)\n ac_conditional='defined __DECC || defined __DECCXX'\n ac_option='-omp' ;;\n IBM)\n ac_conditional='defined __xlc__ || defined __xlC__'\n ac_option='-qsmp=omp' ;;\n esac\n if test $brand = GCC; then\n if test \"$GCC\" = yes; then\n ac_openmp_result=yes\n else\n ac_openmp_result=no\n fi\n else\n AC_EGREP_CPP([Brand], [\n #if $ac_conditional\n Brand\n #endif\n ], [ac_openmp_result=yes], [ac_openmp_result=no])\n fi\n if test $ac_openmp_result = yes; then\n ac_save_CFLAGS=$CFLAGS\n CFLAGS=\"$CFLAGS $ac_option\"\n AC_LINK_IFELSE([\n #ifndef _OPENMP\n choke me\n #endif\n #include \n int main () { return omp_get_num_threads (); }\n ], [ac_cv_prog_cc_openmp=$ac_option])\n CFLAGS=$ac_save_CFLAGS\n break\n fi\n done\n fi\n ])\n AC_MSG_RESULT([$ac_cv_prog_cc_openmp])\n case $ac_cv_prog_cc_openmp in\n \"none needed\" | unsupported)\n OPENMP_CFLAGS= ;;\n *)\n OPENMP_CFLAGS=$ac_cv_prog_cc_openmp ;;\n esac\n fi\n AC_SUBST([OPENMP_CFLAGS])\n ])\n\n" }, { "path": "boost.m4", "content": "dnl AC_PATH_BOOST([MINIMUM-VERSION], [ACTION-IF-FOUND], [ACTION-IF-NOT-FOUND])\ndnl Test for the Boost C++ libraries of a particular version (or newer)\ndnl Defines:\ndnl BOOST_CXXFLAGS to the set of flags required to compiled Boost\nAC_DEFUN([AC_PATH_BOOST], \n[\n BOOST_CXXFLAGS=\"\"\n path_given=\"no\"\n\ndnl Extract the path name from a --with-boost=PATH argument\n AC_ARG_WITH(boost,\n [ --with-boost=PATH absolute path name where the Boost C++ libraries\n reside. Alternatively, the BOOST_ROOT environment variable will be used],\n if test \"$withval\" = no ; then\n\tpath_given=\"no\"\n\tBOOST_CXXFLAGS=\"\"\n else\n if test \"$withval\" != yes ; then\n path_given=\"yes\"\n BOOST_CXXFLAGS=\"-I$withval\"\n BOOST_ROOT=\"$withval\"\n fi\n fi \n )\n\ndnl If no path with given and there is a BOOST_ROOT environment variable,\ndnl use it\n if test \"$path_given\" = \"no\"; then\n if test \"x$BOOST_ROOT\" = \"x\"; then\n BOOST_CXXFLAGS=\"\"\n else\n BOOST_CXXFLAGS=\"-I$BOOST_ROOT\"\n fi\n fi\n\n boost_min_version=ifelse([$1], ,1.20.0,$1)\n\n AC_LANG_SAVE\n AC_LANG_CPLUSPLUS\n OLD_CXX_FLAGS=$CXXFLAGS\n CXXFLAGS=\"$CXXFLAGS $BOOST_CXXFLAGS\"\n AC_MSG_CHECKING([for the Boost C++ libraries, version $boost_min_version or newer])\n AC_TRY_COMPILE(\n [\n#include \n],\n [],\n [\n have_boost=\"yes\"\n ],\n [\n AC_MSG_RESULT(no)\n have_boost=\"no\"\n ifelse([$3], , :, [$3])\n ])\n\n if test \"$have_boost\" = \"yes\"; then\n WANT_BOOST_MAJOR=`expr $boost_min_version : '\\([[0-9]]\\+\\)'`\n WANT_BOOST_MINOR=`expr $boost_min_version : '[[0-9]]\\+\\.\\([[0-9]]\\+\\)'`\n WANT_BOOST_SUB_MINOR=`expr $boost_min_version : '[[0-9]]\\+\\.[[0-9]]\\+\\.\\([[0-9]]\\+\\)'`\n WANT_BOOST_VERSION=`expr $WANT_BOOST_MAJOR \\* 100000 \\+ $WANT_BOOST_MINOR \\* 100 \\+ $WANT_BOOST_SUB_MINOR`\n\n AC_TRY_COMPILE(\n [\n#include \n],\n [\n#if BOOST_VERSION >= $WANT_BOOST_VERSION\n// Everything is okay\n#else\n# error Boost version is too old\n#endif\n\n],\n [\n AC_MSG_RESULT(yes)\n ifelse([$2], , :, [$2])\n ],\n [\n AC_MSG_RESULT([no, version of installed Boost libraries is too old])\n ifelse([$3], , :, [$3])\n ])\n fi\n CXXFLAGS=$OLD_CXXFLAGS\n AC_LANG_RESTORE\n])\n\n" }, { "path": "configure.ac", "content": "m4_include([ax_boost_base.m4])\nm4_include([ax_boost_thread.m4])\nm4_include([ax_boost_system.m4])\nm4_include([ax_boost_serialization.m4])\nm4_include([ax_boost_filesystem.m4])\nm4_include([ax_bam.m4])\nm4_include([ax_check_eigen.m4])\nm4_include([ax_check_zlib.m4])\n\ndefine([svnversion], esyscmd([sh -c \"svnversion|tr -d '\\n'\"]))dnl\nAC_INIT([cufflinks], [2.2.2], [cole@cs.umd.edu])\nAC_DEFINE(SVN_REVISION, \"svnversion\", [SVN Revision])\n\nAC_CONFIG_SRCDIR([config.h.in])\nAC_CONFIG_HEADERS([config.h])\nAC_CONFIG_AUX_DIR([build-aux])\nAM_INIT_AUTOMAKE\n\n#AM_PATH_CPPUNIT(1.10.2)\n\nAC_ARG_VAR(PYTHON, [python program])\n\n # Make sure CXXFLAGS is defined so that AC_PROG_CXX doesn't set it.\nCXXFLAGS=\"$CXXFLAGS\"\nCFLAGS=\"$CFLAGS\"\n\nAC_LANG(C)\n\n# Checks for programs.\nAC_PROG_AWK\nAC_PROG_CXX\nAC_PROG_CC\nAC_PROG_MAKE_SET\nAC_PROG_RANLIB\nAC_PROG_INSTALL\nm4_ifdef([AM_PROG_AR], [AM_PROG_AR])\nAM_PATH_PYTHON([2.4])\nAX_BOOST_BASE([1.47.0])\nAX_BAM\nAX_BOOST_SYSTEM\nAX_BOOST_SERIALIZATION\nAX_BOOST_FILESYSTEM\nAX_BOOST_THREAD\nAX_CHECK_ZLIB()\n\n#PKG_CHECK_MODULES([EIGEN3], [eigen3])\nAX_EIGEN\n\n# Checks for header files.\nAC_CHECK_HEADERS([stdlib.h string.h unistd.h])\n\n# Checks for typedefs, structures, and compiler characteristics.\nAC_HEADER_STDBOOL\nAC_C_INLINE\nAC_TYPE_PID_T\nAC_TYPE_SIZE_T\nAC_CHECK_TYPES([ptrdiff_t])\n\n\n# Checks for library functions.\n#AC_FUNC_FORK\n#AC_CHECK_FUNCS([floor memmove pow regcomp sqrt strchr strcspn strspn strstr])\n\n# check the platform\nAC_CANONICAL_HOST\n\n# set CFLAGS and CXXFLAGS\nuser_CFLAGS=${CFLAGS}\ngeneric_CFLAGS=\"-Wall -Wno-strict-aliasing -g -gdwarf-2 -Wunused -Wuninitialized -ftemplate-depth-1024\"\next_CFLAGS=\"\"\ndebug_CFLAGS=\"\"\n#echo \"${host_cpu}-${host_os}\"\ncase \"${host_cpu}-${host_os}\" in\n i*86-*linux*)\n\t ext_CFLAGS=\"-march=i686\";;\n i*86-darwin*)\n\tCFLAGS=\"-m64\"\n\tAC_COMPILE_IFELSE([AC_LANG_PROGRAM], [ext_CFLAGS=\"-arch x86_64\"], []);;\n *)\n AC_MSG_CHECKING([if gcc accepts -m64])\n CFLAGS=\"-m64\"\n AC_COMPILE_IFELSE([AC_LANG_PROGRAM], [ext_CFLAGS=\"-m64\"; AC_MSG_RESULT([yes])],\n\t\t\t\t\t [ext_CFLAGS=\"-D_FILE_OFFSET_BITS=64\"; AC_MSG_RESULT([no])]);;\nesac\n\nAC_ARG_ENABLE(vectorize, [ --enable-vectorize Enable GCC auto-vectorization],\n\t\t\t [ext_CFLAGS=\"${ext_CFLAGS} -ftree-vectorize -msse3 -ffast-math -ftree-vectorizer-verbose=99\"], [])\n\nAC_ARG_ENABLE(intel64, [ --enable-intel64 optimize for Intel64 CPU such as Xeon and Core2],\n\t\t\t [ext_CFLAGS=\"${ext_CFLAGS} -march=nocona\"], [])\n\nAC_ARG_ENABLE([debug],\n [AS_HELP_STRING([--enable-debug],\n\t [enable debugging info (default is no)])],\n [], [enable_debug=no])\n\n\t\t\t \nAC_ARG_ENABLE([optim],\n [AS_HELP_STRING([--enable-optim@<:@=0|1|2|3@:>@],\n\t [set optimization level (default is 3)])],\n [if test \"x$enable_optim\" = xyes; then enable_optim=3; fi],\n [enable_optim=3])\n\nAS_IF([test \"x$enable_optim\" != xno], [ext_CFLAGS=\"$ext_CFLAGS -O$enable_optim\"])\t\n\nAS_IF([test \"x$enable_debug\" = xyes],\n [debug_CFLAGS=\"-DDEBUG\"],\n [debug_CFLAGS=\"-DNDEBUG\"])\n\t \nAC_ARG_ENABLE(profiling, [ --enable-profiling enable profiling with google-perftools],\n\t [ext_LDFLAGS=\"-lprofiler -ltcmalloc\"], [])\n\t \nCFLAGS=\"${generic_CFLAGS} ${ext_CFLAGS} ${user_CFLAGS} ${debug_CFLAGS} ${OPENMP_CFLAGS}\"\nCXXFLAGS=\"$CFLAGS -std=c++03\"\nCXXFLAGS=\"${CXXFLAGS} ${BOOST_CPPFLAGS} ${BAM_CPPFLAGS} ${EIGEN_CPPFLAGS}\"\nuser_LDFLAGS=\"$LDFLAGS\"\nLDFLAGS=\"${ext_LDFLAGS} ${user_LDFLAGS}\"\n\n# Checks for structures/functions that can be used to determine system memory\nAC_CHECK_MEMBERS([struct sysinfo.totalram], [], [], [#include ])\nAC_CHECK_DECLS([sysctl, CTL_HW, HW_PHYSMEM], [], [], [#include ])\n\nAM_INIT_AUTOMAKE([-Wall tar-pax foreign])\n\nAC_CONFIG_FILES([Makefile\t\t\t\t \n\t\t\t\t src/Makefile])\n\nAC_OUTPUT\n# dump some configuration confirmations\necho \\\n\"\n-- ${PACKAGE_STRING} Configuration Results --\n C++ compiler: ${CXX} ${CXXFLAGS} ${LDFLAGS}\"\n\nif test x\"${GCC}\" = x\"yes\" ; then\n gcc_version=`${CC} --version | head -n 1`\n echo \" GCC version: ${gcc_version}\"\nelse\n gcc_version=''\nfi\n\necho \\\n\" Host System type: ${host}\n Install prefix: ${prefix}\n Install eprefix: ${exec_prefix}\n\n See config.h for further configuration information.\n Email <${PACKAGE_BUGREPORT}> with questions and bug reports.\n\"\n\nif test x\"${PYTHON}\" = x\":\" || ! test -x \"${PYTHON}\"; then\n echo \"WARNING! python was not found and is required to run some utility scripts\"\n echo \" We recommend installing python and pointing configure to the installed location\"\nfi\n\n" }, { "path": "cufflinks.xcodeproj/cole.pbxuser", "content": "// !$*UTF8*$!\n{\n\t08FB7793FE84155DC02AAC07 /* Project object */ = {\n\t\tactiveArchitecturePreference = x86_64;\n\t\tactiveBuildConfigurationName = Debug;\n\t\tactiveExecutable = EDD0CD9D10C72AC200AF308F /* cufflinks */;\n\t\tactiveTarget = 8DD76F620486A84900D96B5E /* cufflinks */;\n\t\taddToTargets = (\n\t\t\tEDFE39B0122BE65600F24CD3 /* cuffcluster */,\n\t\t);\n\t\tbreakpoints = (\n\t\t\tED0E3F3F12CBA86F00074D2E /* sorting_hat.cpp:725 */,\n\t\t\tED0E3F4312CBA89F00074D2E /* sorting_hat.cpp:735 */,\n\t\t\tEDC57AC912F7D53300493081 /* cufflinks.cpp:483 */,\n\t\t\tEDC57ACD12F7D56A00493081 /* scaffolds.cpp:539 */,\n\t\t\tEDC57AE812F8578000493081 /* cufflinks.cpp:543 */,\n\t\t\tEDC57AEE12F857FF00493081 /* filters.cpp:506 */,\n\t\t\tEDDC69AA1304DCE000637B75 /* cuffdiff.cpp:435 */,\n\t\t\tEDDC69BF1304DEF800637B75 /* cuffdiff.cpp:458 */,\n\t\t\tEDDC69C51304DF9000637B75 /* cuffdiff.cpp:452 */,\n\t\t\tEDDC69D41304E09600637B75 /* differential.cpp:28 */,\n\t\t\tEDDC69EC1304E2C600637B75 /* differential.cpp:35 */,\n\t\t\tEDC583B21308455A0013090E /* cufflinks.cpp:646 */,\n\t\t\tEDC583C0130847140013090E /* filters.cpp:867 */,\n\t\t\tEDC583CA130853530013090E /* filters.cpp:897 */,\n\t\t\tEDC583F41308558F0013090E /* filters.cpp:936 */,\n\t\t\tEDC583FF130855D10013090E /* filters.cpp:927 */,\n\t\t\tEDC58405130856060013090E /* filters.cpp:916 */,\n\t\t\tEDC5840D130856B00013090E /* filters.cpp:931 */,\n\t\t\tEDC5843C130859F80013090E /* filters.cpp:894 */,\n\t\t\tEDC5844A13085AD90013090E /* filters.cpp:930 */,\n\t\t\tEDC5845C13085BDA0013090E /* filters.cpp:912 */,\n\t\t\tEDC584A8130863A20013090E /* filters.cpp:985 */,\n\t\t\tEDC584AC130863FE0013090E /* filters.cpp:992 */,\n\t\t);\n\t\tcodeSenseManager = EDA5150110C72929000A4F41 /* Code sense */;\n\t\texecutables = (\n\t\t\tEDD0CD9D10C72AC200AF308F /* cufflinks */,\n\t\t\tEDD0CE2310C72F7D00AF308F /* cuffdiff */,\n\t\t\tEDD6A9D611598ABA00D3F668 /* cuffcompare */,\n\t\t\tEDD6AA241159BA2E00D3F668 /* Unit Tests */,\n\t\t\tEDD94CD31208E48B00AA1086 /* gtf_to_sam */,\n\t\t\tEDFE39CF122BE65600F24CD3 /* cuffcluster */,\n\t\t\tED41B839127602C60000B5A2 /* gtf_reads */,\n\t\t);\n\t\tperUserDictionary = {\n\t\t\t\"PBXConfiguration.PBXBreakpointsDataSource.v1:1CA23EDF0692099D00951B8B\" = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXBreakpointsDataSource_BreakpointID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t20,\n\t\t\t\t\t20,\n\t\t\t\t\t165,\n\t\t\t\t\t20,\n\t\t\t\t\t165,\n\t\t\t\t\t165,\n\t\t\t\t\t165,\n\t\t\t\t\t20,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = (\n\t\t\t\t\tPBXBreakpointsDataSource_ActionID,\n\t\t\t\t\tPBXBreakpointsDataSource_TypeID,\n\t\t\t\t\tPBXBreakpointsDataSource_BreakpointID,\n\t\t\t\t\tPBXBreakpointsDataSource_UseID,\n\t\t\t\t\tPBXBreakpointsDataSource_LocationID,\n\t\t\t\t\tPBXBreakpointsDataSource_ConditionID,\n\t\t\t\t\tPBXBreakpointsDataSource_IgnoreCountID,\n\t\t\t\t\tPBXBreakpointsDataSource_ContinueID,\n\t\t\t\t);\n\t\t\t};\n\t\t\tPBXConfiguration.PBXFileTableDataSource3.PBXExecutablesDataSource = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXExecutablesDataSource_NameID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t22,\n\t\t\t\t\t300,\n\t\t\t\t\t372,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = (\n\t\t\t\t\tPBXExecutablesDataSource_ActiveFlagID,\n\t\t\t\t\tPBXExecutablesDataSource_NameID,\n\t\t\t\t\tPBXExecutablesDataSource_CommentsID,\n\t\t\t\t);\n\t\t\t};\n\t\t\tPBXConfiguration.PBXFileTableDataSource3.PBXFileTableDataSource = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXFileDataSource_Filename_ColumnID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t20,\n\t\t\t\t\t484,\n\t\t\t\t\t20,\n\t\t\t\t\t48,\n\t\t\t\t\t43,\n\t\t\t\t\t43,\n\t\t\t\t\t20,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = (\n\t\t\t\t\tPBXFileDataSource_FiletypeID,\n\t\t\t\t\tPBXFileDataSource_Filename_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Built_ColumnID,\n\t\t\t\t\tPBXFileDataSource_ObjectSize_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Errors_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Warnings_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Target_ColumnID,\n\t\t\t\t);\n\t\t\t};\n\t\t\tPBXConfiguration.PBXFileTableDataSource3.PBXFindDataSource = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXFindDataSource_LocationID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t200,\n\t\t\t\t\t750.20849609375,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = (\n\t\t\t\t\tPBXFindDataSource_MessageID,\n\t\t\t\t\tPBXFindDataSource_LocationID,\n\t\t\t\t);\n\t\t\t};\n\t\t\tPBXConfiguration.PBXFileTableDataSource3.PBXSymbolsDataSource = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXSymbolsDataSource_SymbolNameID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t16,\n\t\t\t\t\t200,\n\t\t\t\t\t50,\n\t\t\t\t\t513,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = (\n\t\t\t\t\tPBXSymbolsDataSource_SymbolTypeIconID,\n\t\t\t\t\tPBXSymbolsDataSource_SymbolNameID,\n\t\t\t\t\tPBXSymbolsDataSource_SymbolTypeID,\n\t\t\t\t\tPBXSymbolsDataSource_ReferenceNameID,\n\t\t\t\t);\n\t\t\t};\n\t\t\tPBXConfiguration.PBXFileTableDataSource3.XCSCMDataSource = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXFileDataSource_Filename_ColumnID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t20,\n\t\t\t\t\t20,\n\t\t\t\t\t815,\n\t\t\t\t\t20,\n\t\t\t\t\t48.16259765625,\n\t\t\t\t\t43,\n\t\t\t\t\t43,\n\t\t\t\t\t20,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = (\n\t\t\t\t\tPBXFileDataSource_SCM_ColumnID,\n\t\t\t\t\tPBXFileDataSource_FiletypeID,\n\t\t\t\t\tPBXFileDataSource_Filename_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Built_ColumnID,\n\t\t\t\t\tPBXFileDataSource_ObjectSize_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Errors_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Warnings_ColumnID,\n\t\t\t\t\tPBXFileDataSource_Target_ColumnID,\n\t\t\t\t);\n\t\t\t};\n\t\t\tPBXConfiguration.PBXTargetDataSource.PBXTargetDataSource = {\n\t\t\t\tPBXFileTableDataSourceColumnSortingDirectionKey = \"-1\";\n\t\t\t\tPBXFileTableDataSourceColumnSortingKey = PBXFileDataSource_Filename_ColumnID;\n\t\t\t\tPBXFileTableDataSourceColumnWidthsKey = (\n\t\t\t\t\t20,\n\t\t\t\t\t510,\n\t\t\t\t\t60,\n\t\t\t\t\t20,\n\t\t\t\t\t48,\n\t\t\t\t\t43,\n\t\t\t\t\t43,\n\t\t\t\t);\n\t\t\t\tPBXFileTableDataSourceColumnsKey = 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sourcecode.cpp.h;\n\t\tname = sp_counted_impl.hpp;\n\t\tpath = \"/usr/local/include/boost-1_41/boost/smart_ptr/detail/sp_counted_impl.hpp\";\n\t\tsourceTree = \"\";\n\t};\n\tEDBCDDFB1235EEB60050A8E4 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDFE38AC12298E3400F24CD3 /* sp_counted_base_gcc_x86.hpp */;\n\t\tname = \"sp_counted_base_gcc_x86.hpp: 133\";\n\t\trLen = 0;\n\t\trLoc = 2707;\n\t\trType = 0;\n\t\tvrLen = 807;\n\t\tvrLoc = 2328;\n\t};\n\tEDBCDDFC1235EEB60050A8E4 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDBEA60411EA75CD00C661A7 /* shared_count.hpp */;\n\t\tname = \"shared_count.hpp: 223\";\n\t\trLen = 0;\n\t\trLoc = 4802;\n\t\trType = 0;\n\t\tvrLen = 675;\n\t\tvrLoc = 4534;\n\t};\n\tEDBCDF80123721790050A8E4 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152810C72963000A4F41 /* GBase.cpp */;\n\t\tname = \"GBase.cpp: 15\";\n\t\trLen = 37;\n\t\trLoc = 449;\n\t\trType = 0;\n\t\tvrLen = 1048;\n\t\tvrLoc = 353;\n\t};\n\tEDBEA60411EA75CD00C661A7 /* shared_count.hpp */ = {\n\t\tisa = PBXFileReference;\n\t\tlastKnownFileType = sourcecode.cpp.h;\n\t\tname = shared_count.hpp;\n\t\tpath = \"/usr/local/include/boost-1_41/boost/smart_ptr/detail/shared_count.hpp\";\n\t\tsourceTree = \"\";\n\t};\n\tEDBEA60611EA75CD00C661A7 /* stl_iterator.h */ = {\n\t\tisa = PBXFileReference;\n\t\tlastKnownFileType = sourcecode.c.h;\n\t\tname = stl_iterator.h;\n\t\tpath = \"/Developer/SDKs/MacOSX10.6.sdk/usr/include/c++/4.2.1/bits/stl_iterator.h\";\n\t\tsourceTree = \"\";\n\t};\n\tEDC57A9512F7CC9F00493081 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5151F10C72963000A4F41 /* assemble.cpp */;\n\t\tname = \"assemble.cpp: 201\";\n\t\trLen = 36;\n\t\trLoc = 4744;\n\t\trType = 0;\n\t\tvrLen = 1463;\n\t\tvrLoc = 4729;\n\t};\n\tEDC57AC912F7D53300493081 /* cufflinks.cpp:483 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152510C72963000A4F41 /* cufflinks.cpp */;\n\t\tfunctionName = \"pseudohits_from_ref(vector >& ref_scaffs, vector& pseudohits, int hit_len)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 483;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.205485;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC57ACD12F7D56A00493081 /* scaffolds.cpp:539 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5153110C72963000A4F41 /* scaffolds.cpp */;\n\t\tfunctionName = \"Scaffold::sub_scaffold(Scaffold& sub_scaff, int g_left, int match_length)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 539;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.205942;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC57AE012F7D66C00493081 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDC57AE112F7D66C00493081 /* shared_ptr.hpp */;\n\t\tname = \"shared_ptr.hpp: 418\";\n\t\trLen = 0;\n\t\trLoc = 10346;\n\t\trType = 0;\n\t\tvrLen = 750;\n\t\tvrLoc = 9887;\n\t};\n\tEDC57AE112F7D66C00493081 /* shared_ptr.hpp */ = {\n\t\tisa = PBXFileReference;\n\t\tlastKnownFileType = sourcecode.cpp.h;\n\t\tname = shared_ptr.hpp;\n\t\tpath = /usr/local/include/boost/smart_ptr/shared_ptr.hpp;\n\t\tsourceTree = \"\";\n\t};\n\tEDC57AE812F8578000493081 /* cufflinks.cpp:543 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152510C72963000A4F41 /* cufflinks.cpp */;\n\t\tfunctionName = \"scaffolds_for_bundle(const HitBundle& bundle, vector >& scaffolds, vector >* ref_scaffs = NULL, BundleStats* stats = NULL)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 543;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.206342;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC57AEE12F857FF00493081 /* filters.cpp:506 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"filter_hits(int bundle_length, int bundle_left, vector& hits)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 506;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.206833;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC57AF312F8580200493081 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDBEA60611EA75CD00C661A7 /* stl_iterator.h */;\n\t\tname = \"stl_iterator.h: 653\";\n\t\trLen = 0;\n\t\trLoc = 20926;\n\t\trType = 0;\n\t\tvrLen = 1260;\n\t\tvrLoc = 20301;\n\t};\n\tEDC57AF612F8580200493081 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5150B10C72963000A4F41 /* bundles.h */;\n\t\tname = \"bundles.h: 82\";\n\t\trLen = 0;\n\t\trLoc = 2226;\n\t\trType = 0;\n\t\tvrLen = 1237;\n\t\tvrLoc = 1684;\n\t};\n\tEDC57B0C12F859B500493081 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5151910C72963000A4F41 /* scaffolds.h */;\n\t\tname = \"scaffolds.h: 204\";\n\t\trLen = 6;\n\t\trLoc = 4712;\n\t\trType = 0;\n\t\tvrLen = 841;\n\t\tvrLoc = 4559;\n\t};\n\tEDC57B1212F85B6E00493081 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5153110C72963000A4F41 /* scaffolds.cpp */;\n\t\tname = \"scaffolds.cpp: 1417\";\n\t\trLen = 0;\n\t\trLoc = 36434;\n\t\trType = 0;\n\t\tvrLen = 1537;\n\t\tvrLoc = 36099;\n\t};\n\tEDC58391130833E90013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDACE431114EFF5C009024DB /* differential.cpp */;\n\t\tname = \"differential.cpp: 35\";\n\t\trLen = 6;\n\t\trLoc = 771;\n\t\trType = 0;\n\t\tvrLen = 1424;\n\t\tvrLoc = 478;\n\t};\n\tEDC58392130833E90013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = 0ACD78C612E6647B00310E07 /* update_check.h */;\n\t\tname = \"update_check.h: 86\";\n\t\trLen = 32;\n\t\trLoc = 2052;\n\t\trType = 0;\n\t\tvrLen = 1451;\n\t\tvrLoc = 1052;\n\t};\n\tEDC58393130833E90013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5151510C72963000A4F41 /* gff.h */;\n\t\tname = \"gff.h: 328\";\n\t\trLen = 6;\n\t\trLoc = 8355;\n\t\trType = 0;\n\t\tvrLen = 1526;\n\t\tvrLoc = 7906;\n\t};\n\tEDC58394130833E90013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152310C72963000A4F41 /* cuffcompare.cpp */;\n\t\trLen = 11;\n\t\trLoc = 11530;\n\t\trType = 0;\n\t};\n\tEDC583B21308455A0013090E /* cufflinks.cpp:646 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152510C72963000A4F41 /* cufflinks.cpp */;\n\t\tfunctionName = \"scaffolds_for_bundle(const HitBundle& bundle, vector >& scaffolds, vector >* ref_scaffs = NULL, BundleStats* stats = NULL)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 646;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.208697;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC583B6130845640013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5150E10C72963000A4F41 /* filters.h */;\n\t\tname = \"filters.h: 39\";\n\t\trLen = 5;\n\t\trLoc = 928;\n\t\trType = 0;\n\t\tvrLen = 968;\n\t\tvrLoc = 0;\n\t};\n\tEDC583B7130845640013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5151C10C72963000A4F41 /* GHash.hh */;\n\t\tname = \"GHash.hh: 108\";\n\t\trLen = 5;\n\t\trLoc = 4533;\n\t\trType = 0;\n\t\tvrLen = 1765;\n\t\tvrLoc = 3766;\n\t};\n\tEDC583C0130847140013090E /* filters.cpp:867 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 867;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.209119;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC583CA130853530013090E /* filters.cpp:897 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 897;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.209418;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC583F0130855740013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = ED79DCDB12AC63F200ABC8B3 /* stl_vector.h */;\n\t\tname = \"stl_vector.h: 478\";\n\t\trLen = 0;\n\t\trLoc = 16903;\n\t\trType = 0;\n\t\tvrLen = 1624;\n\t\tvrLoc = 16136;\n\t};\n\tEDC583F41308558F0013090E /* filters.cpp:936 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 936;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.209702;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC583FF130855D10013090E /* filters.cpp:927 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 927;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.210135;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC58405130856060013090E /* filters.cpp:916 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 916;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.210451;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC5840D130856B00013090E /* filters.cpp:931 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 931;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.210683;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC5843C130859F80013090E /* filters.cpp:894 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 894;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.210957;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC5844A13085AD90013090E /* filters.cpp:930 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 930;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.211209;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC5845C13085BDA0013090E /* filters.cpp:912 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 912;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.21152;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC584A8130863A20013090E /* filters.cpp:985 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 985;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.211774;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC584AC130863FE0013090E /* filters.cpp:992 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tfunctionName = \"clip_by_3_prime_dropoff(Scaffold& scaff)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 992;\n\t\tlocation = cufflinks;\n\t\tmodificationTime = 319317165.212033;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDC584BD130865E80013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152610C72963000A4F41 /* filters.cpp */;\n\t\tname = \"filters.cpp: 965\";\n\t\trLen = 0;\n\t\trLoc = 28447;\n\t\trType = 0;\n\t\tvrLen = 1534;\n\t\tvrLoc = 27593;\n\t};\n\tEDC584C5130866820013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152210C72963000A4F41 /* common.cpp */;\n\t\tname = \"common.cpp: 46\";\n\t\trLen = 0;\n\t\trLoc = 798;\n\t\trType = 0;\n\t\tvrLen = 1245;\n\t\tvrLoc = 410;\n\t};\n\tEDC584C6130866820013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5150D10C72963000A4F41 /* common.h */;\n\t\tname = \"common.h: 311\";\n\t\trLen = 0;\n\t\trLoc = 7402;\n\t\trType = 0;\n\t\tvrLen = 1998;\n\t\tvrLoc = 5413;\n\t};\n\tEDC584C7130866820013090E /* XCBuildMessageTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tcomments = \"'OPT_3_PRIME_DROPOFF_FRAC' was not declared in this scope\";\n\t\tfRef = EDA5152510C72963000A4F41 /* cufflinks.cpp */;\n\t\tfallbackIsa = XCBuildMessageTextBookmark;\n\t\trLen = 0;\n\t\trLoc = 293;\n\t\trType = 1;\n\t};\n\tEDC584C913087F870013090E /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152510C72963000A4F41 /* cufflinks.cpp */;\n\t\tname = \"cufflinks.cpp: 294\";\n\t\trLen = 0;\n\t\trLoc = 11409;\n\t\trType = 0;\n\t\tvrLen = 1066;\n\t\tvrLoc = 10672;\n\t};\n\tEDD0CD9D10C72AC200AF308F /* cufflinks */ = {\n\t\tisa = PBXExecutable;\n\t\tactiveArgIndices = (\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tYES,\n\t\t\tYES,\n\t\t);\n\t\targumentStrings = (\n\t\t\t\"--max-mle-iterations 4\",\n\t\t\t\"--num-importance-samples 100\",\n\t\t\t\"-G MB8_plus_exons.cannonical_chr_names.gtf\",\n\t\t\ttsr.sam,\n\t\t\t\"-F 0.0\",\n\t\t\t\"-j 0.25\",\n\t\t\t\"-m 194\",\n\t\t\tY3_FYB.sam,\n\t\t\t\"--output-dir test_dir\",\n\t\t\t\"--collapse-thresh 30\",\n\t\t\tshort.sam,\n\t\t\t\"-m 100\",\n\t\t\t\"-j 0.05\",\n\t\t\t\"-F 0.05\",\n\t\t\tnanog.sam,\n\t\t\tY4_IL7R.sam,\n\t\t\tlong.sam,\n\t\t\tTpm3.sam,\n\t\t\tsegfault.sam,\n\t\t\tTh17_2_C6orf125.sam,\n\t\t\t\"-o perfect_abund -G mm9_chr123.gtf sim.sam\",\n\t\t\t\"-s 40\",\n\t\t\t\"-m 50\",\n\t\t\tlarge.sam,\n\t\t\t\"-G dmel-all-no-analysis-r5.12.no_ops_strand_exons.gtf\",\n\t\t\ttophat_out_090428_Graveley_lane2_accepted_hits.sam,\n\t\t\tfixed.sam,\n\t\t\tFubp1.sam,\n\t\t\tmega_bundle_2.sam,\n\t\t\tFhl3.sam,\n\t\t\tFn1.sam,\n\t\t\tmega_bundle_1.sam,\n\t\t\tNek2.sam,\n\t\t\tLypla1.sam,\n\t\t\tadipose_snippet.sam,\n\t\t\tyyy.sam,\n\t\t\t\"-s 30\",\n\t\t\tadh_cluster.sam,\n\t\t\tPfkfb2.sam,\n\t\t\tLgtn.sam,\n\t\t\t\"-G gtf4.gtf\",\n\t\t\tdelme7.sam,\n\t\t\ttophat_out/accepted_hits.bam,\n\t\t\taccepted_hits.bam,\n\t\t\tchr1_307200_308260.no_splicing.sam,\n\t\t\tchr1_307200_308260.sam,\n\t\t\tPEchr1MinQV15_15e6.sam,\n\t\t\tPEchr1MinQV15_15e6.bam,\n\t\t\t\"-o perfect_abund -G mm9_chr123.gtf -p 6 fixed.sam\",\n\t\t\t\"-G sample.gtf\",\n\t\t\t\"-s 4000\",\n\t\t\t\"-i 10\",\n\t\t\t\"-m 169\",\n\t\t\t\"-m 165\",\n\t\t\t\"-G SMTN_asm.gtf\",\n\t\t\tSMTN.bam,\n\t\t\t\"-M mask.gtf\",\n\t\t\txxx.bam,\n\t\t\t\"--library-type solid-fragment\",\n\t\t\t/Volumes/Abel/wt.sr.bam,\n\t\t\t\"-j 0.05\",\n\t\t\t\"-F 0.0\",\n\t\t\t\"-F 0.0\",\n\t\t\t\"Xist-breast.bam\",\n\t\t\t\"Xist-ovary.bam\",\n\t\t\t\"-o xxx\",\n\t\t\t\"-G Homo_sapiens.GRCh37.58.name=id.gtf chr19.sam\",\n\t\t\tIgH.bam,\n\t\t\t\"-o perfect_abund -G mm9_chr123.gtf -p 1 sim.sam\",\n\t\t\tsim.sam,\n\t\t\t\"-G hg18-refGene.gtf\",\n\t\t\tassembly.sorted.bam,\n\t\t\t\"--overhang-tolerance 150\",\n\t\t\txxx.sam,\n\t\t\tSAMD11_NOC2L.bam,\n\t\t\t\"-F 0.0\",\n\t\t\t\"-G mm9_chr123.gtf\",\n\t\t\t\"-G small.gtf\",\n\t\t\tsmall.sam,\n\t\t\t\"-G ref_genes.gtf\",\n\t\t\tsim.bam,\n\t\t\tsim.sam,\n\t\t\tadh_cluster.sam,\n\t\t\t\"-j 0.0\",\n\t\t\tTMEM161B.bam,\n\t\t\tAHNAK.sam,\n\t\t\tORM1_ORM2.bam,\n\t\t\ttiny.sam,\n\t\t\t\"--min-frags-per-transfrag 15\",\n\t\t\tsmall_solid.bam,\n\t\t\tNOC2L_solid.bam,\n\t\t\tmega_bundle.bam,\n\t\t\txxx.sam,\n\t\t\tchr1.top100k.blat.sam,\n\t\t\tO16_18.trim.bam,\n\t\t\t\"--verbose\",\n\t\t\t\"-G ginormous.100806.gtf header\",\n\t\t\t\"--min-frags-per-transfrag 0\",\n\t\t\t\"--min-intron-length 20\",\n\t\t\tatpase.sam,\n\t\t\talignments.sam,\n\t\t\tdeletions.sam,\n\t\t\tDUF221_family_protein,\n\t\t\t\"--min-frags-per-transfrag 0\",\n\t\t\t\"--library-type=fr-firststrand\",\n\t\t\t\"CTCF-locus-H1-hESC-WC-PolyA-005WC+.sam\",\n\t\t\tyyy.sam,\n\t\t\t\"-A 0.0 -j 0.0 -F 0.0 --library-type=transfrags xxx.sam\",\n\t\t\t\"-A 0.0 -j 0.0 -F 0.0 --library-type=transfrags allGTFsam_chr14.sam\",\n\t\t\t\"-G chr19_metaAsm.gtf colon_chr19.2.bam\\t\",\n\t\t\t\"-G htt.gtf htt.sam\",\n\t\t\t\"--GTF-guide 174mb.gtf 174mb.sam\",\n\t\t\t\"--trim-3-avgcov-thresh 10000.0\",\n\t\t\tGNA12.bam,\n\t\t);\n\t\tautoAttachOnCrash = 1;\n\t\tbreakpointsEnabled = 1;\n\t\tconfigStateDict = {\n\t\t\t\"PBXLSLaunchAction-0\" = {\n\t\t\t\tPBXLSLaunchAction = 0;\n\t\t\t\tPBXLSLaunchStartAction = 1;\n\t\t\t\tPBXLSLaunchStdioStyle = 2;\n\t\t\t\tPBXLSLaunchStyle = 0;\n\t\t\t\tclass = PBXLSRunLaunchConfig;\n\t\t\t\tcommandLineArgs = (\n\t\t\t\t);\n\t\t\t\tdisplayName = \"Executable Runner\";\n\t\t\t\tenvironment = {\n\t\t\t\t};\n\t\t\t\tidentifier = com.apple.Xcode.launch.runConfig;\n\t\t\t\tremoteHostInfo = \"\";\n\t\t\t\tstartActionInfo = \"\";\n\t\t\t};\n\t\t};\n\t\tcustomDataFormattersEnabled = 1;\n\t\tdataTipCustomDataFormattersEnabled = 1;\n\t\tdataTipShowTypeColumn = 1;\n\t\tdataTipSortType = 0;\n\t\tdebuggerPlugin = GDBDebugging;\n\t\tdisassemblyDisplayState = 0;\n\t\tdylibVariantSuffix = \"\";\n\t\tenableDebugStr = 1;\n\t\tenvironmentEntries = (\n\t\t);\n\t\texecutableSystemSymbolLevel = 0;\n\t\texecutableUserSymbolLevel = 0;\n\t\tlibgmallocEnabled = 0;\n\t\tname = cufflinks;\n\t\tsavedGlobals = {\n\t\t};\n\t\tshowTypeColumn = 0;\n\t\tsourceDirectories = (\n\t\t);\n\t\tstartupPath = /Users/cole/develop/cufflinks/tests/3prime_testing/hit_driven_0.5/GNA12;\n\t\tvariableFormatDictionary = {\n\t\t};\n\t};\n\tEDD0CE0810C72F7D00AF308F /* cuffdiff */ = {\n\t\tactiveExec = 0;\n\t\texecutables = (\n\t\t\tEDD0CE2310C72F7D00AF308F /* cuffdiff */,\n\t\t);\n\t};\n\tEDD0CE2310C72F7D00AF308F /* cuffdiff */ = {\n\t\tisa = PBXExecutable;\n\t\tactiveArgIndices = (\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tYES,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t);\n\t\targumentStrings = (\n\t\t\t\"-c 10\",\n\t\t\t\"--num-importance-samples 10000\",\n\t\t\t\"--max-mle-iterations 4\",\n\t\t\t\"-c 5000 ref_genes.gtf sim.sam sim.sam\",\n\t\t\tsample1/tophat_out/accepted_hits.sam,\n\t\t\tsample2/tophat_out/accepted_hits.sam,\n\t\t\tconsensus.gtf,\n\t\t\tNeu.gtf,\n\t\t\texp.sam,\n\t\t\t60hr.sam,\n\t\t\t5day.sam,\n\t\t\t7day.sam,\n\t\t\t\"-o xxx_out\",\n\t\t\tSAA2.exon.gtf,\n\t\t\tcontrol1.sam,\n\t\t\tcontrol1.shadow.sam,\n\t\t\ttranscripts.gtf,\n\t\t\t\"SMTN.bam,SMTN.bam\",\n\t\t\t\"SMTN.bam,SMTN.bam\",\n\t\t\t\"mm9_chr123.gtf sim.sam,tmp/sim_replicate_1.sam tmp/sim_replicate_3.sam,tmp/sim_replicate_3.sam\",\n\t\t\t\" -o YYY mm9_chr123.gtf xxx.sam xxx.sam\",\n\t\t\t\"-p 1 mm9_chr123.gtf sim.sam tmp/sim_replicate_1.sam\",\n\t\t\t\"-L rep1,rep2 -o replicate_diff -p 2 ref_genes.gtf sim.sam sim.sam\",\n\t\t\t\"-c 0 --FDR 1.0 mm9_chr123.gtf xxx.sam xxx.sam\",\n\t\t\t\"mm9_chr123.gtf sim.bam sim.bam\",\n\t\t\t\"-p 2 -o replicate_diff mm9_chr123.gtf sim.sam tmp/sim_replicate_1.sam\",\n\t\t\t\"tested_transcriptome.gtf sim.sam sim.sam\",\n\t\t\t\"Ensembl.gtf adipose.bam.mitch.bam kidney.bam.mitch.bam\",\n\t\t\t\"Ensembl.gtf adipose.bam.mitch.bam kidney.bam.mitch.bam skeletal-muscle.bam.mitch.bam adrenal.bam.mitch.bam liver.bam.mitch.bam testes.bam.mitch.bam brain.bam.mitch.bam lung.bam.mitch.bam thyroid.bam.mitch.bam breast.bam.mitch.bam lymph-node.bam.mitch.bam white-blood-cells.bam.mitch.bam colon.bam.mitch.bam ovary.bam.mitch.bam heart.bam.mitch.bam prostate.bam.mitch.bam\",\n\t\t\t\"--min-alignment-count 100 Vkorc1.gtf BAT_D0_Vkorc1.bam BAT_D8_Vkorc1.bam\",\n\t\t\t\"-c 100 AK079912.1.gtf AK079912_Sub_D0.bam AK079912_Sub_D8.bam\",\n\t\t);\n\t\tautoAttachOnCrash = 1;\n\t\tbreakpointsEnabled = 1;\n\t\tconfigStateDict = {\n\t\t\t\"PBXLSLaunchAction-0\" = {\n\t\t\t\tPBXLSLaunchAction = 0;\n\t\t\t\tPBXLSLaunchStartAction = 1;\n\t\t\t\tPBXLSLaunchStdioStyle = 2;\n\t\t\t\tPBXLSLaunchStyle = 0;\n\t\t\t\tclass = PBXLSRunLaunchConfig;\n\t\t\t\tcommandLineArgs = (\n\t\t\t\t);\n\t\t\t\tdisplayName = \"Executable Runner\";\n\t\t\t\tenvironment = {\n\t\t\t\t};\n\t\t\t\tidentifier = com.apple.Xcode.launch.runConfig;\n\t\t\t\tremoteHostInfo = \"\";\n\t\t\t\tstartActionInfo = \"\";\n\t\t\t};\n\t\t};\n\t\tcustomDataFormattersEnabled = 1;\n\t\tdataTipCustomDataFormattersEnabled = 1;\n\t\tdataTipShowTypeColumn = 1;\n\t\tdataTipSortType = 0;\n\t\tdebuggerPlugin = GDBDebugging;\n\t\tdisassemblyDisplayState = 0;\n\t\tdylibVariantSuffix = \"\";\n\t\tenableDebugStr = 1;\n\t\tenvironmentEntries = (\n\t\t);\n\t\texecutableSystemSymbolLevel = 0;\n\t\texecutableUserSymbolLevel = 0;\n\t\tlibgmallocEnabled = 0;\n\t\tname = cuffdiff;\n\t\tsavedGlobals = {\n\t\t};\n\t\tshowTypeColumn = 0;\n\t\tsourceDirectories = (\n\t\t);\n\t\tstartupPath = \"/Users/cole/develop/simulations/RNA-Seq/mm9_small\";\n\t\tvariableFormatDictionary = {\n\t\t};\n\t};\n\tEDD6A7A0115562EE00D3F668 /* Makefile.am */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {662, 1820}}\";\n\t\t\tsepNavSelRange = \"{2798, 0}\";\n\t\t\tsepNavVisRange = \"{1957, 1171}\";\n\t\t};\n\t};\n\tEDD6A9BA11598AB900D3F668 /* cuffcompare */ = {\n\t\tactiveExec = 0;\n\t\texecutables = (\n\t\t\tEDD6A9D611598ABA00D3F668 /* cuffcompare */,\n\t\t);\n\t};\n\tEDD6A9D611598ABA00D3F668 /* cuffcompare */ = {\n\t\tisa = PBXExecutable;\n\t\tactiveArgIndices = (\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tNO,\n\t\t\tYES,\n\t\t);\n\t\targumentStrings = (\n\t\t\t\"-r NCBI36.48.short.gtf\",\n\t\t\tNCBI36.48.short.gtf,\n\t\t\ty1_short.gtf,\n\t\t\ty2_short.gtf,\n\t\t\t\"-r MB8_plus_exons.cannonical_chr_names.gtf\",\n\t\t\ttranscripts.gtf,\n\t\t\t\"-r gencode_v4.annotation.GRCh37.gtf -s ./ -o pooled -d 600 lymph-node.gtf\",\n\t\t);\n\t\tautoAttachOnCrash = 1;\n\t\tbreakpointsEnabled = 1;\n\t\tconfigStateDict = {\n\t\t\t\"PBXLSLaunchAction-0\" = {\n\t\t\t\tPBXLSLaunchAction = 0;\n\t\t\t\tPBXLSLaunchStartAction = 1;\n\t\t\t\tPBXLSLaunchStdioStyle = 2;\n\t\t\t\tPBXLSLaunchStyle = 0;\n\t\t\t\tclass = PBXLSRunLaunchConfig;\n\t\t\t\tcommandLineArgs = (\n\t\t\t\t);\n\t\t\t\tdisplayName = \"Executable Runner\";\n\t\t\t\tenvironment = {\n\t\t\t\t};\n\t\t\t\tidentifier = com.apple.Xcode.launch.runConfig;\n\t\t\t\tremoteHostInfo = \"\";\n\t\t\t\tstartActionInfo = \"\";\n\t\t\t};\n\t\t};\n\t\tcustomDataFormattersEnabled = 1;\n\t\tdataTipCustomDataFormattersEnabled = 1;\n\t\tdataTipShowTypeColumn = 1;\n\t\tdataTipSortType = 0;\n\t\tdebuggerPlugin = GDBDebugging;\n\t\tdisassemblyDisplayState = 0;\n\t\tdylibVariantSuffix = \"\";\n\t\tenableDebugStr = 1;\n\t\tenvironmentEntries = (\n\t\t);\n\t\texecutableSystemSymbolLevel = 0;\n\t\texecutableUserSymbolLevel = 0;\n\t\tlibgmallocEnabled = 0;\n\t\tname = cuffcompare;\n\t\tsavedGlobals = {\n\t\t};\n\t\tshowTypeColumn = 0;\n\t\tsourceDirectories = (\n\t\t);\n\t\tstartupPath = /Users/cole/tmp;\n\t\tvariableFormatDictionary = {\n\t\t};\n\t};\n\tEDD6AA221159BA2E00D3F668 /* Unit Tests */ = {\n\t\tactiveExec = 0;\n\t\texecutables = (\n\t\t\tEDD6AA241159BA2E00D3F668 /* Unit Tests */,\n\t\t);\n\t};\n\tEDD6AA241159BA2E00D3F668 /* Unit Tests */ = {\n\t\tisa = PBXExecutable;\n\t\tactiveArgIndices = (\n\t\t);\n\t\targumentStrings = (\n\t\t);\n\t\tautoAttachOnCrash = 1;\n\t\tbreakpointsEnabled = 1;\n\t\tconfigStateDict = {\n\t\t};\n\t\tcustomDataFormattersEnabled = 1;\n\t\tdataTipCustomDataFormattersEnabled = 1;\n\t\tdataTipShowTypeColumn = 1;\n\t\tdataTipSortType = 0;\n\t\tdebuggerPlugin = GDBDebugging;\n\t\tdisassemblyDisplayState = 0;\n\t\tdylibVariantSuffix = \"\";\n\t\tenableDebugStr = 1;\n\t\tenvironmentEntries = (\n\t\t);\n\t\texecutableSystemSymbolLevel = 0;\n\t\texecutableUserSymbolLevel = 0;\n\t\tlibgmallocEnabled = 0;\n\t\tname = \"Unit Tests\";\n\t\tsavedGlobals = {\n\t\t};\n\t\tshowTypeColumn = 0;\n\t\tsourceDirectories = (\n\t\t);\n\t};\n\tEDD6AA461159BE6500D3F668 /* test_abundances.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {859, 358}}\";\n\t\t\tsepNavSelRange = \"{147, 0}\";\n\t\t\tsepNavVisRange = \"{0, 150}\";\n\t\t};\n\t};\n\tEDD6AA471159BE6500D3F668 /* test_main.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {914, 4615}}\";\n\t\t\tsepNavSelRange = \"{1018, 0}\";\n\t\t\tsepNavVisRange = \"{352, 1162}\";\n\t\t};\n\t};\n\tEDD94B461205C9E800AA1086 /* gtf_to_sam */ = {\n\t\tactiveExec = 0;\n\t\texecutables = (\n\t\t\tEDD94CD31208E48B00AA1086 /* gtf_to_sam */,\n\t\t);\n\t};\n\tEDD94B711205CAF400AA1086 /* gtf_to_sam.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {1069, 3146}}\";\n\t\t\tsepNavSelRange = \"{3814, 12}\";\n\t\t\tsepNavVisRange = \"{3290, 801}\";\n\t\t};\n\t};\n\tEDD94CD31208E48B00AA1086 /* gtf_to_sam */ = {\n\t\tisa = PBXExecutable;\n\t\tactiveArgIndices = (\n\t\t);\n\t\targumentStrings = (\n\t\t);\n\t\tautoAttachOnCrash = 1;\n\t\tbreakpointsEnabled = 1;\n\t\tconfigStateDict = {\n\t\t};\n\t\tcustomDataFormattersEnabled = 1;\n\t\tdataTipCustomDataFormattersEnabled = 1;\n\t\tdataTipShowTypeColumn = 1;\n\t\tdataTipSortType = 0;\n\t\tdebuggerPlugin = GDBDebugging;\n\t\tdisassemblyDisplayState = 0;\n\t\tdylibVariantSuffix = \"\";\n\t\tenableDebugStr = 1;\n\t\tenvironmentEntries = (\n\t\t);\n\t\texecutableSystemSymbolLevel = 0;\n\t\texecutableUserSymbolLevel = 0;\n\t\tlibgmallocEnabled = 0;\n\t\tname = gtf_to_sam;\n\t\tsavedGlobals = {\n\t\t};\n\t\tshowTypeColumn = 0;\n\t\tsourceDirectories = (\n\t\t);\n\t};\n\tEDDBF70912D29DF000130D8A /* cuffcluster.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {1090, 12142}}\";\n\t\t\tsepNavSelRange = \"{24115, 0}\";\n\t\t\tsepNavVisRange = \"{23577, 1037}\";\n\t\t};\n\t};\n\tEDDBF71412D2BD4400130D8A /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDFE39FA122BE7B700F24CD3 /* jensen_shannon.cpp */;\n\t\tname = \"jensen_shannon.cpp: 92\";\n\t\trLen = 0;\n\t\trLoc = 2132;\n\t\trType = 0;\n\t\tvrLen = 955;\n\t\tvrLoc = 1806;\n\t};\n\tEDDBF71512D2BD4400130D8A /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDEBCDF8112DFD7600E2479E /* configure.ac */;\n\t\tname = \"configure.ac: 7\";\n\t\trLen = 0;\n\t\trLoc = 213;\n\t\trType = 0;\n\t\tvrLen = 857;\n\t\tvrLoc = 0;\n\t};\n\tEDDC69911304DBC100637B75 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDD6A7A0115562EE00D3F668 /* Makefile.am */;\n\t\tname = \"Makefile.am: 133\";\n\t\trLen = 0;\n\t\trLoc = 2798;\n\t\trType = 0;\n\t\tvrLen = 1171;\n\t\tvrLoc = 1957;\n\t};\n\tEDDC69AA1304DCE000637B75 /* cuffdiff.cpp:435 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152410C72963000A4F41 /* cuffdiff.cpp */;\n\t\tfunctionName = \"fdr_significance(double fdr, vector& tests)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 435;\n\t\tlocation = cuffdiff;\n\t\tmodificationTime = 319317165.207273;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDDC69BF1304DEF800637B75 /* cuffdiff.cpp:458 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152410C72963000A4F41 /* cuffdiff.cpp */;\n\t\tfunctionName = \"fdr_significance(double fdr, vector& tests)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 458;\n\t\tmodificationTime = 319317165.207641;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 0;\n\t};\n\tEDDC69C51304DF9000637B75 /* cuffdiff.cpp:452 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDA5152410C72963000A4F41 /* cuffdiff.cpp */;\n\t\tfunctionName = \"fdr_significance(double fdr, vector& tests)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 452;\n\t\tmodificationTime = 319317165.207932;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 0;\n\t};\n\tEDDC69D41304E09600637B75 /* differential.cpp:28 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDACE431114EFF5C009024DB /* differential.cpp */;\n\t\tfunctionName = \"test_diffexp(const FPKMContext& curr, const FPKMContext& prev, SampleDifference& test)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 28;\n\t\tlocation = cuffdiff;\n\t\tmodificationTime = 319317165.208195;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDDC69EC1304E2C600637B75 /* differential.cpp:35 */ = {\n\t\tisa = PBXFileBreakpoint;\n\t\tactions = (\n\t\t);\n\t\tbreakpointStyle = 0;\n\t\tcontinueAfterActions = 0;\n\t\tcountType = 0;\n\t\tdelayBeforeContinue = 0;\n\t\tfileReference = EDACE431114EFF5C009024DB /* differential.cpp */;\n\t\tfunctionName = \"test_diffexp(const FPKMContext& curr, const FPKMContext& prev, SampleDifference& test)\";\n\t\thitCount = 0;\n\t\tignoreCount = 0;\n\t\tlineNumber = 35;\n\t\tlocation = cuffdiff;\n\t\tmodificationTime = 319317165.208446;\n\t\toriginalNumberOfMultipleMatches = 1;\n\t\tstate = 2;\n\t};\n\tEDDC69F01304E2FC00637B75 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152410C72963000A4F41 /* cuffdiff.cpp */;\n\t\tname = \"cuffdiff.cpp: 452\";\n\t\trLen = 19;\n\t\trLoc = 13672;\n\t\trType = 0;\n\t\tvrLen = 870;\n\t\tvrLoc = 13185;\n\t};\n\tEDE78A2B11B5F4F0000F8846 /* graph_optimize.h */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {976, 1235}}\";\n\t\t\tsepNavSelRange = \"{2174, 30}\";\n\t\t\tsepNavVisRange = \"{1179, 1378}\";\n\t\t};\n\t};\n\tEDE78A2C11B5F4F0000F8846 /* graph_optimize.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {1373, 9555}}\";\n\t\t\tsepNavSelRange = \"{18780, 0}\";\n\t\t\tsepNavVisRange = \"{2854, 910}\";\n\t\t\tsepNavWindowFrame = \"{{58, 0}, {1222, 778}}\";\n\t\t};\n\t};\n\tEDE78A7811B6E912000F8846 /* scaffold_graph.h */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {996, 697}}\";\n\t\t\tsepNavSelRange = \"{372, 0}\";\n\t\t\tsepNavVisRange = \"{0, 1012}\";\n\t\t};\n\t};\n\tEDE78A7911B6E912000F8846 /* scaffold_graph.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {1132, 4095}}\";\n\t\t\tsepNavSelRange = \"{3213, 5}\";\n\t\t\tsepNavVisRange = \"{2490, 1557}\";\n\t\t};\n\t};\n\tEDE78BC411B716C3000F8846 /* matching_merge.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {662, 1352}}\";\n\t\t\tsepNavSelRange = \"{0, 0}\";\n\t\t\tsepNavVisRange = \"{0, 773}\";\n\t\t};\n\t};\n\tEDE78BC511B716C3000F8846 /* matching_merge.h */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {654, 1482}}\";\n\t\t\tsepNavSelRange = \"{2680, 14}\";\n\t\t\tsepNavVisRange = \"{1739, 1080}\";\n\t\t};\n\t};\n\tEDEBCDF8112DFD7600E2479E /* configure.ac */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {728, 1937}}\";\n\t\t\tsepNavSelRange = \"{213, 0}\";\n\t\t\tsepNavVisRange = \"{0, 857}\";\n\t\t};\n\t};\n\tEDFDE77312BEA9100044EDD0 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDACE430114EFF5C009024DB /* differential.h */;\n\t\tname = \"differential.h: 130\";\n\t\trLen = 8;\n\t\trLoc = 2989;\n\t\trType = 0;\n\t\tvrLen = 1063;\n\t\tvrLoc = 2462;\n\t};\n\tEDFDE78F12BEACA20044EDD0 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152010C72963000A4F41 /* bundles.cpp */;\n\t\tname = \"bundles.cpp: 189\";\n\t\trLen = 0;\n\t\trLoc = 4607;\n\t\trType = 0;\n\t\tvrLen = 872;\n\t\tvrLoc = 5236;\n\t};\n\tEDFE38AC12298E3400F24CD3 /* sp_counted_base_gcc_x86.hpp */ = {\n\t\tisa = PBXFileReference;\n\t\tlastKnownFileType = sourcecode.cpp.h;\n\t\tname = sp_counted_base_gcc_x86.hpp;\n\t\tpath = \"/usr/local/include/boost-1_41/boost/smart_ptr/detail/sp_counted_base_gcc_x86.hpp\";\n\t\tsourceTree = \"\";\n\t};\n\tEDFE393512299AE500F24CD3 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5151B10C72963000A4F41 /* transitive_closure.h */;\n\t\tname = \"transitive_closure.h: 136\";\n\t\trLen = 5;\n\t\trLoc = 4199;\n\t\trType = 0;\n\t\tvrLen = 1910;\n\t\tvrLoc = 3205;\n\t};\n\tEDFE39AF122BE4F400F24CD3 /* sorting_hat.cpp */ = {\n\t\tisa = PBXFileReference;\n\t\tfileEncoding = 4;\n\t\tlastKnownFileType = sourcecode.cpp.cpp;\n\t\tname = sorting_hat.cpp;\n\t\tpath = /Users/cole/develop/cufflinks/src/sorting_hat.cpp;\n\t\tsourceTree = \"\";\n\t};\n\tEDFE39B0122BE65600F24CD3 /* cuffcluster */ = {\n\t\tactiveExec = 0;\n\t\texecutables = (\n\t\t\tEDFE39CF122BE65600F24CD3 /* cuffcluster */,\n\t\t);\n\t};\n\tEDFE39CF122BE65600F24CD3 /* cuffcluster */ = {\n\t\tisa = PBXExecutable;\n\t\tactiveArgIndices = (\n\t\t\tNO,\n\t\t\tYES,\n\t\t\tYES,\n\t\t\tYES,\n\t\t\tYES,\n\t\t\tYES,\n\t\t\tNO,\n\t\t\tNO,\n\t\t);\n\t\targumentStrings = (\n\t\t\t\"--log-fpkm\",\n\t\t\t\"--max-iterations 150\",\n\t\t\t\"-k 3\",\n\t\t\t\"--row-densities 2cond.densities\",\n\t\t\t2cond.fpkm_tracking,\n\t\t\t2cond.sorting_hat_out,\n\t\t\txxx.fpkm_tracking,\n\t\t\txxx.sorting_hat_out,\n\t\t);\n\t\tautoAttachOnCrash = 1;\n\t\tbreakpointsEnabled = 1;\n\t\tconfigStateDict = {\n\t\t\t\"PBXLSLaunchAction-0\" = {\n\t\t\t\tPBXLSLaunchAction = 0;\n\t\t\t\tPBXLSLaunchStartAction = 1;\n\t\t\t\tPBXLSLaunchStdioStyle = 2;\n\t\t\t\tPBXLSLaunchStyle = 0;\n\t\t\t\tclass = PBXLSRunLaunchConfig;\n\t\t\t\tcommandLineArgs = (\n\t\t\t\t);\n\t\t\t\tdisplayName = \"Executable Runner\";\n\t\t\t\tenvironment = {\n\t\t\t\t};\n\t\t\t\tidentifier = com.apple.Xcode.launch.runConfig;\n\t\t\t\tremoteHostInfo = \"\";\n\t\t\t\tstartActionInfo = \"\";\n\t\t\t};\n\t\t};\n\t\tcustomDataFormattersEnabled = 1;\n\t\tdataTipCustomDataFormattersEnabled = 1;\n\t\tdataTipShowTypeColumn = 1;\n\t\tdataTipSortType = 0;\n\t\tdebuggerPlugin = GDBDebugging;\n\t\tdisassemblyDisplayState = 0;\n\t\tdylibVariantSuffix = \"\";\n\t\tenableDebugStr = 1;\n\t\tenvironmentEntries = (\n\t\t);\n\t\texecutableSystemSymbolLevel = 0;\n\t\texecutableUserSymbolLevel = 0;\n\t\tlibgmallocEnabled = 0;\n\t\tname = cuffcluster;\n\t\tsavedGlobals = {\n\t\t};\n\t\tshowTypeColumn = 0;\n\t\tsourceDirectories = (\n\t\t);\n\t\tstartupPath = \"/Users/cole/projects/Clone Wars/linc_db2_expression\";\n\t\tvariableFormatDictionary = {\n\t\t};\n\t};\n\tEDFE39FA122BE7B700F24CD3 /* jensen_shannon.cpp */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {728, 2288}}\";\n\t\t\tsepNavSelRange = \"{2132, 0}\";\n\t\t\tsepNavVisRange = \"{1806, 955}\";\n\t\t};\n\t};\n\tEDFE39FD122BE7CB00F24CD3 /* jensen_shannon.h */ = {\n\t\tuiCtxt = {\n\t\t\tsepNavIntBoundsRect = \"{{0, 0}, {845, 606}}\";\n\t\t\tsepNavSelRange = \"{383, 0}\";\n\t\t\tsepNavVisRange = \"{0, 832}\";\n\t\t};\n\t};\n\tEDFE3A0C122BE93000F24CD3 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5152110C72963000A4F41 /* codons.cpp */;\n\t\tname = \"codons.cpp: 1\";\n\t\trLen = 0;\n\t\trLoc = 0;\n\t\trType = 0;\n\t\tvrLen = 3657;\n\t\tvrLoc = 0;\n\t};\n\tEDFE3A0D122BE93000F24CD3 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDA5151A10C72963000A4F41 /* tokenize.h */;\n\t\tname = \"tokenize.h: 17\";\n\t\trLen = 0;\n\t\trLoc = 380;\n\t\trType = 0;\n\t\tvrLen = 417;\n\t\tvrLoc = 0;\n\t};\n\tEDFE3A5B122C04D700F24CD3 /* PBXTextBookmark */ = {\n\t\tisa = PBXTextBookmark;\n\t\tfRef = EDFE39FD122BE7CB00F24CD3 /* jensen_shannon.h */;\n\t\tname = \"jensen_shannon.h: 18\";\n\t\trLen = 0;\n\t\trLoc = 383;\n\t\trType = 0;\n\t\tvrLen = 832;\n\t\tvrLoc = 0;\n\t};\n}\n" }, { "path": "cufflinks.xcodeproj/cole.perspectivev3", "content": "\n\n\n\n\tActivePerspectiveName\n\tProject\n\tAllowedModules\n\t\n\t\t\n\t\t\tBundleLoadPath\n\t\t\t\n\t\t\tMaxInstances\n\t\t\tn\n\t\t\tModule\n\t\t\tPBXSmartGroupTreeModule\n\t\t\tName\n\t\t\tGroups and Files Outline View\n\t\t\n\t\t\n\t\t\tBundleLoadPath\n\t\t\t\n\t\t\tMaxInstances\n\t\t\tn\n\t\t\tModule\n\t\t\tPBXNavigatorGroup\n\t\t\tName\n\t\t\tEditor\n\t\t\n\t\t\n\t\t\tBundleLoadPath\n\t\t\t\n\t\t\tMaxInstances\n\t\t\tn\n\t\t\tModule\n\t\t\tXCTaskListModule\n\t\t\tName\n\t\t\tTask List\n\t\t\n\t\t\n\t\t\tBundleLoadPath\n\t\t\t\n\t\t\tMaxInstances\n\t\t\tn\n\t\t\tModule\n\t\t\tXCDetailModule\n\t\t\tName\n\t\t\tFile and Smart Group Detail 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libbam.a in Frameworks */,\n\t\t\t\tF4D2AB8118D1124600BB1CF0 /* libboost_serialization-mt.a in Frameworks */,\n\t\t\t\tF4D2AB8318D1124600BB1CF0 /* libz.dylib in Frameworks */,\n\t\t\t\tF4D2AB8418D1124600BB1CF0 /* libboost_thread-mt.a in Frameworks */,\n\t\t\t\tF4D2AB8518D1124600BB1CF0 /* libboost_system-mt.a in Frameworks */,\n\t\t\t);\n\t\t\trunOnlyForDeploymentPostprocessing = 0;\n\t\t};\n/* End PBXFrameworksBuildPhase section */\n\n/* Begin PBXGroup section */\n\t\t08FB7794FE84155DC02AAC07 /* cufflinks_xcode */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tA2047A17191141C7007193FC /* libbam.a */,\n\t\t\t\tF4D2AB1218D108EC00BB1CF0 /* libboost_serialization-mt.a */,\n\t\t\t\tF4D2AB1318D108EC00BB1CF0 /* libboost_system-mt.a */,\n\t\t\t\tF4D2AB1418D108EC00BB1CF0 /* libboost_thread-mt.a */,\n\t\t\t\tEDD6AA441159BE6500D3F668 /* tests */,\n\t\t\t\t0A25E74A1242E9270080BEA9 /* src */,\n\t\t\t\tC6859E8C029090F304C91782 /* Documentation */,\n\t\t\t\tF4D43F2718D0BA9900AEB7BA /* cuffnorm */,\n\t\t\t\t1AB674ADFE9D54B511CA2CBB /* Products */,\n\t\t\t\tEDEBCEBB112E316400E2479E /* Accelerate.framework */,\n\t\t\t\tED1C0D7911D7F94900CFD663 /* libz.dylib */,\n\t\t\t);\n\t\t\tname = cufflinks_xcode;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\t0A25E74A1242E9270080BEA9 /* src */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tEDA5151E10C72963000A4F41 /* abundances.cpp */,\n\t\t\t\tEDA5150910C72963000A4F41 /* abundances.h */,\n\t\t\t\tEDA5151F10C72963000A4F41 /* assemble.cpp */,\n\t\t\t\tEDA5150A10C72963000A4F41 /* assemble.h */,\n\t\t\t\tED1C0C6D11D7D9C000CFD663 /* Autotools */,\n\t\t\t\tEDBAC4BF11FF5AD3002B7162 /* biascorrection.cpp */,\n\t\t\t\tEDBAC4C011FF5AD3002B7162 /* biascorrection.h */,\n\t\t\t\tEDA5152010C72963000A4F41 /* bundles.cpp */,\n\t\t\t\tEDA5150B10C72963000A4F41 /* bundles.h */,\n\t\t\t\tEDACE439114F031B009024DB /* clustering.cpp */,\n\t\t\t\tEDACE438114F031B009024DB /* clustering.h */,\n\t\t\t\tEDA5152110C72963000A4F41 /* codons.cpp */,\n\t\t\t\tEDA5150C10C72963000A4F41 /* codons.h */,\n\t\t\t\tEDA5152210C72963000A4F41 /* common.cpp */,\n\t\t\t\tEDA5150D10C72963000A4F41 /* common.h */,\n\t\t\t\t9CD6E8D913379B0900CCE1DE /* compress_gtf.cpp */,\n\t\t\t\tEDDBF70912D29DF000130D8A /* cuffcluster.cpp */,\n\t\t\t\tEDA5152310C72963000A4F41 /* cuffcompare.cpp */,\n\t\t\t\tEDA5152410C72963000A4F41 /* cuffdiff.cpp */,\n\t\t\t\tEDA5152510C72963000A4F41 /* cufflinks.cpp */,\n\t\t\t\tF4D43F2118D0BA3600AEB7BA /* cuffnorm.cpp */,\n\t\t\t\t9CE680F6149697B500855EDB /* cuffmerge */,\n\t\t\t\tF4A9F36D17428648000087E1 /* cuffquant.cpp */,\n\t\t\t\tEDACE431114EFF5C009024DB /* differential.cpp */,\n\t\t\t\tEDACE430114EFF5C009024DB /* differential.h */,\n\t\t\t\tEDA5152610C72963000A4F41 /* filters.cpp */,\n\t\t\t\tEDA5150E10C72963000A4F41 /* filters.h */,\n\t\t\t\tEDA5152710C72963000A4F41 /* GArgs.cpp */,\n\t\t\t\tEDA5150F10C72963000A4F41 /* GArgs.h */,\n\t\t\t\tEDA5152810C72963000A4F41 /* GBase.cpp */,\n\t\t\t\tEDA5151010C72963000A4F41 /* GBase.h */,\n\t\t\t\tEDA5152910C72963000A4F41 /* gdna.cpp */,\n\t\t\t\tEDA5151110C72963000A4F41 /* gdna.h */,\n\t\t\t\tEDA5152A10C72963000A4F41 /* genes.cpp */,\n\t\t\t\tEDA5151210C72963000A4F41 /* genes.h */,\n\t\t\t\tEDA5152B10C72963000A4F41 /* GFaSeqGet.cpp */,\n\t\t\t\tEDA5151310C72963000A4F41 /* GFaSeqGet.h */,\n\t\t\t\tED877B72123BCA3F0025A050 /* GFastaIndex.cpp */,\n\t\t\t\t0A5D9A3F123B5BFE002BEFD5 /* GFastaIndex.h */,\n\t\t\t\tEDA5152C10C72963000A4F41 /* gff.cpp */,\n\t\t\t\tEDA5151510C72963000A4F41 /* gff.h */,\n\t\t\t\tEDA5151C10C72963000A4F41 /* GHash.hh */,\n\t\t\t\tEDA5151D10C72963000A4F41 /* GList.hh */,\n\t\t\t\tEDE78A2C11B5F4F0000F8846 /* graph_optimize.cpp */,\n\t\t\t\tEDE78A2B11B5F4F0000F8846 /* graph_optimize.h */,\n\t\t\t\tEDA5152D10C72963000A4F41 /* GStr.cpp */,\n\t\t\t\tEDA5151610C72963000A4F41 /* GStr.h */,\n\t\t\t\tEDA5152E10C72963000A4F41 /* gtf_reads.cpp */,\n\t\t\t\tEDD94B711205CAF400AA1086 /* gtf_to_sam.cpp */,\n\t\t\t\tEDA5152F10C72963000A4F41 /* gtf_tracking.cpp */,\n\t\t\t\tEDA5151710C72963000A4F41 /* gtf_tracking.h */,\n\t\t\t\tEDA5153010C72963000A4F41 /* hits.cpp */,\n\t\t\t\tEDA5151810C72963000A4F41 /* hits.h */,\n\t\t\t\tEDFE39FA122BE7B700F24CD3 /* jensen_shannon.cpp */,\n\t\t\t\tEDFE39FD122BE7CB00F24CD3 /* jensen_shannon.h */,\n\t\t\t\t9CC50B5813204F180031949A /* locfit */,\n\t\t\t\tEDE78BC411B716C3000F8846 /* matching_merge.cpp */,\n\t\t\t\tEDE78BC511B716C3000F8846 /* matching_merge.h */,\n\t\t\t\t0A6183711324308800EFF9AF /* multireads.cpp */,\n\t\t\t\t0A4C0AD412F734DE0055A9D8 /* multireads.h */,\n\t\t\t\tF4DD461F1625F564008994EC /* negative_binomial_distribution.h */,\n\t\t\t\t0A25E74B1242E95A0080BEA9 /* progressbar.h */,\n\t\t\t\t0A5D9A35123B5BE0002BEFD5 /* GFastaIndex.cpp */,\n\t\t\t\tED677C0D132A3D83000CFDC8 /* replicates.cpp */,\n\t\t\t\tED677C0E132A3D83000CFDC8 /* replicates.h */,\n\t\t\t\tEDE26F1E15115CCB00A5199D /* rounding.h */,\n\t\t\t\t9CE680F8149FBEBD00855EDB /* sampling.cpp */,\n\t\t\t\t9CE680F7149FBE2F00855EDB /* sampling.h */,\n\t\t\t\tEDE78A7911B6E912000F8846 /* scaffold_graph.cpp */,\n\t\t\t\tEDE78A7811B6E912000F8846 /* scaffold_graph.h */,\n\t\t\t\tEDA5153110C72963000A4F41 /* scaffolds.cpp */,\n\t\t\t\tEDA5151910C72963000A4F41 /* scaffolds.h */,\n\t\t\t\tEDA5153210C72963000A4F41 /* tokenize.cpp */,\n\t\t\t\tEDA5151A10C72963000A4F41 /* tokenize.h */,\n\t\t\t\tF4A9F3C81742C26D000087E1 /* tracking.cpp */,\n\t\t\t\tF4A9F3C71742B994000087E1 /* tracking.h */,\n\t\t\t\tEDA5151B10C72963000A4F41 /* transitive_closure.h */,\n\t\t\t\tEDBBE9C811AA3F5E006AC14A /* transitive_reduction.h */,\n\t\t\t\t0ACD78C612E6647B00310E07 /* update_check.h */,\n\t\t\t\tED4835D81249057500642337 /* progressbar.h */,\n\t\t\t\t0A5D9A3F123B5BFE002BEFD5 /* GFastaIndex.h */,\n\t\t\t\t9C40F31B13C78FEF00515339 /* cuffmerge */,\n\t\t\t);\n\t\t\tname = src;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\t1AB674ADFE9D54B511CA2CBB /* Products */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tEDD0CD9C10C72AC200AF308F /* cufflinks */,\n\t\t\t\tEDD0CE2210C72F7D00AF308F /* cuffdiff */,\n\t\t\t\tEDD6A9D511598ABA00D3F668 /* cufflinks_xcode */,\n\t\t\t\tEDD94B671205C9E800AA1086 /* cufflinks_xcode */,\n\t\t\t\tEDFE39CE122BE65600F24CD3 /* cuffcluster */,\n\t\t\t\tED41B838127602C60000B5A2 /* cufflinks_xcode */,\n\t\t\t\t9CD6E93F13379B7E00CCE1DE /* compress_gtf */,\n\t\t\t\tF4A9F3C51742865A000087E1 /* cuffquant */,\n\t\t\t\tF4D2AB8A18D1124600BB1CF0 /* cuffdiff copy */,\n\t\t\t);\n\t\t\tname = Products;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\t9CC50B5813204F180031949A /* locfit */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tED920F3613213B19008DDA0B /* dens_haz.c */,\n\t\t\t\tED920F3713213B19008DDA0B /* dens_int.c */,\n\t\t\t\tED920F3813213B19008DDA0B /* dens_odi.c */,\n\t\t\t\tED920F3313213903008DDA0B /* vari.cpp */,\n\t\t\t\tED920F30132137F4008DDA0B /* density.c */,\n\t\t\t\t9CC50B5D13204FE30031949A /* minmax.c */,\n\t\t\t\t9CC50B5E13204FE30031949A /* locfit.c */,\n\t\t\t\t9CC50B5F13204FE30031949A /* ev_interp.c */,\n\t\t\t\t9CC50B6013204FE30031949A /* ev_atree.c */,\n\t\t\t\t9CC50B6113204FE30031949A /* weight.c */,\n\t\t\t\t9CC50B6213204FE40031949A /* wdiag.c */,\n\t\t\t\tED920F4013213C15008DDA0B /* adap.c */,\n\t\t\t\t9CC50B6313204FE40031949A /* strings.c */,\n\t\t\t\tED920F4F132150D9008DDA0B /* pout.c */,\n\t\t\t\tED920F4313213C49008DDA0B /* dist.c */,\n\t\t\t\t9CC50B6413204FE40031949A /* startlf.c */,\n\t\t\t\tED920F4C13213D03008DDA0B /* band.c */,\n\t\t\t\tED920F4913213CDE008DDA0B /* lfd.c */,\n\t\t\t\tED920F5B132158CA008DDA0B /* readfile.c */,\n\t\t\t\tED920F521321520A008DDA0B /* makecmd.c */,\n\t\t\t\tED920F4613213CAE008DDA0B /* random.c */,\n\t\t\t\tED920F5513215828008DDA0B /* help.c */,\n\t\t\t\tED920F581321587C008DDA0B /* c_plot.c */,\n\t\t\t\t9CC50B6513204FE40031949A /* solve.c */,\n\t\t\t\t9CC50B6613204FE40031949A /* simul.c */,\n\t\t\t\t9CC50B6713204FE40031949A /* preplot.c */,\n\t\t\t\t9CC50B6813204FE40031949A /* pcomp.c */,\n\t\t\t\t9CC50B6913204FE40031949A /* nbhd.c */,\n\t\t\t\t9CC50B6A13204FE40031949A /* math.c */,\n\t\t\t\t9CC50B6B13204FE40031949A /* m_max.c */,\n\t\t\t\t9CC50B6C13204FE40031949A /* m_eigen.c */,\n\t\t\t\t9CC50B6D13204FE40031949A /* linalg.c */,\n\t\t\t\t9CC50B6E13204FE40031949A /* lfstr.c */,\n\t\t\t\t9CC50B6F13204FE40031949A /* lf_vari.c */,\n\t\t\t\t9CC50B7013204FE50031949A /* lf_fitfun.c */,\n\t\t\t\t9CC50B7113204FE50031949A /* lf_dercor.c */,\n\t\t\t\t9CC50B7213204FE50031949A /* frend.c */,\n\t\t\t\t9CC50B7313204FE50031949A /* fitted.c */,\n\t\t\t\t9CC50B7413204FE50031949A /* family.c */,\n\t\t\t\t9CC50B7513204FE50031949A /* ev_trian.c */,\n\t\t\t\t9CC50B7613204FE50031949A /* ev_main.c */,\n\t\t\t\t9CC50B7713204FE50031949A /* ev_kdtre.c */,\n\t\t\t\t9CC50B7813204FE60031949A /* scb.c */,\n\t\t\t\t9CC50B7A13204FE60031949A /* scb_cons.c */,\n\t\t\t\t9CC50B7B13204FE60031949A /* local.h */,\n\t\t\t\t9CC50B7D13204FE60031949A /* m_jacob.c */,\n\t\t\t\t9CC50B7F13204FE70031949A /* design.h */,\n\t\t\t\t9CC50B8013204FE70031949A /* lffuns.h */,\n\t\t\t\t9CC50B8113204FE70031949A /* lfstruc.h */,\n\t\t\t\t9CC50B8213204FE70031949A /* mutil.h */,\n\t\t\t\t9CC50B8313204FE80031949A /* lf_robust.c */,\n\t\t\t\t9CC50B8413204FE80031949A /* m_chol.c */,\n\t\t\t\t9CC50B8513204FE80031949A /* lfcons.h */,\n\t\t\t\t9CC50B8913204FE90031949A /* arith.c */,\n\t\t\t\t9CC50B8A13204FE90031949A /* c_args.c */,\n\t\t\t\t9CC50B8B13204FE90031949A /* ar_funs.c */,\n\t\t\t\t9CC50B8E13204FEA0031949A /* lfwin.h */,\n\t\t\t\t9CC50B8F13204FEA0031949A /* imatlb.h */,\n\t\t\t\t9CC50B5913204F180031949A /* cmd.c */,\n\t\t\t\t9C29C3E31339285000EC6E65 /* vari.hpp */,\n\t\t\t);\n\t\t\tname = locfit;\n\t\t\tpath = src/locfit;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\tC6859E8C029090F304C91782 /* Documentation */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t);\n\t\t\tname = Documentation;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\tED1C0C6D11D7D9C000CFD663 /* Autotools */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tEDDC08C015064AA600CFC7EF /* ax_check_eigen.m4 */,\n\t\t\t\tED1C0CAA11D7E52400CFD663 /* ax_check_zlib.m4 */,\n\t\t\t\tED1C0C7111D7D9E500CFD663 /* ax_boost_base.m4 */,\n\t\t\t\tED1C0C7211D7D9E500CFD663 /* ax_boost_thread.m4 */,\n\t\t\t\tF4A9F3CB1742D011000087E1 /* ax_boost_serialization.m4 */,\n\t\t\t\tF4E2296816CD261C00D355C5 /* ax_boost_system.m4 */,\n\t\t\t\tEDEBCDF8112DFD7600E2479E /* configure.ac */,\n\t\t\t\tEDD6A7A0115562EE00D3F668 /* Makefile.am */,\n\t\t\t\tED1C0C7411D7DA3200CFD663 /* ax_bam.m4 */,\n\t\t\t);\n\t\t\tname = Autotools;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\tEDD6AA441159BE6500D3F668 /* tests */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tEDD6AA451159BE6500D3F668 /* unit_tests */,\n\t\t\t);\n\t\t\tpath = tests;\n\t\t\tsourceTree = \"\";\n\t\t};\n\t\tEDD6AA451159BE6500D3F668 /* unit_tests */ = {\n\t\t\tisa = PBXGroup;\n\t\t\tchildren = (\n\t\t\t\tEDD6AA461159BE6500D3F668 /* test_abundances.cpp 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NO;\n\t\t\t\tGCC_UNROLL_LOOPS = YES;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t./src,\n\t\t\t\t\t\"/usr/local/include/boost-1_41\",\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"\\\\\\\\\\\\\\\"$(SRCROOT)\\\\\\\\\\\\\\\"\",\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t);\n\t\t\t\tONLY_ACTIVE_ARCH = YES;\n\t\t\t\tPRODUCT_NAME = cufflinks;\n\t\t\t};\n\t\t\tname = Release;\n\t\t};\n\t\tEDD0CE2010C72F7D00AF308F /* Debug */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tCOPY_PHASE_STRIP = NO;\n\t\t\t\tGCC_DYNAMIC_NO_PIC = NO;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 0;\n\t\t\t\tGCC_PREPROCESSOR_DEFINITIONS = (\n\t\t\t\t\tHAVE_BAM,\n\t\t\t\t\tDEBUG,\n\t\t\t\t);\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_VERSION = \"\";\n\t\t\t\tGCC_WARN_ABOUT_MISSING_NEWLINE = YES;\n\t\t\t\tGCC_WARN_UNINITIALIZED_AUTOS = NO;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t/opt/local/include,\n\t\t\t\t\t/usr/local/include,\n\t\t\t\t\t./src,\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"$(SRCROOT)\",\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/libIntel64,\n\t\t\t\t);\n\t\t\t\tOTHER_CPLUSPLUSFLAGS = (\n\t\t\t\t\t\"$(OTHER_CFLAGS)\",\n\t\t\t\t\t\"-ftemplate-depth=1024\",\n\t\t\t\t);\n\t\t\t\tPRODUCT_NAME = cuffdiff;\n\t\t\t\tRUN_CLANG_STATIC_ANALYZER = NO;\n\t\t\t\tSTANDARD_C_PLUS_PLUS_LIBRARY_TYPE = dynamic;\n\t\t\t\tUSER_HEADER_SEARCH_PATHS = \"\";\n\t\t\t};\n\t\t\tname = Debug;\n\t\t};\n\t\tEDD0CE2110C72F7D00AF308F /* Release */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tDEBUG_INFORMATION_FORMAT = \"dwarf-with-dsym\";\n\t\t\t\tGCC_AUTO_VECTORIZATION = YES;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 3;\n\t\t\t\tGCC_PREPROCESSOR_DEFINITIONS = HAVE_VECLIB;\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_UNROLL_LOOPS = YES;\n\t\t\t\tGCC_VERSION = \"\";\n\t\t\t\tGCC_WARN_ABOUT_MISSING_NEWLINE = YES;\n\t\t\t\tGCC_WARN_UNINITIALIZED_AUTOS = YES;\n\t\t\t\tGENERATE_PROFILING_CODE = YES;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t/opt/local/include,\n\t\t\t\t\t/usr/local/include,\n\t\t\t\t\t./src,\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"$(SRCROOT)\",\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/libIntel64,\n\t\t\t\t);\n\t\t\t\tONLY_ACTIVE_ARCH = YES;\n\t\t\t\tOTHER_CPLUSPLUSFLAGS = (\n\t\t\t\t\t\"$(OTHER_CFLAGS)\",\n\t\t\t\t\t\"-ftemplate-depth=1024\",\n\t\t\t\t);\n\t\t\t\tPRODUCT_NAME = cuffdiff;\n\t\t\t\tRUN_CLANG_STATIC_ANALYZER = NO;\n\t\t\t};\n\t\t\tname = Release;\n\t\t};\n\t\tEDD6A9D311598AB900D3F668 /* Debug */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tCOPY_PHASE_STRIP = NO;\n\t\t\t\tGCC_AUTO_VECTORIZATION = NO;\n\t\t\t\tGCC_DYNAMIC_NO_PIC = NO;\n\t\t\t\tGCC_ENABLE_SSE3_EXTENSIONS = NO;\n\t\t\t\tGCC_ENABLE_SSE41_EXTENSIONS = NO;\n\t\t\t\tGCC_ENABLE_SSE42_EXTENSIONS = NO;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 0;\n\t\t\t\tGCC_PREPROCESSOR_DEFINITIONS = DEBUG;\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tGCC_WARN_ABOUT_MISSING_FIELD_INITIALIZERS = NO;\n\t\t\t\tGCC_WARN_ABOUT_MISSING_NEWLINE = YES;\n\t\t\t\tGCC_WARN_EFFECTIVE_CPLUSPLUS_VIOLATIONS = NO;\n\t\t\t\tGCC_WARN_NON_VIRTUAL_DESTRUCTOR = NO;\n\t\t\t\tGCC_WARN_SHADOW = NO;\n\t\t\t\tGCC_WARN_UNINITIALIZED_AUTOS = YES;\n\t\t\t\tGCC_WARN_UNUSED_FUNCTION = NO;\n\t\t\t\tGCC_WARN_UNUSED_PARAMETER = NO;\n\t\t\t\tGCC_WARN_UNUSED_VALUE = YES;\n\t\t\t\tGENERATE_PROFILING_CODE = NO;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t/usr/local/include,\n\t\t\t\t\t./src,\n\t\t\t\t\t\"/usr/local/include/boost-1_41\",\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"\\\\\\\\\\\\\\\"$(SRCROOT)\\\\\\\\\\\\\\\"\",\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t);\n\t\t\t\tPRODUCT_NAME = cufflinks_xcode;\n\t\t\t\tSTANDARD_C_PLUS_PLUS_LIBRARY_TYPE = dynamic;\n\t\t\t};\n\t\t\tname = Debug;\n\t\t};\n\t\tEDD6A9D411598AB900D3F668 /* Release */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tCOPY_PHASE_STRIP = NO;\n\t\t\t\tDEBUG_INFORMATION_FORMAT = \"dwarf-with-dsym\";\n\t\t\t\tGCC_DEBUGGING_SYMBOLS = used;\n\t\t\t\tGCC_ENABLE_SSE3_EXTENSIONS = YES;\n\t\t\t\tGCC_ENABLE_SSE41_EXTENSIONS = YES;\n\t\t\t\tGCC_ENABLE_SSE42_EXTENSIONS = YES;\n\t\t\t\tGCC_ENABLE_SUPPLEMENTAL_SSE3_INSTRUCTIONS = YES;\n\t\t\t\tGCC_ENABLE_TRIGRAPHS = NO;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 3;\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_UNROLL_LOOPS = YES;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t./src,\n\t\t\t\t\t\"/usr/local/include/boost-1_41\",\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"\\\\\\\\\\\\\\\"$(SRCROOT)\\\\\\\\\\\\\\\"\",\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t);\n\t\t\t\tONLY_ACTIVE_ARCH = YES;\n\t\t\t\tPRODUCT_NAME = cufflinks;\n\t\t\t};\n\t\t\tname = Release;\n\t\t};\n\t\tEDD94B651205C9E800AA1086 /* Debug */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tCOPY_PHASE_STRIP = NO;\n\t\t\t\tGCC_AUTO_VECTORIZATION = NO;\n\t\t\t\tGCC_DYNAMIC_NO_PIC = NO;\n\t\t\t\tGCC_ENABLE_SSE3_EXTENSIONS = NO;\n\t\t\t\tGCC_ENABLE_SSE41_EXTENSIONS = NO;\n\t\t\t\tGCC_ENABLE_SSE42_EXTENSIONS = NO;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 0;\n\t\t\t\tGCC_PREPROCESSOR_DEFINITIONS = (\n\t\t\t\t\tHAVE_BAM,\n\t\t\t\t\tDEBUG,\n\t\t\t\t);\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tGCC_WARN_ABOUT_MISSING_FIELD_INITIALIZERS = NO;\n\t\t\t\tGCC_WARN_ABOUT_MISSING_NEWLINE = YES;\n\t\t\t\tGCC_WARN_EFFECTIVE_CPLUSPLUS_VIOLATIONS = NO;\n\t\t\t\tGCC_WARN_NON_VIRTUAL_DESTRUCTOR = NO;\n\t\t\t\tGCC_WARN_SHADOW = NO;\n\t\t\t\tGCC_WARN_UNINITIALIZED_AUTOS = NO;\n\t\t\t\tGCC_WARN_UNUSED_FUNCTION = NO;\n\t\t\t\tGCC_WARN_UNUSED_PARAMETER = NO;\n\t\t\t\tGCC_WARN_UNUSED_VALUE = YES;\n\t\t\t\tGENERATE_PROFILING_CODE = NO;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t/usr/local/include,\n\t\t\t\t\t./src,\n\t\t\t\t\t\"/usr/local/include/boost-1_41\",\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"\\\\\\\"$(SRCROOT)\\\\\\\"\",\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t);\n\t\t\t\tPRODUCT_NAME = cufflinks_xcode;\n\t\t\t\tSTANDARD_C_PLUS_PLUS_LIBRARY_TYPE = dynamic;\n\t\t\t};\n\t\t\tname = Debug;\n\t\t};\n\t\tEDD94B661205C9E800AA1086 /* Release */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tCOPY_PHASE_STRIP = NO;\n\t\t\t\tDEBUG_INFORMATION_FORMAT = \"dwarf-with-dsym\";\n\t\t\t\tGCC_DEBUGGING_SYMBOLS = used;\n\t\t\t\tGCC_ENABLE_SSE3_EXTENSIONS = YES;\n\t\t\t\tGCC_ENABLE_SSE41_EXTENSIONS = YES;\n\t\t\t\tGCC_ENABLE_SSE42_EXTENSIONS = YES;\n\t\t\t\tGCC_ENABLE_SUPPLEMENTAL_SSE3_INSTRUCTIONS = YES;\n\t\t\t\tGCC_ENABLE_TRIGRAPHS = NO;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 3;\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_UNROLL_LOOPS = YES;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t./src,\n\t\t\t\t\t\"/usr/local/include/boost-1_41\",\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"\\\\\\\"$(SRCROOT)\\\\\\\"\",\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t);\n\t\t\t\tONLY_ACTIVE_ARCH = YES;\n\t\t\t\tPRODUCT_NAME = cufflinks;\n\t\t\t};\n\t\t\tname = Release;\n\t\t};\n\t\tEDFE39CC122BE65600F24CD3 /* Debug */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tCOPY_PHASE_STRIP = NO;\n\t\t\t\tGCC_DYNAMIC_NO_PIC = NO;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 0;\n\t\t\t\tGCC_PREPROCESSOR_DEFINITIONS = (\n\t\t\t\t\tHAVE_BAM,\n\t\t\t\t\tDEBUG,\n\t\t\t\t);\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tHEADER_SEARCH_PATHS = (\n\t\t\t\t\t/usr/local/include/,\n\t\t\t\t\t./src,\n\t\t\t\t\t\"/usr/local/include/boost-1_41\",\n\t\t\t\t);\n\t\t\t\tINSTALL_PATH = /usr/local/bin;\n\t\t\t\tLIBRARY_SEARCH_PATHS = (\n\t\t\t\t\t\"$(inherited)\",\n\t\t\t\t\t/usr/local/lib,\n\t\t\t\t\t\"\\\\\\\"$(SRCROOT)\\\\\\\"\",\n\t\t\t\t\t/usr/local/libIntel64,\n\t\t\t\t\t/opt/local/lib,\n\t\t\t\t);\n\t\t\t\tPRODUCT_NAME = cuffcluster;\n\t\t\t\tSTANDARD_C_PLUS_PLUS_LIBRARY_TYPE = dynamic;\n\t\t\t};\n\t\t\tname = Debug;\n\t\t};\n\t\tEDFE39CD122BE65600F24CD3 /* Release */ = {\n\t\t\tisa = XCBuildConfiguration;\n\t\t\tbuildSettings = {\n\t\t\t\tALWAYS_SEARCH_USER_PATHS = NO;\n\t\t\t\tDEBUG_INFORMATION_FORMAT = \"dwarf-with-dsym\";\n\t\t\t\tGCC_AUTO_VECTORIZATION = YES;\n\t\t\t\tGCC_INLINES_ARE_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_MODEL_TUNING = G5;\n\t\t\t\tGCC_OPTIMIZATION_LEVEL = 3;\n\t\t\t\tGCC_PREPROCESSOR_DEFINITIONS = HAVE_VECLIB;\n\t\t\t\tGCC_SYMBOLS_PRIVATE_EXTERN = NO;\n\t\t\t\tGCC_UNROLL_LOOPS = YES;\n\t\t\t\tGCC_VERSION = com.apple.compilers.llvm.clang.1_0;\n\t\t\t\tHEADER_SEARCH_PATHS = 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"\\documentclass[12pt]{amsart}\r\n\r\n\\usepackage{graphicx}\r\n\\usepackage{amsmath}\r\n\\usepackage[pdftex]{hyperref}\r\n\r\n\\setlength{\\textwidth}{6.2in}\r\n\\setlength{\\textheight}{8.4in}\r\n\\setlength{\\topmargin}{0.2in}\r\n\\setlength{\\oddsidemargin}{0in}\r\n\\setlength{\\evensidemargin}{0in}\r\n\\setlength{\\headsep}{.3in}\r\n\\footskip 0.3in\r\n\\newtheorem{thm}{Theorem}\r\n\\newtheorem{lemma}[thm]{Lemma}\r\n\\newtheorem{prop}[thm]{Proposition}\r\n\\newtheorem{cor}[thm]{Corollary}\r\n\\theoremstyle{definition}\r\n\\newtheorem{defn}[thm]{Definition}\r\n\\newtheorem{example}[thm]{Example}\r\n\\newtheorem{remark}[thm]{Remark}\r\n\\newtheorem{conj}[thm]{Conjecture}\r\n\r\n\\newcommand{\\cherry}[1]{\\ensuremath{\\langle #1 \\rangle}}\r\n\\newcommand{\\ignore}[1]{}\r\n\\newcommand{\\half}{\\frac{1}{2}}\r\n\\newcommand{\\eopf}{\\framebox[6.5pt]{} \\vspace{0.2in}}\r\n\r\n%------------------document begins-----------------------\r\n\r\n\\begin{document}\r\n\r\n\\title{Supplementary methods for the paper\\\\\r\nTranscript assembly and quantification by RNA-Seq \r\nreveals unannotated transcripts and isoform switching \r\nduring cell differentiation}\r\n\\author{Cole Trapnell \\and Brian A Williams \\and Geo Pertea \\and Ali Mortazavi \\and Gordon Kwan \\and Marijke J van Baren \\and Steven L Salzberg\r\n\\and Barbara J Wold \\and Lior Pachter}\r\n%\\address{}\r\n%\\email{\\{whoweare\\}@math.berkeley.edu}\r\n%\\dedicatory{\\today}\r\n\r\n%\\thanks{Supported in part by ....}\r\n%\\subjclass{Primary 55M20; Secondary 47H10}\r\n\r\n\\maketitle\r\n\\markboth{C Trapnell et al.}{Transcript assembly and abundance estimation from RNA-Seq}\r\n\r\nThis document is a companion to the paper ``Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms''. It describes the experiment\r\nperformed, the details of the {\\tt Cufflinks} assembler and abundance\r\nestimation methods, and provides\r\nsupplementary tables, figures and supporting evidence for claims in\r\nthe paper.\r\n%\\pagestyle{plain}\r\n\\pagenumbering{roman} \\tableofcontents \\listoffigures \\listoftables \\newpage \\pagenumbering{arabic}\r\n\r\n\\section{Sequencing experiment}\r\n\r\nThe data analyzed in the paper consisted of 430,467,018 paired 75bp reads\r\nsequenced from the transcriptome of mouse skeletal muscle C2C12 cells induced\r\nto undergo myogenic differentiation. Total RNA was extracted from these cells,\r\nand subsequently mRNA was isolated at four different time points (-24 hours,\r\n60 hours, 120 hours, 168 hours). cDNA was prepared following a similar\r\nprocedure to the one described in \\cite{Mortazavi2008}. Fragmentation of the\r\nmRNA followed by size selection resulted in fragment lengths\r\napproximately 200nt long for\r\nall of the time-points. The distribution of fragment lengths is shown in\r\nSupplementary Figure \\ref{fragment_lengths} (in Section \\ref{sec:abundances}\r\nthis distribution of fragment lengths is referred to as $F$). These estimates\r\nare based on alignments of the spiked-in sequences using {\\tt Bowtie} 0.12\r\n\\cite{Langmead2009}.\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics{pdfs/library_frags.pdf}\r\n \\caption[Fragment length distributions of C2C12 time-course libraries]{Fragment length distributions of C2C12 time-course libraries. \\label{fragment_lengths}}\r\n\\end{figure}\r\n\r\n\\section{Mapping fragments to the genome}\r\n\r\nIn principle, an algorithm that infers individual transcript abundances by\r\nmeasuring the fraction of fragments originating from each of a set of known\r\ntranscripts would begin by computing alignments between fragments and the set\r\nof known transcripts that may be contained in the sample. However, because the\r\ntranscriptome for mouse is incompletely annotated, such an analysis requires\r\nmapping of fragments to the genome as a proxy for mapping directly to\r\ntranscripts. This means that alignments of short sequencing reads must be\r\nallowed to span exon-exon splice junctions in genomic coordinate space. We\r\npreviously developed a program called {\\tt TopHat} to map RNA-Seq reads to the\r\ngenome. {\\tt TopHat} does not require a reference transcriptome and can\r\ntherefore be used to discover novel splice junctions. \\cite{Trapnell2009}\r\n\r\nFragments were mapped to build 37.1 of the mouse genome with {\\tt TopHat} version\r\n1.0.13. We extended our previous algorithms described in\r\n\\cite{Trapnell2009} to exploit the longer\r\npaired reads used in the study. The original {\\tt TopHat} program used a\r\nseed-and-extend alignment strategy to find spliced alignments of unpaired\r\nRNA-Seq experiments. However, due to computational limitations, our original\r\nmethod reported only alignments across GT-AG introns shorter than 20Kb by\r\ndefault. This strategy also could not align reads that spanned multiple splice\r\njunctions. However, as sequencing technology has improved and longer (paired end) reads have \r\nbecome available, we have\r\nmodified the software to employ new strategies to align reads across splice\r\njunctions. {\\tt TopHat} version 1.0.7 and later splits a read 75bp or longer in\r\nthree or more segments of approximately equal size (25bp), and maps them\r\nindependently. Reads with segments that can be mapped to the genome only\r\nnon-contiguously are marked as possible intron-spanning reads. These\r\n``contiguously unmappable'' reads are used to build a set of possible introns\r\nin the transcriptome.\r\n\r\n\\subsection{Discovering splice junctions} Suppose $S$ is a read of length\r\n$l$ that crosses a splice junction. {\\tt TopHat} splits $S$ into $n=\\lfloor\r\nl/k \\rfloor$ segments where $k=25$bp ($k$ is a parameter that can be\r\nadjusted by the user in {\\tt TopHat}). At most\r\none of these segments must cross the splice junction, and junctions\r\ncan be discovered if they lie in any of the segments. {\\tt TopHat} maps the\r\nsegments $s_1,...,s_n$ with {\\tt Bowtie} to the genome, and checks for\r\ninternal segments $s_2,...,s_{n-1}$ that do not map anywhere to the genome, as\r\nwell as for pairs of successive segments $s_i,s_{i+1}$ that both align to the\r\ngenome, but not adjacently. When a segment $s_i$ fails to align because it\r\ncrosses a splice junction, but $s_{i-1}$ and $s_{i+1}$ are aligned (say at\r\nstarting at positions $x$ and $y$, respectively), {\\tt TopHat} looks for the\r\ndonor and acceptor sites for the junction near $x$ and $y$. Assuming\r\n(without loss of generality) that the\r\ntranscript is on Crick strand of the genome the\r\ndonor must fall within $k$ bases upstream of position $x+k$, and the acceptor\r\nmust be within $k$ bases downstream of $y$, a total of $k$ possible exon-exon\r\nsplice junctions. Similarly, when successive segments $s_i$ and $s_{i+1}$\r\nalign to the genome non-adjacently at positions $x$ and $y$, the junction\r\nspanned by the read must be from positions $x+k$ to $y$ in the genome.\r\n\r\n{\\tt TopHat} accumulates an index of potential splice junctions by examining\r\nsegment mapping for all contiguously unmappable reads. For each junction the\r\nprogram then concatenates $k$bp upstream of the donor to $k$bp downstream of\r\nthe acceptor to form a synthetic spliced sequence around the junction. The\r\nsegments of the contiguously unmappable reads are then aligned against these\r\nsynthetic sequences with {\\tt Bowtie}. The resulting contiguous and spliced\r\nsegment alignments for these reads are merged to form complete alignments to\r\nthe genome, each spanning one or more splice junctions.\r\n\r\n\\subsection{Resolving multiple alignments for fragments}\r\n\r\nThe alignments for both reads from a mate pair are examined together to\r\nproduce a set of alignments for the corresponding library fragment as a whole,\r\nreported in SAM format \\cite{Li2009a}. These fragment alignments are\r\nranked according to the procedure described below, and only highest ranking alignments are reported. The ranks are\r\ndesigned to incorporate very loose assumptions on intron and gene length,\r\nnamely that introns longer than 20kb are rare. Let $x$ and $y$ be fragment\r\nalignments. Then $x < y$ if {\\em any} of the following (applied in order) are\r\ntrue:\r\n\r\n\\begin {enumerate}\r\n \\item $x$ is a singleton, and $y$ has both ends mapped,\r\n \\item $x$ crosses more splice junctions than $y$,\r\n \\item the reads from $x$ map significantly farther apart in the genome than expected according to the library's fragment length distribution ($\\geq$ 3 s.d.), and the reads from $y$ do not,\r\n \\item the reads from $x$ are significantly closer together than expected according to the library's fragment length distribution, and the reads from $y$ are not,\r\n \\item The reads from $x$ map more than 100bp farther apart than the reads from $y$,\r\n \\item $x$ and $y$ both span an intron, and $x$ spans a longer one,\r\n \\item $x$ has more mismatches than $y$ to the genome.\r\n\\end {enumerate}\r\n\r\nFragments that have multiple equally good alignments according to the above\r\nrules are all reported. If there are $n$ alignments for a fragment,\r\neach is assigned a probability\r\nof only $1/n$ of being correct. The SAM format encodes this probability in the\r\nmapping quality field, which is later used by {\\tt Cufflinks} to reduce the\r\ncontribution of multiply mapping fragments (to $1/n$ of a uniquely\r\nmappable read) in FPKM calculations (FPKM is a measurement of\r\nexpression, and is formally defined in Section\r\n\\ref{sec:abundances}). The recent work of Li et al. \\cite{Li2009b}\r\naddresses the problem of probabilistically assigning multi-reads, and\r\nit should be possible to incorporate the ideas of that paper into\r\nfuture versions of {\\tt Tophat} and {\\tt Cufflinks}. \r\n\r\n\\begin{table}[h]{\r\n \\Small\r\n \\label{table:reads}\r\n \\begin{tabular}{l|c|c|c|c|c|c} \r\n \\hline\r\n \\textbf{Sample} & \\textbf{Sequenced} & \\textbf{Aligned} & \\textbf{Singleton} & \\textbf{Spliced} & \\textbf{Multi-mapping} & \\textbf{Total} \\\\\r\n & \\textbf{fragments} & \\textbf{fragments} & \\textbf{fragments} & \\textbf{fragments} & \\textbf{fragments} & \\textbf{alignments} \\\\\r\n \\hline\r\n\r\n -24 hours & 42,184,539 & 35,852,366 & 11,031,886 & 8,824,825 & 1,768,041 & 41,663,170 \\\\\r\n 60 hours & 70,192,031 & 57,071,494 & 18,104,211 & 15,778,114 & \t2,265,378 & 64,637,511 \\\\\r\n 120 hours & 41,069,106 & 27,914,989 & 14,431,734 & 7,711,026 & 1,881,772 & 33,929,133 \\\\\r\n 168 hours & 61,787,833 & 50,705,080 & 20,396,250 & 14,585,287 & \t2,458,292 &\t58,797,912 \\\\\r\n Total & 215,233,509 & 171,543,929 & 63,964,081 & 46,899,252 & 8,373,483 & 199,027,726 \\\\\r\n\r\n \\hline\r\n \r\n \\end{tabular}\r\n }\r\n\\vskip 0.2in\r\n\\caption[Number of fragments sequenced, aligned and mapped with {\\tt TopHat}]{Number of fragments sequenced, aligned and mapped with {\\tt\r\n TopHat}. Singleton fragments are fragments for which only one end\r\n could be mapped. Spliced fragments include at least one end that\r\n maps across a junction. The numbers in the total alignment column\r\n may not be the sum of the entries in each row, because some fragments\r\n fall into multiple classes.}\r\n\\end{table}\r\n\r\n\\newpage\r\n\\section{Transcript abundance estimation \\label{abundances}}\r\n\\label{sec:abundances}\r\n\r\n\\subsection{Definitions}\r\n\r\nA {\\em transcript} is an RNA molecule that has been transcribed from DNA. A\r\n{\\em primary transcript} is an RNA molecule that has yet to undergo\r\nmodification. The {\\em genomic location} of a primary transcript consists of a\r\npair of coordinates in the genome representing the $5'$ transcription start\r\nsite and the $3'$ polyadenylation cleavage site. We denote the set of all\r\ntranscripts in a transcriptome by $T$. We partition transcripts into {\\em\r\ntranscription loci} (for simplicity we refer to these as loci) so that every\r\nlocus contains a set of transcripts all of whose genomic locations do not\r\noverlap the genomic location of any transcript in any other locus. Formally,\r\nwe consider a maximal partition of transcripts into loci, a partition denoted\r\nby $G$, where the genomic location of a transcript $t \\in g \\in G$ does not\r\noverlap the genomic location of any transcript $u $ where $u \\in h \\in G$ and\r\n$h \\neq g$. We emphasize that the definition of a transcription locus is not\r\nbiological; transcripts in the same locus may be regulated via different\r\npromoters, and may differ completely in sequence (for example if one\r\ntranscript is in the intron of another) or have different functions. The\r\nreason for defining loci is that we will see that they are computationally\r\nconvenient.\r\n\r\n\r\nWe assume that at the time of an experiment, a transcriptome consists of an\r\nensemble of transcripts $T$ where the proportion of transcript $t \\in T$ is\r\n$\\rho_t$, so that $\\sum_{t \\in T} \\rho_t = 1$ and $0 \\leq \\rho_t \\leq 1$ for\r\nall $t \\in T$. Formally, a {\\em transcriptome} is a set of transcripts $T$\r\ntogether with the abundances $\\rho=\\{ \\rho_t\\}_{t \\in T}$. For convenience we also introduce\r\nnotation for the proportion of transcripts in each locus. We let $\\sigma_g =\r\n\\sum_{t \\in g} \\rho_t$. Similarly, within a locus $g$, we denote the\r\nproportion of each transcript $t \\in g$ by $\\tau_t = \\frac{\\rho_t}{\\sigma_g}$.\r\nWe refer to $\\rho,\\sigma$ and $\\tau$ as {\\em transcript abundances}.\r\n\r\n\r\nTranscripts have lengths, which we denote by $l(t)$. For a collection of transcripts $S \\subset T$ in a transcriptome, we define the length of $S$ using the weighted mean:\r\n\\begin{equation}\r\n\\label{eq:effective_length}\r\nl(S) =\\frac{\\sum_{t \\in S} \\rho_tl(t)}{\\sum_{t \\in S}\\rho_t}.\r\n\\end{equation}\r\nIt is important to note that the length of a set of transcripts depends on their relative abundances; the reason for this will be clear later. \r\n\r\nOne grouping of transcripts that we will focus on is the set of transcripts\r\nwithin a locus that share the same transcription start site (TSS). Unlike the\r\nconcept of a locus, grouping by TSS has a biological basis. Transcripts\r\nwithin such a group are by definition alternatively spliced, and if they have\r\ndifferent expression levels, this is most likely due to the spliceosome and\r\nnot due to differences in transcriptional regulation.\r\n\r\n\\subsection{A statistical model for RNA-Seq}\r\n\r\nIn order to analyze expression levels of transcripts with RNA-Seq data, it is\r\nnecessary to have a model for the (stochastic) process of sequencing. A {\\em\r\nsequencing experiment} consists of selecting a total of $M$ fragments of\r\ntranscripts uniformly at random from the transcriptome. Each fragment is\r\nidentified by sequencing from its ends, resulting in two reads called {\\em\r\nmate pairs}. The length of a fragment is a random variable, with a\r\ndistribution we will denote by $F$. That is, the probability that a fragment\r\nhas length $i$ is $F(i)$ and $\\sum_{i=1}^{\\infty} F(i) = 1$. In this paper we\r\nassume that $F$ is normal, however in principle $F$ can be estimated using\r\ndata from the experiment (e.g. spike-in sequences). We decided to use\r\nthe normal approximation to $F$ (allowing for user specified\r\nparameters of the normal distribution) in order to simplify the\r\nrequirements for running {\\tt Cufflinks} at this time. \r\n\r\nThe assumption of random\r\nfragment selection is known to oversimplify the complexities of a sequencing\r\nexperiment, however without rigorous ways to normalize we decided to work with\r\nthe uniform at random assumption. It is easy to adapt the model to include\r\nmore complex models that address sequencing bias as RNA-Seq experiments mature\r\nand the technologies are better understood.\r\n\r\nThe transcript abundance estimation problem in paired-end RNA-Seq is to\r\nestimate $\\rho$ given a set of transcripts $T$ and a set of reads sequenced\r\nfrom the ends of fragments. In {\\tt Cufflinks}, the transcripts $T$ can be\r\nspecified by the user, or alternatively $T$ can be estimated directly from the\r\nreads. The latter problem is the transcript assembly problem which we discuss\r\nin Section \\ref{sec:assembly}. We ran {\\tt Cufflinks} in the latter\r\n``discovery'' mode where we assembled the transcripts without using the\r\nreference annotation.\r\n\r\nThe fact that fragments have different lengths has bearing on the calculation\r\nof the probability of selecting a fragment from a transcript. Consider a\r\ntranscript $t$ with length $l(t)$. The probability of selecting a fragment of\r\nlength $k$ from $t$ at one of the positions in $t$ assuming that it is\r\nselected uniformly at random, is $\\frac{1}{l(t)-k}$. For this reason, we will\r\ndefine an adjusted length for transcripts as\r\n\r\n\\begin{equation}\r\n\\tilde{l}(t) = \\sum_{i=1}^{l(t)} F(i)(l(t)-i+1).\r\n\\end{equation}\r\nWe also revisit the definition of length for a group of transcripts, and define \r\n\\begin{equation}\r\n\\tilde{l}(S) =\\frac{\\sum_{t \\in S} \\rho_t\\tilde{l}(t)}{\\sum_{t \\in S}\\rho_t}.\r\n\\end{equation} \r\n\r\nIt is important to note that given a read it may not be obvious from which\r\ntranscript the fragment it was sequenced from originated. The consistency of\r\nfragments with transcripts is important and we define the {\\em\r\nfragment-transcript matrix} $A_{R,T}$ to be the $M \\times |T|$ matrix with\r\n$A(r,t)=1$ if the fragment alignment $r$ is completely contained in the\r\ngenomic interval spanned by $t$, and all the implied introns in $r$ match\r\nintrons in $t$ (in order), and with $A(r,t)=0$ otherwise. Note that the reads\r\nin Figure 1c in the main text are colored according to the matrix $A_{R,T}$,\r\nwith each column of the matrix corresponding to one of the three colors\r\n(yellow, blue, red) and reads colored according to the mixture of colors\r\ncorresponding to the transcripts their fragments are contained in.\r\n\r\nEven given the read\r\nalignment to a reference genome, it may not be obvious what the length of the\r\nfragment was. Formally, in the case that $A_{R,T}(r,t)=1$ we denote by $I_t(r)$ the fragment length from within\r\na transcript $t$ implied by the (presumably unique) sequences corresponding to\r\nthe mate pairs of a fragment $r$. If $A_{R,T}(r,t)=0$ then $I_t(r)$ is set to be\r\ninfinite and $F(I_t(r)) = 0$. \r\n\r\nGiven a set of reads, we assume that we can identify for each of them the set\r\nof transcripts with which the fragments the reads belonged to are consistent. The\r\nrationale for this assumption is the following: we map the reads to a\r\nreference genome, and we assume that the read lengths are sufficiently long so\r\nthat every mate-pair can be uniquely mapped to the genome. We refer to this\r\nmapping as the {\\em fragment alignment}. We also assume that we know all the\r\npossible transcripts and their alignments to the genome. Therefore, we can\r\nidentify for each read the possible transcripts from which the fragment it\r\nbelonged to originated.\r\n\r\n\\begin{figure}[!ht] \r\n \\includegraphics[scale=0.8]{pdfs/implied_length}\r\n \\caption[Implied length of a fragment alignment]{Alignments of reads to the genome (rectangles) may be consistent with multiple transcripts (in this case both $t_1$ and $t_2$). The transcripts $t_1$ and $t_2$ differ by an internal exon; introns are indicated by long dashed lines. If we denote the fragment alignment by $r$, this means that $A_{R,T}(r,t_1)=1$ and $A_{R,T}(r,t_2)=1$. It is apparent that the implied length $I_{t_1}(r) > I_{t_2}(r)$ due to the presence of the extra internal exon in $t_1$. }\r\n\\end{figure}\r\n\r\nWe are now ready to write down the likelihood equation for the model. We will\r\nwrite $L(\\rho|R)$ for the likelihood of a set of fragment alignments $R$\r\nconstructed from $M$ reads. The notation $Pr(trans. = t)$ means ``the\r\nprobability that a fragment selected at random originates from transcript $t$''.\r\n\r\n\\begin{eqnarray}\r\n& & L(\\rho|R) = \\prod_{r \\in R} Pr(rd.\\ aln. =r)\\\\\r\n& = & \\prod_{r \\in R} \\sum_{t \\in T} Pr(rd.\\ aln. =r|trans. =t)Pr(trans. =t)\\\\\r\n& = & \\prod_{r \\in R} \\sum_{t \\in T} \\frac{\\rho_t\\tilde{l}(t)}{\\sum_{u \\in T}\\rho_u\\tilde{l}(u)} Pr(rd.\\ aln. = r|trans. =t)\\\\ \r\n& = & \\prod_{r \\in R} \\sum_{t \\in T}\\frac{\\rho_t\\tilde{l}(t)}{\\sum_{u \\in T}\\rho_u\\tilde{l}(u)} \\left(\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right) \\\\ \\label{eq:likerho}\r\n& = & \\prod_{r \\in R} \\sum_{t \\in T} \\alpha_t\\left(\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right),\r\n\\end{eqnarray}\r\nwhere \r\n\\begin{equation}\r\n\\alpha_t = \\frac{\\rho_t\\tilde{l}(t)}{\\sum_{u \\in T}\\rho_u\\tilde{l}(u)}.\r\n\\end{equation}\r\n\r\nObserve that $\\alpha_t$ is exactly the probability that a fragment selected at\r\nrandom comes from transcript $t$, and we have that $\\sum_{t \\in T}\\alpha_t =\r\n1$. In light of the probabilistic meaning of the $\\alpha=\\{\\alpha_t\\}_{t \\in T}$, we refer to them as\r\n{\\em fragment abundances}.\r\n\r\nIt is evident that the likelihood function is that of a linear model\r\nand that the likelihood function is\r\nconcave (Proposition \\ref{prop:linearmodel}) so a numerical method can be used\r\nto find the $\\alpha$. It is then possible, in principle, to recover the $\\rho$\r\nusing Lemma \\ref{lemma:readstoprobs}. However the number of parameters is in the tens of\r\nthousands, and in practice this form of the likelihood function is unwieldy.\r\nInstead, we re-write the likelihood utilizing the fact that transcripts in\r\ndistinct loci do not overlap in genomic location.\r\n\r\nWe first calculate the probability that a fragment originates from a transcript within a given locus $g$:\r\n\\begin{eqnarray}\r\n\\beta_g & := & \\sum_{t \\in g} \\alpha_t\\\\\r\n& = & \\frac{\\sum_{t \\in g} \\rho_t \\tilde{l}(t)}{\\sum_{u \\in T} \\rho_u \\tilde{l}(u)}\\\\\r\n& = & \\frac{\\sum_{t \\in g} \\sigma_g\\tau_t \\tilde{l}(t)}{\\sum_{h \\in G} \\sum_{u \\in h} \\sigma_h\\tau_u \\tilde{l}(u)}\\\\\r\n& = & \\frac{\\sigma_g\\sum_{t \\in g} \\tau_t \\tilde{l}(t)}{\\sum_{h \\in G} \\sigma_h \\sum_{u \\in h} \\tau_u \\tilde{l}(u)}\\\\\r\n & = & \\frac{\\sigma_g \\tilde{l}(g)}{\\sum_{h \\in G} \\sigma_h \\tilde{l}(h)}. \r\n\\end{eqnarray}\r\nRecall that $\\sigma_g = \\sum_{t \\in g} \\rho_t$ and that $\\tau_t = \\frac{\\rho_t}{\\sigma_g}$ for a locus $g$. \r\n\r\nSimilarly, the probability of selecting a fragment from a single transcript $t$ conditioned on selecting a transcript from the locus $g$ in which $t$ is contained is\r\n\\begin{equation}\r\n\\label{eq:gammatau}\r\n\\gamma_t = \\frac{\\tau_t \\tilde{l}(t)}{\\sum_{u \\in g} \\tau_u \\tilde{l}(u)}. \r\n\\end{equation}\r\nThe parameters $\\gamma=\\{\\gamma_t\\}_{t \\in g}$ are conditional fragment abundances, and they are the parameters we estimate from the data in the next Section. Note that for a transcript $t \\in g$, $\\alpha_t = \\beta_g \\cdot \\gamma_t$ and it is easy to convert between fragment abundances and transcript abundances using Lemma \\ref{lemma:readstoprobs}.\r\n\r\nWe denote the fragment counts by $X$; specifically, we denote the number of\r\nalignments in locus $g$ by $X_g$. Note that $\\sum_{g \\in G} X_g = M$. We also\r\nuse the notation $g_r$ to denote the (unique) locus from which a read\r\nalignment $r$ can be obtained.\r\n\r\nThe likelihood function is given by\r\n\\begin{eqnarray}\r\n& & L(\\rho|R) = \\prod_{r \\in R} Pr(rd.\\ aln. =r)\\\\\r\n& = & \\prod_{r \\in R} \\sum_{g \\in G} Pr(rd.\\ aln. =r|locus=g)Pr(locus=g)\\\\\r\n& = & \\prod_{r \\in R} \\frac{\\sigma_{g_{r}}\\tilde{l}(g_{r})}{\\sum_{g \\in G} \\sigma_g\\tilde{l}(g)} Pr(rd.\\ aln. =r|locus=g_r)\\\\\r\n& = & \\prod_{r \\in R} \\beta_{g_r}\r\n\\sum_{t \\in g_r}Pr(rd.\\ aln. =r|locus=g_{r},trans. = t)Pr(trans. =t|locus=g_r)\\\\\r\n& = & \\prod_{r \\in R} \\beta_{g_r}\r\n\\sum_{t \\in g_r} \\frac{\\tau_t\\tilde{l}(t)}{\\sum_{u \\in g_r}\\tau_u \\tilde{l}(u)} Pr(rd.\\ aln. =r|locus=g_{r},trans. = t)\\\\ \r\n& = & \\left( \\prod_{r \\in R} \\beta_{g_r} \\right) \\left( \\prod_{r \\in R} \\sum_{t \\in g} \\gamma_t \\cdot\r\nPr(rd.\\ aln. =r|locus=g_r,trans. =t) \\right)\\\\\r\n& = & \\left( \\prod_{r \\in R} \\beta_{g_r} \\right) \\left( \\prod_{r \\in R} \\sum_{t \\in g} \\gamma_t\\cdot \\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right)\\\\\r\n& = & \\left( \\prod_{g \\in G} \\beta_g^{X_{g}} \\right) \\left( \\prod_{g \\in G} \\left( \\prod_{r \\in R:r \\in g} \\sum_{t \\in g} \\gamma_t \\cdot\r\n\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right) \\right). \\label{eq:likebest}\r\n\\end{eqnarray}\r\n\r\nExplicitly, in terms of the parameters $\\rho$, Equation (\\ref{eq:likebest})\r\nsimplifies to Equation (\\ref{eq:likerho}) but we will see in the next section\r\nhow the maximum likelihood estimates $\\hat{\\rho}$ are most conveniently\r\nobtained by first finding $\\hat{\\beta}$ and $\\hat{\\gamma}$ using Equation\r\n(\\ref{eq:likebest}).\r\n\r\nWe note that it is biologically meaningful to include prior distributions on\r\n$\\sigma$ and $\\tau$ that reflect the inherent stochasticity and resulting\r\nvariability of transcription in a cell. This will be an interesting direction\r\nfor further research as more RNA-Seq data (with replicates) becomes available\r\nallowing for the determination of biologically meaningful priors. In\r\nparticular, it seems plausible that specific isoform abundances may vary\r\nconsiderably and randomly within cells from a single tissue and that this may\r\nbe important in studying differential splicing. We mention to this to clarify\r\nthat in this paper, the confidence intervals we report represent the\r\nvariability in the maximum likelihood estimates $\\hat{\\sigma}_j$ and\r\n$\\hat{\\tau}^k_j$, and are not the variances of prior distributions.\r\n\r\n\r\n\\subsection{Estimation of parameters}\r\n\r\nWe begin with a discussion of identifiability of our\r\nmodel. Identifiability refers to the injectivity of the model, i.e.,\r\n\\begin{equation}\r\n\\mbox{if } Pr_{\\rho_1}(r) = Pr_{\\rho_2}(r) \\ \\forall r \\in R, \\ \\mbox{ then } \\rho_1 = \\rho_2.\r\n\\end{equation}\r\n\r\n\r\nThe identifiability of RNA-Seq models was discussed in\r\n\\cite{Hiller2009}, where a standard analysis for linear models is\r\napplied to RNA-Seq (for another related biological example, see \\cite{Pe'er2004} which discusses\r\nidentifiability of haplotypes in mixed populations from genotype data).\r\nThe results in these papers apply to our model. For completeness we review the conditions for\r\nidentifiability. Recall that $A_{R,T}$ is the fragment-transcript matrix that specifies which transcripts each fragment is compatible with. The following theorem provides a simple characterization of identifiability:\r\n\r\n\\begin{thm}\r\nThe RNA-Seq model is identifiable iff $A_{R,T}$ is full rank.\r\n\\end{thm}\r\n\r\nTherefore, for a given set of transcripts and a read set $R$, we can\r\ntest whether the model is identifiable using elementary linear\r\nalgebra. For the results in this paper, when estimating expression\r\nwith given annotations, when the model was not identifiable we picked {\\em a} maximum likelihood solution,\r\nalthough in principle it is possible to bound the total expression of\r\nthe locus and/or report identifiability problems to the user. \r\n\r\nReturning to the likelihood function\r\n\\begin{equation}\r\n\\left( \\prod_{g \\in G} \\beta_g^{X_{g}} \\right) \\left( \\prod_{g \\in G} \\left( \\prod_{r \\in R:r \\in g} \\sum_{t \\in g} \\gamma_t \\cdot\r\n\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right) \\right),\r\n\\end{equation}\r\nwe note that both the $\\beta$ and $\\gamma$ parameters depend on the $\\rho$ parameters. However, we will see that if we maximize the $\\beta$ separately from the $\\gamma$, and also each of the sets $\\{\\gamma_t:t \\in g\\}$ separately, then it is always possible to find $\\rho$ that match both the maximal $\\beta$ and $\\gamma$. In other words, \r\nthe problem of finding $\\hat{\\rho}$ is equivalent\r\nto finding $\\hat{\\beta}$ that maximizes \r\n$ \\prod_{g \\in G} \\beta_g^{X_g}$\r\nand separately, for each locus $g$, the $\\hat{\\gamma}_t$ that maximize\r\n\\begin{equation}\r\n\\prod_{r \\in R:r \\in g} \\sum_{t \\in g} \\gamma_t\r\n\\frac{F(I_t(r))}{l(t)-I_t(r)+1}.\r\n\\end{equation}\r\n\r\nWe begin by solving for the $\\hat{\\beta}$ and $\\hat{\\gamma}$ and the variances\r\nof the maximum likelihood estimates, and then explain how these are used to\r\nreport expression levels.\r\n\r\nWe can solve for the $\\hat{\\gamma}$ using the fact that the model is linear.\r\nThat is, the probability of each individual read is linear in the read\r\nabundances $\\gamma_t$. It is a standard result in statistics (see, e.g.,\r\nProposition 1.4 in \\cite{ASCB2005}) that the log likelihood function of a\r\nlinear model is concave. Thus, a hill climbing method can be used to find the\r\n$\\hat{\\gamma}$. We used the EM algorithm for this purpose.\r\n\r\nRather than using the direct ML estimates, we obtained a regularized\r\nestimate by importance sampling from the posterior distribution with a\r\nproposal distribution we explain below. The samples were also used to estimate\r\nvariances for our estimates. \r\n\r\nIt follows from standard MLE asymptotic\r\ntheory that the $\\hat{\\gamma}$ are asymptotically multivariate normal with\r\nvariance-covariance matrix given by the inverse of the observed Fisher\r\ninformation matrix. This matrix is defined as follows:\r\n\r\n\\begin{defn}[Observed Fisher information matrix]\r\nThe observed Fisher information matrix is the negative of the Hessian of the log likelihood function evaluated at the maximum likelihood estimate. That is, for parameters $\\Theta=(\\theta_1,\\ldots,\\theta_n)$, the $n \\times n$ matrix is\r\n\\begin{eqnarray}\r\n\\mathcal{F}_{k,l}(\\hat{\\Theta}) & = & - \\frac{\\partial^2 log(\\mathcal{L}(\\Theta|R))}{\\partial \\theta_k \\theta_l} |_{\\theta=\\hat{\\theta}}.\r\n\\end{eqnarray}\r\n\\end{defn}\r\nIn our case, considering a single locus $g$, the parameters are $\\Theta = (\\gamma_{t_1},\\ldots,\\gamma_{t_{|g|}})$, and as expected from Proposition \\ref{prop:linearmodel}:\r\n\\begin{eqnarray}\r\n\\label{eq:Fisher}\r\n\\mathcal{F}_{t_k,t_l}(\\hat{\\Theta}) & = & \\sum_{r \\in R:r \\in g}\r\n\\left[ \\frac{1}{\\left( \\sum_{h \\in g} \\hat{\\gamma}_h \\frac{F(I_h(r))}{l(h) - I_h(r)+1} \\right)^2} \\frac{F(I_{t_k}(r)) F(I_{t_l}(r)) }{(l(t_k)-I_{t_k}+1)(l(t_l)-I_{t_l}+1)} \\right].\r\n\\end{eqnarray}\r\n\r\n\r\nBecause some of the transcript abundances may be close to zero, we\r\nadopted the\r\nBayesian approach of \\cite{Jiang2009} and instead sampled from the\r\njoint posterior distribution of $\\Theta$ using the proposal\r\ndistribution consisting of the multivariate normal with mean\r\ngiven by the MLE, and variance-covariance matrix given by the inverse of\r\n(\\ref{eq:Fisher}). If the Observed Fisher Information Matrix is\r\nsingular then the user is warned and the confidence intervals of all\r\ntranscripts are set to $[0,1]$ (meaning that there is no information\r\nabout relative abundances).\r\n\r\nThe method used for sampling was importance sampling. The samples were used to obtain a maximum-a-posterior estimate for\r\n$\\hat{\\gamma}_t$ for each $t$ and for the variance-covariance matrix which we\r\ndenote by $\\Psi^g$ (where $g \\in G$ denotes the locus). Note that $\\Psi^g$ is\r\na $|g| \\times |g|$ matrix. The covariance between $\\hat{\\gamma}_{t_k}$ and\r\n$\\hat{\\gamma}_{t_l}$ for $t_k,t_l \\in g$ is given by $\\Psi^g_{t_k,t_l}$.\r\n\r\nTurning to the maximum likelihood estimates $\\hat{\\beta}$, we use the fact that the model is the log-linear. Therefore, \r\n\\begin{equation}\r\n\\label{eq:sigmahat}\r\n\\hat{\\beta_g} = \\frac{X_{g}}{M}.\r\n\\end{equation}\r\n\r\nViewed as a random variable, the counts $X_{g}$ are approximately Poisson and therefore the variance of the MLE $\\hat{\\beta}_g$ is approximately $X_{g}$. We note that for the tests in this paper we directly used the total counts $M$ and the proportional counts $X_g$, however it is easy to incorporate recent suggestions for total count normalization, such as \\cite{Bullard2010} into {\\tt Cufflinks}.\r\n\r\nThe favored units for reporting expression in RNA-Seq studies to date is not\r\nusing the transcript abundances directly, but rather using a measure\r\nabbreviated as FPKM, which means ``expected number of fragments per kilobase\r\nof transcript sequence per millions base pairs sequenced''. These units are\r\nequivalent to measuring transcript abundances (multiplied by a scalar). The\r\ncomputational advantage of FPKM, is that the normalization constants\r\nconveniently simplify some of the formulas for the variances of transcript\r\nabundance estimates.\r\n\r\nFor example, the abundance of a transcript $t \\in g$ in FPKM units is\r\n\\begin{equation}\r\n\\label{eq:FPKM1}\r\n \\frac{10^6 \\cdot 10^3 \\cdot \\alpha_t}{\\tilde{l}(t)} = \\frac{10^6 \\cdot 10^3 \\cdot \\beta_g \\cdot \\gamma_t}{\\tilde{l}(t)}.\r\n\\end{equation}\r\n\r\nEquation (\\ref{eq:FPKM1}) makes it clear that although the abundance of each\r\ntranscript $t \\in g$ in FPKM units is proportional to the transcript abundance\r\n$\\rho_t$ it is given in terms of the read abundances $\\beta_g$ and $\\gamma_t$\r\nwhich are the parameters estimated from the likelihood function.\r\n\r\nThe maximum likelihood estimates of $\\beta_g$ and $\\gamma_t$ are random\r\nvariables, and we denote their scaled product (in FPKM units) by $A_t$. That\r\nis $Pr(A_t = a)$ is the probability that for a random set of fragment alignments\r\nfrom a sequencing experiment, the maximum likelihood estimate of the\r\ntranscript abundance for $t$ in FPKM units is $a$.\r\n\r\nUsing the fact that the expectation of a product of independent random\r\nvariables is the product of the expectations, for a transcript $t \\in g$ we\r\nhave\r\n\\begin{equation}\r\nE[A_t] = \\frac{10^9X_{g}\\hat{\\gamma}_t}{\\tilde{l}(t)M}.\r\n\\end{equation}\r\n\r\nGiven the variance estimates for the $\\hat{\\gamma}_t$ we turn to the problem of estimating $Var[A_t]$ for a transcript $t \\in g$. We use Lemma \\ref{lemma:varproduct} to obtain\r\n\\begin{eqnarray}\r\nVar[A_t]& = & \\left(\\frac{10^9}{\\tilde{l}(t)M}\\right)^2 \\left( \\Psi^g_{t,t}X_{g} + \\Psi^g_{t,t}X^2_{g} + (\\hat{\\gamma}_t)^2 X_{g} \\right)\\\\\r\n& = & X_{g}\\left(\\frac{10^9}{\\tilde{l}(t)M)}\\right)^2 \\left( \\Psi^g_{t,t}(1+X_{g}) + (\\hat{\\gamma}_t)^2\\right).\r\n\\end{eqnarray}\r\n\r\nThis variance calculation can be used to estimate a confidence interval by\r\nutilizing the fact \\cite{Aroian1978} that when the expectation divided by the\r\nstandard deviation of at least one of two random variables is large, their\r\nproduct is approximately normal.\r\n\r\nNext we turn to the problem of estimating expression levels (and variances of\r\nthese estimates) for groups of transcripts. Let $S \\subset T$ be a group of\r\ntranscripts located in a single locus $g$, e.g. a collection of transcripts\r\nsharing a common TSS.\r\n\r\nThe analogy of Equation (\\ref{eq:FPKM1}) for the FPKM of the group is\r\n\\begin{eqnarray}\r\n\\label{eq:grouphard}\r\n& & \\frac{10^6 \\cdot 10^3 \\cdot \\beta_g \\cdot \\left( \\sum_{t \\in S} \\gamma_t\\right)}{\\tilde{l}(S)}\\\\ \r\n& = & 10^6 \\cdot 10^3 \\cdot \\beta_g \\cdot \\sum_{t \\in S} \\frac{\\gamma_t}{\\tilde{l}(t)}. \\label{eq:groupeasy}\r\n\\end{eqnarray}\r\n\r\nAs before, we denote by $B_S$ the random variables for which $Pr(B_S = b)$ is\r\nthe probability that for a random set of fragment alignments from a sequencing\r\nexperiment, the maximum likelihood estimate of the transcript abundance for\r\nall the transcripts in $S$ in FPKM units is $b$. We note that the $B_S$ are\r\nproducts and sums of random variables (Equation (\\ref{eq:groupeasy})). This\r\nmakes Equation (\\ref{eq:groupeasy}) more useful than the\r\nequivalent unsimplified Equation (\\ref{eq:grouphard}), especially because\r\n$\\tilde{l}(S)$ is, in general, a ratio of two random variables.\r\n\r\nWe again use the fact that the expectation of independent random variables is\r\nthe product of the expectation, in addition to the fact that expectation is a\r\nlinear operator to conclude that for a group of transcripts $S$,\r\n\r\n\\begin{equation}\r\n\\label{eq:expectTSS}\r\nE[B_S] = \\frac{10^9 \\cdot X_g \\cdot \\sum_{t \\in S} \\frac{\\hat{\\gamma}_t}{\\tilde{l}(t)}}{M}.\r\n\\end{equation}\r\nIn order to compute the variance of $B_S$, we first note that \r\n\\begin{equation}\r\nVar\\left[\\sum_{t \\in S} \\frac{\\hat{\\gamma}_t}{\\tilde{l}(t)}\\right] = \\sum_{t \\in S}\\frac{1}{\\tilde{l}(t)^2}\\Psi^g_{t,t} + \\sum_{t,u \\in S} \\frac{1}{\\tilde{l}(t)\\tilde{l}(u)} \\Psi^g_{t,u}.\r\n\\end{equation}\r\nTherefore,\r\n\\begin{eqnarray}\r\n& & \\quad Var[B_S] \\quad = \\quad \\nonumber \\\\ \r\n& & X_g\\left(\\frac{10^9}{M}\\right)^2\\left( \\left(1+X_g\\right) \\left(\\sum_{t \\in S}\\frac{1}{\\tilde{l}(t)^2}\\Psi^g_{t,t} + \\sum_{t,u \\in S} \\frac{1}{\\tilde{l}(t)\\tilde{l}(u)} \\Psi^g_{t,u}\\right) + \\left( \\sum_{t \\in S} \\frac{\\hat{\\gamma}_t}{\\tilde{l}(t)} \\right)^2 \\right). \\label{eq:varTSS}\r\n\\end{eqnarray}\r\n\r\nWe can again estimate a confidence interval by utilizing the fact that $B_S$\r\nis approximately normal \\cite{Aroian1978}.\r\n\r\n\\subsection{Assessment of abundance estimation}\r\n\r\nWe evaluated the accuracy of {\\tt Cufflinks}' transcript abundance estimates by first comparing the estimated FPKM values for the spiked-in sequences in each sample against their intended concentrations. Spike FPKMs were highly correlated across a 5-log dynamic range in all four samples (Supplementary Figure \\ref{spikes}). However, because sequenced spike fragments were unambiguously mappable, we performed additional simulation to measure the accuracy of the software in alternatively spliced loci. \r\n\r\n\\begin{figure}[h] \r\n \\includegraphics{pdfs/spikes.pdf}\r\n \\caption[{\\tt Cufflinks}' abundance estimates of spiked-in sequences]{{\\tt Cufflinks}' abundance estimates of spiked-in sequences.\\label{spikes}}\r\n\\end{figure}\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics[scale=0.5]{pdfs/sim_small.pdf}\r\n \\caption[Accuracy of {\\tt Cufflinks} abundance estimates]{\\emph{In silico} assessment of the accuracy of {\\tt Cufflinks}’ abundance estimation when provided with a perfect assembly (a) and after {\\it de novo} comparative assembly (b). Red points indicate in silico transcripts that were only partially recovered, where black points were fully reconstructed by {\\tt Cufflinks}. Simulated reads were aligned with {\\tt TopHat} and the alignments were provided to {\\tt Cufflinks} along with the structures of the transcripts in the simulated sample. \\label{sim}}\r\n\\end{figure}\r\n\r\nTo assess the accuracy of {\\tt Cufflinks}' estimates,\r\nwe simulated an RNA-Seq experiment using the FluxSimulator, a freely available\r\nsoftware package that models whole-transcriptome sequencing experiments with\r\nthe Illumina Genome Analyzer. \\cite{Sammeth} The software works by first\r\nrandomly assigning expression values to the transcripts provided by the user,\r\nconstructing an amplified, size-selected library, and sequencing\r\nit. Mouse UCSC transcripts were supplied to the software, along with build\r\n37.1 of the genome. FluxSimulator then randomly assigned expression ranks to\r\n18,935 transcripts, with the expression value $y$ computed from the rank $x$\r\naccording to the formula \r\n\r\n\\begin{equation}\r\n y =\\left( \\frac{x}{5.0 \\times 10^7}\\right)^{-0.6}e^{-\\left(\\frac{x}{9.5\\times{10^3}}\\right) - \\left(\\frac{x}{9.5\\times{10^3}}\\right)^2}. \r\n\\end{equation}\r\n\r\nFrom these relative expression levels, the software constructed an \\emph{in\r\nsilico} RNA sample, with each transcript assigned a number of molecules\r\naccording to its abundances. The software modeled the polyadenylation of each\r\ntranscript by adding a poly-A tail (of mean length 125nt) after the terminal\r\nexon. FluxSimulator then simulated reverse transcription of \\emph{in silico}\r\nmRNAs by random hexamer priming, followed by size selection of RT products to\r\nbetween 175 and 225 nt. The resulting ``library'' of 6,601,805 cDNA fragments\r\nwas then sampled uniformly at random for simulated sequencing, where the\r\ninitial and terminal 75bp of each selected fragment were reported as reads.\r\nFluxSimulator does not allow precise control over the number of reads\r\ngenerated (Michael Sammeth, personal communication), but nevertheless\r\ngenerated 13,203,516 75nt paired-end RNA-Seq reads. These reads included\r\nsequencing errors; FluxSimulator includes a position-specific sequencing\r\nerror model.\r\n\r\nFragments were mapped with {\\tt TopHat} to the mouse genome using identical\r\nparameters to those used to map the C2C12 reads, mapping a total of 6,176,961\r\n(93\\% of the library). These alignments were supplied along with the exact set\r\nof expressed transcripts to {\\tt Cufflinks}, to measure {\\tt Cufflinks}' abundance\r\nestimation accuracy when working with a ``perfect'' assembly (Supplementary\r\nFigure \\ref{sim}). Estimated FPKM was very close to true \\emph{in silico} FPKM\r\nacross a dynamic range of expression of nearly six orders of magnitude\r\n($R^2=0.95$).\r\n\r\n\\begin{figure}[!ht] \r\n \\includegraphics[scale=0.30]{pdfs/vs_no_discovery_small.pdf}\r\n \\caption[Improved abundance accuracy with novel\r\n transcripts]{Excluding novel C2C12 transcripts from abundance\r\n estimation results in inaccurate estimates for known transcripts. \\label{vs_no_discovery}}\r\n\\end{figure}\r\n\r\nEstimation of transcript abundances by assigning fragments to them may be inaccurate if\r\none is working with an incomplete set of transcripts for a particular sample.\r\nTo evaluate the impact of missing transcripts, we removed the newly discovered\r\ntranscripts from our high-confidence set and re-estimated the abundances of\r\nknown transcripts, and then compared them to those obtained when working with\r\nthe complete high-confidence set. While estimates of known transcripts were\r\noverall similar or identical when working with both sets, reflecting\r\nsingle-isoform or fully annotated genes, isoforms of some alternatively\r\nspliced genes differed greatly. (Supplementary Figure \\ref{vs_no_discovery})\r\n\r\nAs a final note, we point out that a na\\\"{i}ve, yet popular, current\r\napproach to expression estimation is to sum the reads mapping to a\r\ngene (where the sum is taken across all exons appearing in all\r\npossible isoforms), and then to normalize the count by either the\r\ntotal number of exonic bases, or by the average length of the\r\ntranscripts. We call the former method the ``projective normalization''\r\nmethod, and the latter the ``average length'' method.\r\n\r\n\\begin{prop}\r\nThe totally projective normalization method is correct only for single isoform\r\ngenes. If a gene has two or more isoforms the expression is underestimated.\r\n\\end{prop}\r\n{\\bf Proof}: The effective length of the gene is overestimated, hence\r\nthe expression level is underestimated. To see this, first note that\r\nthe length of some transcript in a gene is less than the total number\r\nof exonic bases among all transcripts. Then, if $a_1,\\ldots,a_n$ are\r\nreal numbers all greater than zero and $b_1,\\ldots,b_n$ are not all equal, we have\r\n\\begin{equation}\r\n\\frac{\\sum_{i=1}^na_ib_i}{\\sum_{i=1}^n a_i} < max_{i}(b_i),\r\n\\end{equation}\r\nso that the effective length in equation (\\ref{eq:effective_length}) is always less than the total\r\nnumber of exonic bases among all transcripts.\r\n\r\nStated differently, the projective normalization method has the problem that\r\nit produces numbers that are not proportional to the $\\rho$, so that\r\nit is not additive. The average length method is flawed for the same reason. The\r\ntranscript abundances are not taken into account in computing the\r\neffective lengths. In some cases the method might produce the correct\r\nanswer (for the wrong reasons), but it is bound to be incorrect on most examples,\r\nespecially in genes with transcripts of variable lengths and\r\nnon-uniform abundances.\r\n\r\n\\section{Transcript assembly}\r\n\\label{sec:assembly}\r\n\\subsection{Overview}\r\n\r\n{\\tt Cufflinks} takes as input alignments of RNA-Seq fragments to a reference\r\ngenome and, in the absence of an (optional) user provided annotation, initially assembles transcripts from the alignments. Transcripts in\r\neach of the loci are assembled independently. The assembly algorithm\r\nis designed to aim for the following:\r\n\\begin{enumerate}\r\n\\item Every fragment is consistent with at least one assembled\r\n transcript.\r\n\\item Every transcript is tiled by reads.\r\n\\item The number of transcripts is the smallest required to satisfy requirement (1).\r\n\\item The resulting RNA-Seq models (in the sense of Section \\ref{sec:abundances}) are identifiable.\r\n\\end{enumerate}\r\nIn other words, we seek an assembly that parsimoniously “explains” the fragments from the RNA-Seq experiment; every\r\nfragment in the experiment (except those filtered out during a preliminary\r\nerror-control step) should have come from a {\\tt Cufflinks} transcript, and\r\n{\\tt Cufflinks} should produce as few transcripts as possible with that property.\r\nThus, {\\tt Cufflinks} seeks to optimize the criterion suggested in \\cite{Xing2004},\r\nhowever, unlike the method in that paper, {\\tt Cufflinks} leverages Dilworth's\r\nTheorem \\cite{Dilworth1950} to solve the problem by reducing it\r\nto a matching problem via the equivalence of Dilworth's and K\\\"{o}nig's theorems (Theorem \\ref{thm:dilko} in Appendix A). Our approach to isoform reconstruction is inspired by a similar approach used for haplotype reconstruction from HIV quasi-species \\cite{Eriksson2008}.\r\n\r\n\\subsection{A partial order on fragment alignments}\r\n\r\nThe {\\tt Cufflinks} program loads a set of alignments in SAM format sorted by reference\r\nposition and assembles non-overlapping sets of alignments independently. After\r\nfiltering out any erroneous spliced alignments or reads from incompletely\r\nspliced RNAs, {\\tt Cufflinks} constructs a partial order (Definition \\ref{def:po}), or equivalently a directed acyclic graph (DAG), with\r\none node for each fragment that in turn consists of an aligned pair of mated reads. First, we note that fragment alignments are of two types: those where reads align in their entirety to the genome, and reads which have a split alignment (due to an implied intron). \r\n\r\nIn the case of single reads, the partial order can be simply\r\nconstructed by checking the reads for {\\em compatibility}. Two reads\r\nare {\\em compatible} if their overlap contains the exact same implied\r\nintrons (or none). If two reads are not compatible they are {\\em\r\n incompatible}. The reads can be partially ordered by defining, for\r\ntwo reads $x,y$, that $x\\leq y$ if the starting coordinate of $x$ is\r\nat or before the starting coordinate of $y$, and if they are\r\ncompatible.\r\n\r\nIn the case of paired-end RNA-Seq the situation is more complicated\r\nbecause the unknown sequence between mate pairs. To understand this,\r\nwe first note that pairs of fragments can still be determined to be\r\nincompatible if they cannot have originated from the same\r\ntranscript. As with single reads, this happens when there is\r\ndisagreement on implied introns in the overlap. However compatibility\r\nis more subtle. We would like to define a pair of fragments $x,y$ to\r\nbe compatible if they do not overlap, or if every implied intron in one fragment overlaps an identical implied intron in the other fragment. \r\n\r\nHowever it is important to note that it may be impossible to determine\r\nthe compatibility (as defined above) or incompatibility of a pair of fragments. For example, an unknown region internal to a fragment may overlap two different introns (that are incompatible with each other). The fragment may be compatible with one of the introns (and the fragment from which it originates) in which case it is incompatible with the other. Since the opposite situation is also feasible, compatibility (or incompatibility) cannot be assigned. Fragments for which the compatibility/incompatibility cannot be determined with respect to every other fragment are called {\\em uncertain}. Finally, two fragments are called {\\em nested} if one is contained within the other.\r\n\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics[scale=0.5]{pdfs/compatibility.pdf}\r\n \\caption[Compatibility and incompatibility of\r\n fragments]{Compatibility and incompatibility of\r\n fragments. End-reads are solid lines, unknown sequences within\r\n fragments are shown by dotted lines and implied introns are\r\n dashed lines. The reads in (a) are compatible, whereas the\r\n fragments in (b) are incompatible. The fragments in (c) are\r\n nested. Fragment $x_4$ in (d) is uncertain, because $y_4$ and\r\n $y_5$ are incompatible with each other.}\r\n\\end{figure}\r\n\r\nBefore constructing a partial order, fragments are extended to include\r\ntheir nested fragments and uncertain fragments are discarded. These discarded fragments\r\nare used in the abundance estimation. In theory, this may result in\r\nsuboptimal (i.e. non-minimal assemblies) but we determined empirically\r\nthat after assembly uncertain fragments are almost always consistent\r\nwith one of the transcripts. When they are not, there was no\r\ncompletely tiled transcript that contained them. Thus, we employ a\r\nheuristic that substantially speeds up the program, and that works\r\nin practice.\r\n\r\nA partial order $P$ is then constructed from the remaining fragments\r\nby declaring that $x \\leq y$ whenever the fragment corresponding to\r\n$x$ begins at, or before, the location of the fragment corresponding\r\nto $y$ and $x$ and $y$ are compatible. In what follows we identify $P$\r\nwith its Hasse diagram (or covering relation), equivalently a directed\r\nacyclic graph (DAG) that is the transitive reduction. \r\n\r\n\\begin{prop}\r\n$P$ is a partial order.\r\n\\end{prop}\r\n{\\bf Proof}: The fragments can be totally ordered according to the locations where they begin. It therefore suffices to check that if $x,y,z$ are fragments with $x$ compatible with $y$ and $y$ compatible with $z$ then $x$ is compatible with $z$. Since $x$ is not uncertain, it must be either compatible or incompatible with $z$. The latter case can occur only if $x$ and/or $z$ contain implied introns that overlap and are not identical. Since $y$ is not nested within $z$ and $x$ is not nested within $y$, it must be that $y$ contains an implied intron that is not identical with an implied intron in either $x$ or $z$. Therefore $y$ cannot be compatible with both $x$ and $z$. \\qed\r\n\r\n\\subsection{Assembling a parsimonious set of transcripts}\r\n\r\nIn order to assemble a set of transcripts, {\\tt Cufflinks} finds a\r\n(minimum) partition of $P$ into chains (see Definition\r\n\\ref{def:po}). A partition of $P$ into chains yields an assembly\r\nbecause every chain is a totally ordered set of compatible fragments\r\n$x_1,\\ldots,x_l$ and therefore there is a set of overlapping fragments\r\nthat connects them. By Dilworth's theorem (Theorem \\ref{thm:Dilworth}), the problem of finding a minimum partition $P$ into chains is equivalent to finding a maximum antichain in $P$ (an antichain is a set of mutually incompatible fragments). Subsequently, by Theorem \\ref{thm:dilko}, the problem of finding a maximum antichain in $P$ can be reduced to finding a maximum matching in a certain bipartite graph that emerges naturally in deducing Dilworth's theorem from K\\\"{o}nig's theorem \\ref{thm:konig}. \r\nWe call the key bipartite graph the ``reachability'' graph. It is the transitive closure of the DAG, i.e. it is the graph \r\nwhere each fragment $x$ has nodes $L_x$ and $R_x$ in the left and\r\nright partitions of the reachability graph respectively, and where there is an edge between $L_x$ and $R_y$ when $x \\leq y$ in $P$. The maximum matching problem is a classic problem that admits a polynomial time algorithm. The Hopcroft-Karp algorithm \\cite{Hopcroft1973} has a run time of $O(\\sqrt{V}E)$ where in our case $V$ is the number of fragments and $E$ depends on the extent of overlap, but is bounded by a constant times the coverage depth. We note that our parsimony approach to assembly therefore has a better complexity than the $O(V^3)$ PASA algorithm \\cite{Haas2003}. \r\n\r\nThe minimum cardinality chain decomposition computed using the approach above may not be unique.\r\nFor example, a locus may contain two putative distinct initial exons (defined by overlapping incompatible fragments), and one\r\nof two distinct terminal and a constitutive exon in between that is longer than any\r\nread or insert in the RNA-Seq experiment. In such a case, the parsimonious assembly will consist of two transcripts, but there are four possible solutions that are all minimal. In order to ``phase'' distant exons, we leverage the fact that abundance inhomogeneities can link distant exons via their coverage. We therefore weight the edges of the bipartite reachability graph based on the percent-spliced-in metric introduced by\r\nWang \\emph{et al.} in \\cite{Wang2008}. In our setting, the percent-spliced-in\r\n$\\psi_x$ for an alignment $x$ is computed by counting the alignments\r\noverlapping $x$ in the genome that are compatible with $x$ and dividing by the\r\ntotal number of alignments that overlap $x$, and normalizing for the length of\r\nthe $x$. The cost $C(y,z)$ assigned to an edge between alignments $y$ and $z$ reflects\r\nthe belief that they originate from different transcripts:\r\n\r\n\r\n\\begin{equation}\r\n C(y,z) = -\\log(1 - |\\psi_y - \\psi_z|).\r\n \\label{eq:match_cost} \r\n\\end{equation}\r\n\r\nRather than using the Hopcroft-Karp algorithm, a modified version of the {\\tt\r\nLEMON} \\cite{LEMON} and {\\tt Boost} \\cite{Boost} graph libraries are used to\r\ncompute a {\\em min-cost} maximum cardinality matching on the bipartite compatibility\r\ngraph. Even with the presence of weighted edges, our algorithm is\r\nvery fast. The best known algorithm for weighted matching is $O(V^2logV+VE)$.\r\n\r\nBecause we isolated total RNA, we\r\nexpected that a small fraction of our reads would come from the intronic\r\nregions of incompletely processed primary transcripts. Moreover, transcribed\r\nrepetitive elements and low-complexity sequence result in ``shadow''\r\ntransfrags that we wished to discard as artifacts. Thus, {\\tt Cufflinks}\r\nheuristically identifies artifact transfrags and suppresses them in its\r\noutput. We also filter extremely low-abundance minor isoforms of alternatively\r\nspliced genes, using the model described in Section \\ref {abundances} as a\r\nmeans of reducing the variance of estimates for more abundant transcripts. A\r\ntranscript $x$ meeting any of the following criteria is suppressed:\r\n\r\n\\begin{enumerate}\r\n \\item $x$ aligns to the genome entirely within an intronic region of the alignment for a transcript $y$, and the abundance of $x$ is less than 15\\% of $y$'s abundance.\r\n \\item $x$ is supported by only a single fragment alignment to the genome.\r\n \\item More than 75\\% of the fragment alignments supporting $x$, are mappable to multiple genomic loci.\r\n \\item $x$ is an isoform of an alternatively spliced gene, and has an estimated abundance less than 5\\% of the major isoform of the gene. \r\n\\end{enumerate}\r\n\r\nPrior to transcript assembly, {\\tt Cufflinks} also filters out some of the alignments\r\nfor fragments that are likely to originate from incompletely spliced nuclear\r\nRNA, as these can reduce the accuracy abundance estimates for fully spliced\r\nmRNAs. These filters and the output filters above are detailed in the source\r\nfile \\verb!filters.cpp! of the source code for {\\tt Cufflinks}.\r\n\r\nIn the overview of this Section, we mentioned that our assembly algorithm has the property that the resulting models are identifiable. This is a convenient property that emerges naturally from the parsimony criterion for a ``minimal explanation'' of the fragment alignments. Formally, it is a corollary of Dilworth's theorem:\r\n\r\n\\begin{prop}\r\nThe assembly produced by the {\\tt Cufflinks} algorithm always results in an\r\nidentifiable RNA-Seq model.\r\n\\end{prop}\r\n{\\bf Proof}: By Dilworth's theorem, the minimum chain decomposition (assembly)\r\nwe obtain has the same size as the maximum antichain in the partially\r\nordered set we\r\nconstruct from the reads. An antichain consists of reads that are\r\npairwise incompatible, and therefore those reads must form a\r\npermutation sub-matrix in the fragment-transcript matrix $A_{R,T}$ with columns corresponding to the transcripts in a locus, and with rows corresponding to the fragments in the antichain. The matrix $A_{R,T}$ therefore contains permutation sub-matrices that together span all the columns, and the matrix is full-rank.\r\n\r\n\\subsection{Assessment of assembly quality}\r\n\r\nTo compare {\\tt Cufflinks} transfrags against annotated transcriptomes, and\r\nalso to find transfrags common to multiple assemblies, we developed a tool\r\ncalled {\\tt Cuffcompare} that builds structural equivalence classes of\r\ntranscripts. We ran {\\tt Cuffcompare} on each the assembly from each time point against the\r\ncombined annotated transcriptomes of the {UCSC known genes}, {\\tt Ensembl},\r\nand {\\tt Vega}. Because of the stochastic nature of sequencing, \\emph{ab\r\ninitio} assembly of the same transcript in two different samples may result in\r\ntransfrags of slightly different lengths. A {\\tt Cufflinks} transfrag was\r\nconsidered a complete match when there was a transcript with an identical\r\nchain of introns in the combined annotation.\r\n\r\nWhen no complete match is found between a {\\tt Cufflinks} transfrag and the\r\ntranscripts in the combined annotation, {\\tt Cuffcompare} determines and reports if\r\nanother substantial relationship exists with any of the annotation\r\ntranscripts that can be found in or around the same genomic locus. For\r\nexample, when all the introns of a transfrag match perfectly a part of the\r\nintron chain (sub-chain) of an annotation transcript, a ``containment''\r\nrelationship is reported. For single-exon transfrags, containment is also\r\nreported when the exon appears fully overlapped by any of the exons of an\r\nannotation transcript. If there is no perfect match for the intron chain of a\r\ntransfrag but only some exons overlap and there is at least one intron-exon\r\njunction match, {\\tt Cuffcompare} classifies the transfrag as a putative ``novel''\r\nisoform of an annotated gene. When a transfrag is unspliced (single-exon) and\r\nit overlaps the intronic genomic space of a reference annotation transcript,\r\nthe transfrag is classified as potential pre-mRNA fragment. Finally, when no\r\nother relationship is found between a {\\tt Cufflinks} transfrag and an\r\nannotation transcript, {\\tt Cuffcompare} can check the repeat content of the\r\ntransfrag's genomic region (assuming the soft-masked genomic sequence was also\r\nprovided) and it would classify the transfrag as ``repeat'' if most of its\r\nbases are found to be repeat-masked.\r\n\r\nWhen provided multiple time point assemblies, {\\tt Cuffcompare} matches transcripts\r\nbetween samples that have an identical intron structure, placing all mutually\r\nmatching transcripts in the same equivalence class. The program reports a\r\nnon-redundant set of transcript structures, choosing the longest transcript\r\nfrom each equivalence class as the representative transcript. {\\tt Cuffcompare} also\r\nreports the relationships found between each equivalence class (transcripts\r\nthat have a complete match across time points) and reference transcripts from\r\nthe combined annotation set, where applicable.\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics{pdfs/categories.pdf}\r\n \\caption[Categorization of {\\tt Cufflinks} transcripts by estimated depth of read coverage]{Categorization of {\\tt Cufflinks} transcripts by estimated depth of read coverage. \\label{categories}}\r\n\\end{figure}\r\n\r\nTable \\ref{category_table} includes the classifications of the transfrags\r\nreported by {\\tt Cufflinks} after assembling the C2C12 reads. While only\r\n13.5\\% of assembled transfrags represent known transcripts, {\\tt Cufflinks}\r\nassigns more than 76\\% of reads to these, reflecting the fact that moderate\r\nand highly-abundant transfrags generate most of the library fragments in the\r\nexperiment. Less abundant transcripts receive less complete sequencing\r\ncoverage, resulting in numerous transfrags that partially but compatibly match\r\nknown transcripts. Supplementary Figure \\ref{categories} shows the categories\r\nof {\\tt Cufflinks} transfrags as estimated depth of sequencing coverage\r\nincreasing.\r\n\r\n\\begin{table}[h]{\r\n \\Small\r\n \\begin{tabular}{l|c|c|c} \r\n \\hline\r\n \\textbf{Category} & \\textbf{Transfrags} & \\textbf{\\% of total transfrags} & \\textbf{Assembled reads} (\\%) \\\\\r\n \\hline\r\n Match to known isoform & 39,857 & 13.5 &\t76.7 \\\\\r\n Novel isoform of known gene & 18,565 & 6.3 & 11.3 \\\\\r\n Contained in known isoform & 71,029 & 24.1 & 4.6 \\\\\r\n Repeat & 41,906 & 14.2 & 0.6 \\\\\r\n Intronic & 32,658 & 11.1 & 0.6 \\\\\r\n Polymerase run-on & 18,522 & 6.3 & 0.5 \\\\\r\n Intergenic & 48,604 & 16.5 & 1.2 \\\\\r\n Other artifacts & 22,483 & 7.7 & 4.5 \\\\\r\n \\hline\r\n Total transfrags & 293,624 & 100.0 & 100.0 \\\\\r\n \\hline\r\n \r\n \\end{tabular}\r\n }\r\n\\caption[Types of assembled transfrags]{Classification of all transfrags produced at any time point with respect to annotated gene models and masked repeats in the mouse genome. Transfrags that are present in multiple time point assemblies are multiply counted to preserve the relative distribution of transfrags among the categories across the full experiment. \\label{category_table}}\r\n\\end{table}\r\n\r\nWe selected the {\\tt Cufflinks} transfrags that did not have a complete match\r\nor ``containment'' relationship with a known annotation transcript, but were\r\nclassified by {\\tt Cuffcompare} as putative ``novel isoforms'' of known genes. We\r\nexplored the sequence similarity between these transfrags and two sets of mRNA\r\nsequences: one set representing the mouse transcriptome and consisting of all\r\nmouse ESTs in dbEST plus all reviewed or validated RefSeq mouse mRNAs, and the\r\nother consisting of all reviewed or validated RefSeq mRNAs from other\r\nmammalian species.\r\n\r\nWe used megablast to map all mouse ESTs onto this set of {\\tt Cufflinks}\r\ntransfrags, only keeping EST alignments where at least 80\\% of the EST length\r\nwas aligned with at least 95\\% identity. We calculated transfrag coverage by\r\ntiling overlapping EST mappings on each transfrag and counted only those\r\ntransfrags that are covered by ESTs for at least 80\\% of the transfrag length\r\nwithout any coverage gaps, and with coverage discontinuities only allowed at\r\nno more than 10\\% distance from either end. For the mouse mRNAs alignments we\r\nalso used megablast with the same basic coverage cutoffs (minimum 80\\% covered\r\nwith no more than 10\\% unaligned on either side of the overlap) but applied to\r\neach pairwise alignment independently (i.e. as opposed to EST alignments, no\r\ncoverage tiling was considered for mRNA alignments). For alignments with the\r\nnon-mouse mRNAs we used discontiguous megablast with a dual (combined)\r\ndiscontiguous word template (option -N 2), with the same coverage assessment\r\nprotocol as in the case of mouse mRNA alignments but with the percent identity\r\ncutoff lowered to 80\\%.\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics{pdfs/FHL3_RT-PCR.pdf}\r\n \\caption[New isoform of Fhl3]{New and known isoforms of Fhl3 recovered by {\\tt Cufflinks} at each time point (a) were confirmed by form-specific RT-PCR (b). \\label{FHL3_RT-PCR}}\r\n\\end{figure}\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics{pdfs/Additional_RT-PCR.pdf}\r\n \\caption[RT-PCR validation of selected genes]{RT-PCR of selected genes. For Schip1, {\\tt Cufflinks} assembled a known and a novel isoform (with a new TSS), both of which are detected by RT-PCR. Prkar1a is annotated with two alternate first exons and start sites in {\\tt UCSC known genes}, both of which were detected. {\\tt Cufflinks} assembles the known isoform of the splicing factor Sfpq, along with a novel variant that contains most of RIKEN clone. Tpm1, a gene known to have muscle- and non-muscle-specific isoforms displays previously observed alternative first and last exons. \\label{RT-PCR}}\r\n\\end{figure}\r\n\r\n\\begin{table}[h]{\r\n \\Small\r\n \\begin{tabular}{r|l|c|c}\r\n \r\n \\hline\r\n \\textbf{Primer name}\t& \\textbf{sequence}\t& \\textbf{product length} & \\textbf{endpoint gel score} \\\\\r\n \\hline\r\n \r\n \\textbf{FHL3 Ex1Ex3}\t& & & \\\\\t\t\r\n Left &\tCTCGCCGCTGCTCTCTCG & 221 & +++ \\\\\r\n Right &\tGTGTTGTCATAGCACGGAACG & &\t\\\\\t\r\n \\textbf{FHL3 Ex2Ex3}\t & & & \\\\\t\t\r\n Left &\tAGGAAGGGCTCACAAGTGG\t& 407 & +++ \\\\\r\n Right &\tATAGCACGGAACGCAGTAGG & & \\\\\t\t\r\n \\textbf{Sfpq Ex9Ex10} & & & \\\\\t\t\r\n Left & GTGGTGGCATAGGTTATGAAGC & 936 & +++ \\\\\r\n Right & CCATTTTCAAAAGCTTTCAAGG & & \\\\\t\t\r\n \\textbf{Sfpq Ex9Ex11} & & & \\\\\t\t\r\n Left & GTGGTGGCATAGGTTATGAAGC & 172 & +++ \\\\\r\n Right & CTCAAGTAAATAAGACTCCAAAATCAGC & & \\\\\t\t\r\n \\textbf{Prkar1aEx1Ex3} & & & \\\\\t\t\r\n Left & ACAGCAGGGATCTCCTTGTCC & 418 & +++ \\\\\r\n Right & CCTCTCAAAGTATTCCCGAAGG & & \\\\\t\t\r\n \\textbf{Prkar1aEx2Ex3} & & & \\\\\t\t\t\r\n Left & GCTATCGCAGAGTGGTAGTGAGG & 279 & +++ \\\\\r\n Right & CCTCTCAAAGTATTCCCGAAGG & & \\\\\t\t\t\r\n \\textbf{Schip1Ex1Ex3} & & & \\\\\t\t\r\n Left & GGCTATGAGGGTGAAAAGTGC & 1050\t& +++ \\\\\r\n Right & GTATAGATTCCTGGGCCATCG & & \\\\\t\t\r\n \\textbf{Schip1Ex2Ex3} & & & \\\\\t\t\r\n Left & CAGCATGAGTGGTAACCAAGG & 269 & +++ \\\\\r\n Right & GTATAGATTCCTGGGCCATCG & & \\\\\t\t\r\n \\textbf{Tpm1Ex1Ex3} & & & \\\\\t\t\r\n Left & TGAACAAAAGACCCCAGAGG & 565 & +++ \\\\\r\n Right & CTGAAGTACAAGGCCATCAGC & & \\\\\t\t\r\n \\textbf{Tpm1Ex2Ex3} & & & \\\\\t\t\t\r\n Left & AGTTTTATTGAGCGTTGAGACG &\t318 & +++ \\\\\r\n Right & CTGAAGTACAAGGCCATCAGC & & \\\\\r\n \\hline\r\n \r\n \\end{tabular}\r\n }\r\n\\caption[Form-specific RT-PCR primers for selected genes]{Form-specific RT-PCR primers for selected genes, designed with Primer3 \\cite{Rozen2000}. \\label{RT-PCR_primers}}\r\n\\end{table}\r\n\r\n\r\nTo assess the dependence of assembly quality on the depth of sequencing, we\r\nmapped and assembled subsets of our reads at the 60 hour time point. We\r\npartitioned the three Illumina lanes' worth of data (a total of ~140 million\r\nreads) into 64 subsets. We then processed a single subset with {\\tt TopHat}\r\nand {\\tt Cufflinks}, as above, and compared the resulting transfrags to the\r\noutput of {\\tt Cufflinks} on all three lanes using {\\tt Cuffcompare}. We\r\nrepeated the mapping and assembly with two subsets, four subsets, eight, and\r\nso on. Figure 4 in the main text shows the fraction of reference transcripts\r\ncaptured by {\\tt Cufflinks} using all three lanes that are still captured when\r\nless data is available. For transcripts with low abundance ($<$15\r\nFPKM), increased sequencing yields more full-length transcripts. However, for\r\neven moderately abundant transcripts ($\\geq$15 FPKM), 75\\% or more of\r\nthe transcripts are recovered with only ~40 million reads, or a lane's worth\r\nof Illumina GA II sequencing.\r\n\r\n\\section{Analysis of gene expression dynamics}\r\n\r\nExpression dynamics of genes are composed of absolute changes in\r\noverall transcript abundances, as well as relative changes in\r\ntranscript abundances over time. Moreover, select groups of\r\ntranscripts, for example transcripts grouped by TSS, may exhibit\r\nspecific dynamics due to the underlying biological mechanisms that\r\ndrive expression.\r\n\r\nIn this section we describe statistical tests we developed in the\r\nmultiple hypothesis testing framework for examining absolute and\r\nrelative changes in arbitrary groups of transcripts.\r\n\r\n\\subsection{Selection of high-confidence transcripts for expression tracking}\r\n\r\nWe first restricted our analysis of expression dynamics over the time-course\r\nto a set of transcripts we believed were fully sequenced and correctly\r\nassembled, and we focused only on known and reliable novel isoforms of\r\nannotated genes. This set consisted of transcripts that either were present in\r\nthe {\\tt UCSC genome browser}, {\\tt Ensembl}, or {\\tt Vega} annotated\r\ntranscriptomes, or were found in multiple C2C12 time point assemblies. We\r\nignored transfrags classified as intronic pre-mRNA or polymerase run-on, as\r\nwell as intergenic repeats to focus on coding genes and long non-coding RNAs.\r\nThis high-confidence set contained a total of 17,416 transcripts, 13,692 of\r\nwhich were in {\\tt UCSC known genes}, {\\tt Ensembl} or {\\tt VEGA} annotation\r\nand 3,724 of which are novel. Running {\\tt Cufflinks}' abundance estimation\r\nalgorithm on this high-confidence set of transcripts at each time point\r\nallowed us to scan for differentially expressed transcripts, differentially\r\nspliced pre-mRNAs, and genes with shifts in promoter preference.\r\n\r\n\\subsection{Testing for changes in absolute expression} \r\n\r\nBetween any two consecutive time points, we tested whether a transcript was\r\nsignificantly (after FDR control \\cite{Benjamini1995}) up or down regulated with respect to the\r\nnull hypothesis of no change, with variability in expression due solely to the\r\nuncertainties resulting from our abundance estimation procedure. This was done\r\nusing the following testing procedure for absolute differential expression:\r\n\r\nWe employed the standard method used in microarray-based expression analysis\r\nand proposed for RNA-Seq in \\cite{Bullard2010}, which is to compute the\r\nlogarithm of the ratio of intensities (in our case FPKM), and to then use the\r\ndelta method to estimate the variance of the log odds. We describe this for\r\ntesting differential expression of individual transcripts and also groups of\r\ntranscripts (e.g. grouped by TSS).\r\n\r\nWe recall that the MLE FPKM for a transcript $t$ in a locus $g$ is given by \r\n\\begin{equation}\r\n\\frac{10^9X_g\\hat{\\gamma}_t}{\\tilde{l}(t)M}.\r\n\\end{equation}\r\nGiven two different experiments resulting in $X^a_g,M^a$ and $X^b_g,M^b$ respectively, as well as $\\hat{\\gamma}^a_t$ and $\\hat{\\gamma}^b_t$, we would like to test the significance of departures from unity of the ratio of MLE FPKMS, i.e. \r\n\\begin{eqnarray}\r\n& & \\left(\\frac{10^9X^a_g\\hat{\\gamma}^a_t}{\\tilde{l}(t)M^a}\\right) / \\left(\\frac{10^9X^b_g\\hat{\\gamma}^b_t}{\\tilde{l}(t)M^b}\\right)\\\\\r\n& = & \\frac{X_g^a\\hat{\\gamma}^a_tM^b}{X_g^b \\hat{\\gamma}^b_tM^a}.\r\n\\end{eqnarray}\r\n\r\nThis can be turned into a test statistic that is approximately normal by\r\ntaking the logarithm, and normalizing by the variance. We recall that using\r\nthe delta method, if $X$ is a random variable then $Var[log(X)] \\approx\r\n\\frac{Var[X]}{E[X]^2}$.\r\n\r\nTherefore, our test statistic is \r\n\\begin{equation}\r\n\\frac{log(X^a_g)+log(\\hat{\\gamma}^a_t)+log(M^b)-log(X^b_g)-log(\\hat{\\gamma}^b_t)-log(M^a)}\r\n{ \\sqrt{\\frac{\\left( \\Psi^{g,a}_{t,t}(1+X^a_{g}) + (\\hat{\\gamma}^a_t)^2\\right)}{X^a_g \\left(\\hat{\\gamma}^a_t \\right)^2}+\r\n\\frac{\\left( \\Psi^{g,b}_{t,t}(1+X^b_{g}) + (\\hat{\\gamma}^b_t)^2\\right)}{X^b_{g}\\left( \\hat{\\gamma}^b_t\\right)^2} } }.\r\n\\end{equation}\r\n\r\nIn order to test for differential expression of a group of transcripts, we\r\nreplace the numerator and denominator above by those from Equations\r\n(\\ref{eq:expectTSS}) and (\\ref{eq:varTSS}).\r\n\r\nIt is has been noted that the power of differential expression tests in RNA-Seq depend on the length of the transcripts being tested, because longer transcripts accumulate more reads \\cite{Oshlack2009}. This means that the results we report are biased towards discovering longer differentially expressed transcripts and genes.\r\n\r\n\\subsection{Quantifying transcriptional and post-transcriptional overloading}\r\n\r\nIn order to infer the extent of differential promoter usage, we\r\ndecided to measure changes in relative abundances of primary\r\ntranscripts of single genes. Similarly, we investigated changes in\r\nrelative abundances of transcripts grouped by TSS in order to infer\r\ndifferential splicing. These inferences required two ingredients: \r\n\\begin{enumerate}\r\n\\item A metric on probability distributions (derived from relative\r\n abundances).\r\n\\item A test statistic for assessing significant changes in\r\n differential promoter usage and splicing as measured using the\r\n metric referred to above. \r\n\\end{enumerate}\r\n\r\nIn order to address the first requirement, namely a metric on\r\nprobability distributions, we turned to an entropy-based metric. This\r\nwas motivated by the methods in \\cite{Ritchie2008} where tests for\r\ndifferences in relative isoform abundances were performed to\r\ndistinguish cancer cells from normal cells. We extend\r\nthis approach to be able to test for relative isoform abundance changes among multiple\r\nexperiments in RNA-Seq.\r\n\r\n\\begin{defn}[Entropy]\r\nThe entropy of a discrete probability distribution\r\n$p=(p_1,\\ldots,p_n)$ ($0 \\leq p_i \\leq 1$ and $\\sum_{i=1}^n p_i = 1$) is \r\n\\begin{equation}\r\nH(p) = -\\sum_{i=1}^n p_i log p_i.\r\n\\end{equation}\r\nIf $p_i=0$ for some $i$ the value of $p_i log p_i$ is taken to be $0$.\r\n\\end{defn}\r\n\\begin{defn}[The Jensen-Shannon divergence]\r\nThe Jensen-Shannon divergence of $m$ discrete probability distributions $p^1,\\ldots,p^m$ is defined to be:\r\n\\begin{equation}\r\nJS(p^1,\\ldots,p^m) = H\\left(\\frac{p^1 + \\cdots + p^m}{m}\\right) - \\frac{\\sum_{j=1}^m H(p^j)}{m}.\r\n\\end{equation}\r\n\r\nIn other words, the Jensen-Shannon divergence of a set of probability\r\ndistributions is the entropy of their average minus the average of their\r\nentropies. \\end{defn}\r\n\r\nIn the case where $m=2$, we remark that the Jensen-Shannon divergence can also\r\nbe described in terms of the Kullback-Leibler divergence of two discrete\r\nprobability distributions. If we denote Kullback-Leibler divergence by\r\n\r\n\\begin{equation}\r\nD(p^1\\|p^2) = \\sum_i p^1_i log \\frac{p^1_i}{p^2_i},\r\n\\end{equation}\r\nthen\r\n\\begin{equation}\r\nJS(p^1,p^2) = \\frac{1}{2}D(p^1\\|m)+\\frac{1}{2}D(p^2\\|m)\r\n\\end{equation}\r\nwhere $m=\\frac{1}{2}(p^1+p^2)$. In other words the Jensen-Shannon\r\ndivergence is a symmetrized variant of the Kullback-Leibler divergence.\r\n\r\nThe Jensen-Shannon divergence has a number of useful properties: for\r\nexample it is symmetric and non-negative. However it is {\\em not} a\r\nmetric. The following theorem shows how to construct a metric\r\nfrom the Jensen-Shannon divergence:\r\n\r\n\\begin{thm}[Fuglede and Tops{\\o}e 2004 \\cite{Fuglede2004}]\r\nThe square root of the Jensen-Shannon divergence is a metric.\r\n\\end{thm}\r\n\r\nThe proof of this result is based on a harmonic analysis argument that is the basis for\r\nthe remark in the main paper that ``transcript abundances move in time along a\r\nlogarithmic spiral in Hilbert space''. We therefore call the square root of the Jensen-Shannon divergence the {\\em Jensen-Shannon metric}. We employed this metric in\r\norder to quantify relative changes in expression in (groups of) transcripts.\r\n\r\nIn order to test for significance, we introduce a bit of notation.\r\nSuppose that $S$ is a collection of transcripts (for example, they may\r\n share a common TSS). We define\r\n\\begin{equation}\r\n\\kappa_t = \\frac{ \\frac{\\gamma_t}{\\tilde{l}(t)}}{\\sum_{u \\in S} \\frac{\\gamma_u}{\\tilde{l}(u)}}\r\n\\end{equation}\r\nto be the proportion of transcript $t$ among all the transcripts in a\r\ngroup $S$. We let \r\n$Z=\\sum_{u \\in S} \\hat{\\gamma}_u / \\tilde{l}(u)$ so that $\\hat{\\kappa}_t = \\frac{\\gamma_t}{\\tilde{l}(t)Z}$. We therefore have that\r\n\\begin{eqnarray}\r\n\\label{eq:variancekappa1}\r\nVar[\\hat{\\kappa}_t] & = &\\frac{Var[\\hat{\\gamma}_t]}{\\tilde{l}(t)^2Z^2}, \\\\\r\nCov[\\hat{\\kappa}_t,\\hat{\\kappa}_u] & = & \\frac{Cov[\\hat{\\gamma}_t,\\hat{\\gamma}_u]}{\\tilde{l}(t)\\tilde{l}(u)Z^2}.\\label{eq:variancekappa2}\r\n\\end{eqnarray}\r\n\r\nOur test statistic for divergent relative expression was the\r\nJensen-Shannon metric. The test could be applied to multiple time\r\npoints simultaneously, but we focused on pairwise tests (involving\r\nconsecutive time points). Under the null hypothesis of no change in\r\nrelative expression, the Jensen-Shannon metric should be\r\nzero. We tested for this using a one-sided $t$-test, based on an\r\nasymptotic derivation of the distribution of the Jensen-Shannon metric\r\nunder the null hypothesis. This asymptotic distribution is normal by applying the delta method approximation,\r\nwhich involves computing the linear component of the Taylor expansion of the\r\nvariance of $\\sqrt{JS}$.\r\n\r\nIn order to simplify notation, we let $f(p^1,\\ldots,p^m)$ be the\r\nJensen-Shannon metric for $m$ probability distributions $p^1,\\ldots,p^m$.\r\n\r\n\\begin{lemma}\r\nThe partial derivatives of the Jensen-Shannon metric are give by\r\n\\begin{equation}\r\n\\frac{\\partial f}{\\partial p^k_l} = \\frac{1}{2m\\sqrt{f(p^1,\\ldots,p^m)}} log \\left( \\frac{p^k_l}{\\frac{1}{m} \\sum_{j=1}^m p_l^j} \\right).\r\n\\end{equation}\r\n\\end{lemma}\r\n\r\nLet $\\hat{\\kappa}^1,\\ldots,\\hat{\\kappa}^m$ denote $m$ probability\r\ndistributions on the set of transcripts $S$, for example the MLE for the transcript abundances in a time course. Then from the delta method we\r\nhave that $\\sqrt{JS(\\hat{\\kappa}^1,\\ldots,\\hat{\\kappa}^m)}$ is approximately normally\r\ndistributed with variance given by\r\n\r\n\\begin{equation}\r\nVar[\\sqrt{JS(\\hat{\\kappa}^1,\\ldots,\\hat{\\kappa}^m)}] \\approx (\\bigtriangledown f)^T \\Sigma (\\bigtriangledown f),\r\n\\end{equation}\r\n\r\nwhere $\\Sigma$ is the variance-covariance matrix for the\r\n$\\kappa^1,\\ldots,\\kappa^m$, i.e., it is a block diagonal matrix where the\r\n$i$th block is the variance-covariance matrix for the $\\kappa^i_t$ given by\r\nEquations (\\ref{eq:variancekappa1},\\ref{eq:variancekappa2}).\r\n\r\n\r\nThere are two biologically meaningful groupings of transcripts whose\r\nrelative abundances are interesting to track in a\r\ntime course. Transcripts that share a TSS are likely to be regulated by\r\nthe same promoter, and therefore tracking the change in relative\r\nabundances of groups of transcripts sharing a TSS may reveal how\r\ntranscriptional regulation is affecting expression over\r\ntime. Similarly, transcripts that share a TSS and exhibit changes in\r\nexpression relative to each other are likely to be affected by\r\nsplicing or other post-transcriptional regulation. We therefore\r\ngrouped transcripts by TSS and compared relative abundance changes\r\nwithin and between groups. \r\n\r\nWe define ``overloading'' to be a significant change in relative\r\nabundances for a set of transcripts (as determined by the Jensen-Shannon metric, see below). The term is intended to generalize the simple notion of\r\n``isoform switching'' that is well-defined in the case of two\r\ntranscripts, to multiple transcripts. It is complementary to absolute\r\ndifferential changes in expression: the overall expression of a gene\r\nmay remain constant while individual transcripts change drastically in\r\nrelative abundances resulting in overloading. The term is borrowed from\r\ncomputer science, where in some statically-typed programming\r\nlanguages, a function may be used in multiple, specialized instances\r\nvia ``method overloading''. \r\n\r\nWe tested for overloaded genes by performing a one-sided $t$-test based on the asymptotics of the Jensen-Shannon metric under the null hypothesis of no change in relative abundnaces of isoforms (either grouped by shared TSS for for post-transcriptional overloading, or by comparison of groups of isoforms with shared TSS for transcriptional overloading). Type I errors were controlled with the Benjamini-Hochberg \\cite{Benjamini1995} correction for multiple testing. \r\n A selection of overloaded genes are displayed in Supplemental Figs. \\ref{splice_overloaded} and \\ref{promoter_overloaded}.\r\n\r\n\\newpage\r\n\r\n\\begin{figure}[!ht] \r\n \\includegraphics{pdfs/splice_overloaded}\r\n \\caption[Selected genes with post-transcriptional\r\n overloading]{Selected genes with post-transcriptional\r\n overloading. Trajectories indicate the expression of individual\r\n isoforms in FPKM ($y$ axis) over time in hours ($x$\r\n axis). Dashed isoforms have not been previously\r\n annotated. Isoform trajectories are colored by TSS, so isoforms\r\n with the same color presumably share a common promoter and are\r\n processed from the same primary transcript. It is evident that\r\n total gene expression may remain constant during isoforms\r\n switching (Eya3) while in other cases changes in relative\r\n abundance are accompanied by changes in absolute expression. \r\nThe Jensen-Shannon metric generalizes the notion of ``isoform\r\nswitching'' and is useful in cases with multiple isoforms\r\n(e.g. Ddx17). \r\n \\label{splice_overloaded}}\r\n\\end{figure}\r\n\r\n\\begin{figure}[!ht] \r\n \\includegraphics{pdfs/promoter_overloaded}\r\n \\caption[Selected genes with transcriptional overloading]{Selected\r\n genes with transcriptional overloading. Trajectories indicate\r\n the expression of individual isoforms in FPKM (y axis) over time\r\n in hours (x axis). Dashed isoforms have not been previously\r\n annotated. Isoform trajectories are colored by TSS, so that\r\n isoforms with different colors presumably vary in their promoter\r\n and are processed from different primary transcripts. \\label{promoter_overloaded}}\r\n\\end{figure}\r\n\r\nWe can visualize overloading and expression dynamics with a plot that\r\nsuperimposes transcriptional and post-transcriptional overloading and\r\ngene-level expression over the time course. We refer to these as\r\n``Minard plots'', after Charles Joseph Minard's famous depiction\r\nof the advance and retreat of\r\nNapoleon's armies in the campaign against Russia in 1812\r\n\\cite{Tufte2001}. Minard made use of multiple visual cues to display\r\nnumerous varying quantities in one diagram. An example of a Minard\r\nplot for the gene\r\nMyc is shown in Figure 3c, and others are given in Appendix B. The dotted line indicates gene-level FPKM, with measured\r\nFPKM indicated by black circles. Grey circles indicate the arithmetic mean of\r\ngene-level FPKM between consecutive measured time points, interpolating FPKM\r\nat intermediate time points. The total gene expression overloading is\r\nvisualized as a swatch centered around the interpolated expression curve.\r\nThe width of the swatch encodes the amount of expression overloading between\r\nsuccessive time points. The color of the swatch indicates the relative\r\ncontributions of transcriptional and post-transcriptional expression\r\noverloading.\r\n\r\nSome genes, such as tropomyosin I and II, feature a single primary\r\ntranscript, and so all overloading is by definition post-transcriptional.\r\nOthers, like Fhl3, have two primary transcripts, but only a single isoform\r\narises from each, so all overloading is transcriptional. Genes with multiple\r\nprimary transcripts, one or more of which are alternatively spliced, such as\r\nMyc or RTN4, display both forms.\r\n\r\n\r\n\\section{The {\\tt Cufflinks} software}\r\n\r\nThe transcript assembly and abundance estimation algorithms are implemented in\r\nfreely available open source software called {\\tt Cufflinks} that is available\r\nfrom \\newline {\\tt http://cufflinks.cbcb.umd.edu/} \\newline Furthermore\r\nmethods for comparing annotations across time points, and for performing the\r\ndifferential expression, promoter usage and splicing tests are implemented in\r\nthe companion programs {\\tt Cuffdiff} and {\\tt Cuffcompare}. Instructions on\r\nhow to install and run the software are provided on the website.\r\n\r\nThe input to {\\tt Cufflinks} consists of fragment alignments in the SAM format\r\n\\cite{Li2009a}. These may consist of either single fragment alignments, or alignments of mate-pairs (paired-end reads produce better assemblies and more accurate abundance estimates than single reads). {\\tt Cufflinks} will assemble the transcripts using the\r\nalgorithm in Section \\ref{sec:assembly}, and transcript abundances will be\r\nestimated using the model in Section \\ref{sec:abundances}. Transcript\r\ncoordinates and abundances are reported in the Gene Transfer Format (GTF).\r\nUser supplied annotations may be provided to {\\tt Cufflinks} (optional input) in\r\nwhich case they form the basis for the transcript abundance estimation.\r\n\r\nSome of the algorithms here rely on sufficient depth of sequencing in order to\r\nproduce reliable output. {\\tt Cufflinks} determines that depth is sufficient where\r\npossible to check that required assumptions hold. For example, in loci where one\r\nor more isoforms have extremely low relative expression, the observed Fisher\r\nInformation Matrix may not be positive definite after rounding errors. In this\r\ncase, it is not possible to produce a reliable variance-covariance matrix for\r\nisoform fragment abundances. {\\tt Cufflinks} will report a numerical\r\nexception in this and similar cases. When an exception is reported, the\r\nconfidence intervals for the isoforms' abundances will be set from 0\r\nFPKM to the FPKM for the whole gene. If such an exception is generated during\r\na {\\tt Cuffdiff} run, no differential analysis involving the problematic\r\nsample will be performed on that locus. \r\n\\newpage \r\n\\section{Appendix A: Lemmas and Theorems}\r\n\r\nThe following elementary/classical results are required for our methods and we include them so that the supplement is self-contained.\r\n\r\n\\begin{lemma}\r\n\\label{lemma:varsum}\r\nLet $X_1,\\ldots,X_n$ be random variables and $a_1,\\ldots,a_n$ real numbers with $Y=\\sum_{i=1}^na_iX_i$. Then\r\n\\begin{equation}\r\nVar[Y] = \\sum_{i=1}^n a_i^2Var[X_i]+2\\sum_{i I_{t_2}(r)$ due to the presence of the extra internal exon in $t_1$. }\r\n\\end{figure}\r\n\r\nWe are now ready to write down the likelihood equation for the model. We will\r\nwrite $L(\\rho|R)$ for the likelihood of a set of fragment alignments $R$\r\nconstructed from $M$ reads. The notation $Pr(trans. = t)$ means ``the\r\nprobability that a fragment selected at random originates from transcript $t$''.\r\n\r\n\\begin{eqnarray}\r\n& & L(\\rho|R) = \\prod_{r \\in R} Pr(rd.\\ aln. =r)\\\\\r\n& = & \\prod_{r \\in R} \\sum_{t \\in T} Pr(rd.\\ aln. =r|trans. =t)Pr(trans. =t)\\\\\r\n& = & \\prod_{r \\in R} \\sum_{t \\in T} \\frac{\\rho_t\\tilde{l}(t)}{\\sum_{u \\in T}\\rho_u\\tilde{l}(u)} Pr(rd.\\ aln. = r|trans. =t)\\\\ \r\n& = & \\prod_{r \\in R} \\sum_{t \\in T}\\frac{\\rho_t\\tilde{l}(t)}{\\sum_{u \\in T}\\rho_u\\tilde{l}(u)} \\left(\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right) \\\\ \\label{eq:likerho}\r\n& = & \\prod_{r \\in R} \\sum_{t \\in T} \\alpha_t\\left(\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right),\r\n\\end{eqnarray}\r\nwhere \r\n\\begin{equation}\r\n\\alpha_t = \\frac{\\rho_t\\tilde{l}(t)}{\\sum_{u \\in T}\\rho_u\\tilde{l}(u)}.\r\n\\end{equation}\r\n\r\nObserve that $\\alpha_t$ is exactly the probability that a fragment selected at\r\nrandom comes from transcript $t$, and we have that $\\sum_{t \\in T}\\alpha_t =\r\n1$. In light of the probabilistic meaning of the $\\alpha=\\{\\alpha_t\\}_{t \\in T}$, we refer to them as\r\n{\\em fragment abundances}.\r\n\r\nIt is evident that the likelihood function is that of a linear model\r\nand that the likelihood function is\r\nconcave (Proposition \\ref{prop:linearmodel}) so a numerical method can be used\r\nto find the $\\alpha$. It is then possible, in principle, to recover the $\\rho$\r\nusing Lemma \\ref{lemma:readstoprobs}. However the number of parameters is in the tens of\r\nthousands, and in practice this form of the likelihood function is unwieldy.\r\nInstead, we re-write the likelihood utilizing the fact that transcripts in\r\ndistinct loci do not overlap in genomic location.\r\n\r\nWe first calculate the probability that a fragment originates from a transcript within a given locus $g$:\r\n\\begin{eqnarray}\r\n\\beta_g & := & \\sum_{t \\in g} \\alpha_t\\\\\r\n& = & \\frac{\\sum_{t \\in g} \\rho_t \\tilde{l}(t)}{\\sum_{u \\in T} \\rho_u \\tilde{l}(u)}\\\\\r\n& = & \\frac{\\sum_{t \\in g} \\sigma_g\\tau_t \\tilde{l}(t)}{\\sum_{h \\in G} \\sum_{u \\in h} \\sigma_h\\tau_u \\tilde{l}(u)}\\\\\r\n& = & \\frac{\\sigma_g\\sum_{t \\in g} \\tau_t \\tilde{l}(t)}{\\sum_{h \\in G} \\sigma_h \\sum_{u \\in h} \\tau_u \\tilde{l}(u)}\\\\\r\n & = & \\frac{\\sigma_g \\tilde{l}(g)}{\\sum_{h \\in G} \\sigma_h \\tilde{l}(h)}. \r\n\\end{eqnarray}\r\nRecall that $\\sigma_g = \\sum_{t \\in g} \\rho_t$ and that $\\tau_t = \\frac{\\rho_t}{\\sigma_g}$ for a locus $g$. \r\n\r\nSimilarly, the probability of selecting a fragment from a single transcript $t$ conditioned on selecting a transcript from the locus $g$ in which $t$ is contained is\r\n\\begin{equation}\r\n\\label{eq:gammatau}\r\n\\gamma_t = \\frac{\\tau_t \\tilde{l}(t)}{\\sum_{u \\in g} \\tau_u \\tilde{l}(u)}. \r\n\\end{equation}\r\nThe parameters $\\gamma=\\{\\gamma_t\\}_{t \\in g}$ are conditional fragment abundances, and they are the parameters we estimate from the data in the next Section. Note that for a transcript $t \\in g$, $\\alpha_t = \\beta_g \\cdot \\gamma_t$ and it is easy to convert between fragment abundances and transcript abundances using Lemma \\ref{lemma:readstoprobs}.\r\n\r\nWe denote the fragment counts by $X$; specifically, we denote the number of\r\nalignments in locus $g$ by $X_g$. Note that $\\sum_{g \\in G} X_g = M$. We also\r\nuse the notation $g_r$ to denote the (unique) locus from which a read\r\nalignment $r$ can be obtained.\r\n\r\nThe likelihood function is given by\r\n\\begin{eqnarray}\r\n& & L(\\rho|R) = \\prod_{r \\in R} Pr(rd.\\ aln. =r)\\\\\r\n& = & \\prod_{r \\in R} \\sum_{g \\in G} Pr(rd.\\ aln. =r|locus=g)Pr(locus=g)\\\\\r\n& = & \\prod_{r \\in R} \\frac{\\sigma_{g_{r}}\\tilde{l}(g_{r})}{\\sum_{g \\in G} \\sigma_g\\tilde{l}(g)} Pr(rd.\\ aln. =r|locus=g_r)\\\\\r\n& = & \\prod_{r \\in R} \\beta_{g_r}\r\n\\sum_{t \\in g_r}Pr(rd.\\ aln. =r|locus=g_{r},trans. = t)Pr(trans. =t|locus=g_r)\\\\\r\n& = & \\prod_{r \\in R} \\beta_{g_r}\r\n\\sum_{t \\in g_r} \\frac{\\tau_t\\tilde{l}(t)}{\\sum_{u \\in g_r}\\tau_u \\tilde{l}(u)} Pr(rd.\\ aln. =r|locus=g_{r},trans. = t)\\\\ \r\n& = & \\left( \\prod_{r \\in R} \\beta_{g_r} \\right) \\left( \\prod_{r \\in R} \\sum_{t \\in g} \\gamma_t \\cdot\r\nPr(rd.\\ aln. =r|locus=g_r,trans. =t) \\right)\\\\\r\n& = & \\left( \\prod_{r \\in R} \\beta_{g_r} \\right) \\left( \\prod_{r \\in R} \\sum_{t \\in g} \\gamma_t\\cdot \\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right)\\\\\r\n& = & \\left( \\prod_{g \\in G} \\beta_g^{X_{g}} \\right) \\left( \\prod_{g \\in G} \\left( \\prod_{r \\in R:r \\in g} \\sum_{t \\in g} \\gamma_t \\cdot\r\n\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right) \\right). \\label{eq:likebest}\r\n\\end{eqnarray}\r\n\r\nExplicitly, in terms of the parameters $\\rho$, Equation (\\ref{eq:likebest})\r\nsimplifies to Equation (\\ref{eq:likerho}) but we will see in the next section\r\nhow the maximum likelihood estimates $\\hat{\\rho}$ are most conveniently\r\nobtained by first finding $\\hat{\\beta}$ and $\\hat{\\gamma}$ using Equation\r\n(\\ref{eq:likebest}).\r\n\r\nWe note that it is biologically meaningful to include prior distributions on\r\n$\\sigma$ and $\\tau$ that reflect the inherent stochasticity and resulting\r\nvariability of transcription in a cell. This will be an interesting direction\r\nfor further research as more RNA-Seq data (with replicates) becomes available\r\nallowing for the determination of biologically meaningful priors. In\r\nparticular, it seems plausible that specific isoform abundances may vary\r\nconsiderably and randomly within cells from a single tissue and that this may\r\nbe important in studying differential splicing. We mention to this to clarify\r\nthat in this paper, the confidence intervals we report represent the\r\nvariability in the maximum likelihood estimates $\\hat{\\sigma}_j$ and\r\n$\\hat{\\tau}^k_j$, and are not the variances of prior distributions.\r\n\r\n\r\n\\subsubsection{Estimation of parameters}\r\n\r\nWe begin with a discussion of identifiability of our\r\nmodel. Identifiability refers to the injectivity of the model, i.e.,\r\n\\begin{equation}\r\n\\mbox{if } Pr_{\\rho_1}(r) = Pr_{\\rho_2}(r) \\ \\forall r \\in R, \\ \\mbox{ then } \\rho_1 = \\rho_2.\r\n\\end{equation}\r\n\r\n\r\nThe identifiability of RNA-Seq models was discussed in\r\n\\cite{Hiller2009}, where a standard analysis for linear models is\r\napplied to RNA-Seq (for another related biological example, see \\cite{Pe'er2004} which discusses\r\nidentifiability of haplotypes in mixed populations from genotype data).\r\nThe results in these papers apply to our model. For completeness we review the conditions for\r\nidentifiability. Recall that $A_{R,T}$ is the fragment-transcript matrix that specifies which transcripts each fragment is compatible with. The following theorem provides a simple characterization of identifiability:\r\n\r\n\\begin{thm}\r\nThe RNA-Seq model is identifiable iff $A_{R,T}$ is full rank.\r\n\\end{thm}\r\n\r\nTherefore, for a given set of transcripts and a read set $R$, we can\r\ntest whether the model is identifiable using elementary linear\r\nalgebra. For the results in this paper, when estimating expression\r\nwith given annotations, when the model was not identifiable we picked {\\em a} maximum likelihood solution,\r\nalthough in principle it is possible to bound the total expression of\r\nthe locus and/or report identifiability problems to the user. \r\n\r\nReturning to the likelihood function\r\n\\begin{equation}\r\n\\left( \\prod_{g \\in G} \\beta_g^{X_{g}} \\right) \\left( \\prod_{g \\in G} \\left( \\prod_{r \\in R:r \\in g} \\sum_{t \\in g} \\gamma_t \\cdot\r\n\\frac{F(I_t(r))}{l(t)-I_t(r)+1}\\right) \\right),\r\n\\end{equation}\r\nwe note that both the $\\beta$ and $\\gamma$ parameters depend on the $\\rho$ parameters. However, we will see that if we maximize the $\\beta$ separately from the $\\gamma$, and also each of the sets $\\{\\gamma_t:t \\in g\\}$ separately, then it is always possible to find $\\rho$ that match both the maximal $\\beta$ and $\\gamma$. In other words, \r\nthe problem of finding $\\hat{\\rho}$ is equivalent\r\nto finding $\\hat{\\beta}$ that maximizes \r\n$ \\prod_{g \\in G} \\beta_g^{X_g}$\r\nand separately, for each locus $g$, the $\\hat{\\gamma}_t$ that maximize\r\n\\begin{equation}\r\n\\prod_{r \\in R:r \\in g} \\sum_{t \\in g} \\gamma_t\r\n\\frac{F(I_t(r))}{l(t)-I_t(r)+1}.\r\n\\end{equation}\r\n\r\nWe begin by solving for the $\\hat{\\beta}$ and $\\hat{\\gamma}$ and the variances\r\nof the maximum likelihood estimates, and then explain how these are used to\r\nreport expression levels.\r\n\r\nWe can solve for the $\\hat{\\gamma}$ using the fact that the model is linear.\r\nThat is, the probability of each individual read is linear in the read\r\nabundances $\\gamma_t$. It is a standard result in statistics (see, e.g.,\r\nProposition 1.4 in \\cite{ASCB2005}) that the log likelihood function of a\r\nlinear model is concave. Thus, a hill climbing method can be used to find the\r\n$\\hat{\\gamma}$. We used the EM algorithm for this purpose.\r\n\r\nRather than using the direct ML estimates, we obtained a regularized\r\nestimate by importance sampling from the posterior distribution with a\r\nproposal distribution we explain below. The samples were also used to estimate\r\nvariances for our estimates. \r\n\r\nIt follows from standard MLE asymptotic\r\ntheory that the $\\hat{\\gamma}$ are asymptotically multivariate normal with\r\nvariance-covariance matrix given by the inverse of the observed Fisher\r\ninformation matrix. This matrix is defined as follows:\r\n\r\n\\begin{defn}[Observed Fisher information matrix]\r\nThe observed Fisher information matrix is the negative of the Hessian of the log likelihood function evaluated at the maximum likelihood estimate. That is, for parameters $\\Theta=(\\theta_1,\\ldots,\\theta_n)$, the $n \\times n$ matrix is\r\n\\begin{eqnarray}\r\n\\mathcal{F}_{k,l}(\\hat{\\Theta}) & = & - \\frac{\\partial^2 log(\\mathcal{L}(\\Theta|R))}{\\partial \\theta_k \\theta_l} |_{\\theta=\\hat{\\theta}}.\r\n\\end{eqnarray}\r\n\\end{defn}\r\nIn our case, considering a single locus $g$, the parameters are $\\Theta = (\\gamma_{t_1},\\ldots,\\gamma_{t_{|g|}})$, and as expected from Proposition \\ref{prop:linearmodel}:\r\n\\begin{eqnarray}\r\n\\label{eq:Fisher}\r\n\\mathcal{F}_{t_k,t_l}(\\hat{\\Theta}) & = & \\sum_{r \\in R:r \\in g}\r\n\\left[ \\frac{1}{\\left( \\sum_{h \\in g} \\hat{\\gamma}_h \\frac{F(I_h(r))}{l(h) - I_h(r)+1} \\right)^2} \\frac{F(I_{t_k}(r)) F(I_{t_l}(r)) }{(l(t_k)-I_{t_k}+1)(l(t_l)-I_{t_l}+1)} \\right].\r\n\\end{eqnarray}\r\n\r\n\r\nBecause some of the transcript abundances may be close to zero, we\r\nadopted the\r\nBayesian approach of \\cite{Jiang2009} and instead sampled from the\r\njoint posterior distribution of $\\Theta$ using the proposal\r\ndistribution consisting of the multivariate normal with mean\r\ngiven by the MLE, and variance-covariance matrix given by the inverse of\r\n(\\ref{eq:Fisher}). If the Observed Fisher Information Matrix is\r\nsingular then the user is warned and the confidence intervals of all\r\ntranscripts are set to $[0,1]$ (meaning that there is no information\r\nabout relative abundances).\r\n\r\nThe method used for sampling was importance sampling. The samples were used to obtain a maximum-a-posterior estimate for\r\n$\\hat{\\gamma}_t$ for each $t$ and for the variance-covariance matrix which we\r\ndenote by $\\Psi^g$ (where $g \\in G$ denotes the locus). Note that $\\Psi^g$ is\r\na $|g| \\times |g|$ matrix. The covariance between $\\hat{\\gamma}_{t_k}$ and\r\n$\\hat{\\gamma}_{t_l}$ for $t_k,t_l \\in g$ is given by $\\Psi^g_{t_k,t_l}$.\r\n\r\nTurning to the maximum likelihood estimates $\\hat{\\beta}$, we use the fact that the model is the log-linear. Therefore, \r\n\\begin{equation}\r\n\\label{eq:sigmahat}\r\n\\hat{\\beta_g} = \\frac{X_{g}}{M}.\r\n\\end{equation}\r\n\r\nViewed as a random variable, the counts $X_{g}$ are approximately Poisson and therefore the variance of the MLE $\\hat{\\beta}_g$ is approximately $X_{g}$. We note that for the tests in this paper we directly used the total counts $M$ and the proportional counts $X_g$, however it is easy to incorporate recent suggestions for total count normalization, such as \\cite{Bullard2010} into {\\tt Cufflinks}.\r\n\r\nThe favored units for reporting expression in RNA-Seq studies to date is not\r\nusing the transcript abundances directly, but rather using a measure\r\nabbreviated as FPKM, which means ``expected number of fragments per kilobase\r\nof transcript sequence per millions base pairs sequenced''. These units are\r\nequivalent to measuring transcript abundances (multiplied by a scalar). The\r\ncomputational advantage of FPKM, is that the normalization constants\r\nconveniently simplify some of the formulas for the variances of transcript\r\nabundance estimates.\r\n\r\nFor example, the abundance of a transcript $t \\in g$ in FPKM units is\r\n\\begin{equation}\r\n\\label{eq:FPKM1}\r\n \\frac{10^6 \\cdot 10^3 \\cdot \\alpha_t}{\\tilde{l}(t)} = \\frac{10^6 \\cdot 10^3 \\cdot \\beta_g \\cdot \\gamma_t}{\\tilde{l}(t)}.\r\n\\end{equation}\r\n\r\nEquation (\\ref{eq:FPKM1}) makes it clear that although the abundance of each\r\ntranscript $t \\in g$ in FPKM units is proportional to the transcript abundance\r\n$\\rho_t$ it is given in terms of the read abundances $\\beta_g$ and $\\gamma_t$\r\nwhich are the parameters estimated from the likelihood function.\r\n\r\nThe maximum likelihood estimates of $\\beta_g$ and $\\gamma_t$ are random\r\nvariables, and we denote their scaled product (in FPKM units) by $A_t$. That\r\nis $Pr(A_t = a)$ is the probability that for a random set of fragment alignments\r\nfrom a sequencing experiment, the maximum likelihood estimate of the\r\ntranscript abundance for $t$ in FPKM units is $a$.\r\n\r\nUsing the fact that the expectation of a product of independent random\r\nvariables is the product of the expectations, for a transcript $t \\in g$ we\r\nhave\r\n\\begin{equation}\r\nE[A_t] = \\frac{10^9X_{g}\\hat{\\gamma}_t}{\\tilde{l}(t)M}.\r\n\\end{equation}\r\n\r\nGiven the variance estimates for the $\\hat{\\gamma}_t$ we turn to the problem of estimating $Var[A_t]$ for a transcript $t \\in g$. We use Lemma \\ref{lemma:varproduct} to obtain\r\n\\begin{eqnarray}\r\nVar[A_t]& = & \\left(\\frac{10^9}{\\tilde{l}(t)M}\\right)^2 \\left( \\Psi^g_{t,t}X_{g} + \\Psi^g_{t,t}X^2_{g} + (\\hat{\\gamma}_t)^2 X_{g} \\right)\\\\\r\n& = & X_{g}\\left(\\frac{10^9}{\\tilde{l}(t)M)}\\right)^2 \\left( \\Psi^g_{t,t}(1+X_{g}) + (\\hat{\\gamma}_t)^2\\right).\r\n\\end{eqnarray}\r\n\r\nThis variance calculation can be used to estimate a confidence interval by\r\nutilizing the fact \\cite{Aroian1978} that when the expectation divided by the\r\nstandard deviation of at least one of two random variables is large, their\r\nproduct is approximately normal.\r\n\r\nNext we turn to the problem of estimating expression levels (and variances of\r\nthese estimates) for groups of transcripts. Let $S \\subset T$ be a group of\r\ntranscripts located in a single locus $g$, e.g. a collection of transcripts\r\nsharing a common TSS.\r\n\r\nThe analogy of Equation (\\ref{eq:FPKM1}) for the FPKM of the group is\r\n\\begin{eqnarray}\r\n\\label{eq:grouphard}\r\n& & \\frac{10^6 \\cdot 10^3 \\cdot \\beta_g \\cdot \\left( \\sum_{t \\in S} \\gamma_t\\right)}{\\tilde{l}(S)}\\\\ \r\n& = & 10^6 \\cdot 10^3 \\cdot \\beta_g \\cdot \\sum_{t \\in S} \\frac{\\gamma_t}{\\tilde{l}(t)}. \\label{eq:groupeasy}\r\n\\end{eqnarray}\r\n\r\nAs before, we denote by $B_S$ the random variables for which $Pr(B_S = b)$ is\r\nthe probability that for a random set of fragment alignments from a sequencing\r\nexperiment, the maximum likelihood estimate of the transcript abundance for\r\nall the transcripts in $S$ in FPKM units is $b$. We note that the $B_S$ are\r\nproducts and sums of random variables (Equation (\\ref{eq:groupeasy})). This\r\nmakes Equation (\\ref{eq:groupeasy}) more useful than the\r\nequivalent unsimplified Equation (\\ref{eq:grouphard}), especially because\r\n$\\tilde{l}(S)$ is, in general, a ratio of two random variables.\r\n\r\nWe again use the fact that the expectation of independent random variables is\r\nthe product of the expectation, in addition to the fact that expectation is a\r\nlinear operator to conclude that for a group of transcripts $S$,\r\n\r\n\\begin{equation}\r\n\\label{eq:expectTSS}\r\nE[B_S] = \\frac{10^9 \\cdot X_g \\cdot \\sum_{t \\in S} \\frac{\\hat{\\gamma}_t}{\\tilde{l}(t)}}{M}.\r\n\\end{equation}\r\nIn order to compute the variance of $B_S$, we first note that \r\n\\begin{equation}\r\nVar\\left[\\sum_{t \\in S} \\frac{\\hat{\\gamma}_t}{\\tilde{l}(t)}\\right] = \\sum_{t \\in S}\\frac{1}{\\tilde{l}(t)^2}\\Psi^g_{t,t} + \\sum_{t,u \\in S} \\frac{1}{\\tilde{l}(t)\\tilde{l}(u)} \\Psi^g_{t,u}.\r\n\\end{equation}\r\nTherefore,\r\n\\begin{eqnarray}\r\n& & \\quad Var[B_S] \\quad = \\quad \\nonumber \\\\ \r\n& & X_g\\left(\\frac{10^9}{M}\\right)^2\\left( \\left(1+X_g\\right) \\left(\\sum_{t \\in S}\\frac{1}{\\tilde{l}(t)^2}\\Psi^g_{t,t} + \\sum_{t,u \\in S} \\frac{1}{\\tilde{l}(t)\\tilde{l}(u)} \\Psi^g_{t,u}\\right) + \\left( \\sum_{t \\in S} \\frac{\\hat{\\gamma}_t}{\\tilde{l}(t)} \\right)^2 \\right). \\label{eq:varTSS}\r\n\\end{eqnarray}\r\n\r\nWe can again estimate a confidence interval by utilizing the fact that $B_S$\r\nis approximately normal \\cite{Aroian1978}.\r\n\r\n\\subsection{Transcript assembly}\r\n\\label{sec:assembly}\r\n\\subsubsection{Overview}\r\n\r\n{\\tt Cufflinks} takes as input alignments of RNA-Seq fragments to a reference\r\ngenome and, in the absence of an (optional) user provided annotation, initially assembles transcripts from the alignments. Transcripts in\r\neach of the loci are assembled independently. The assembly algorithm\r\nis designed to aim for the following:\r\n\\begin{enumerate}\r\n\\item Every fragment is consistent with at least one assembled\r\n transcript.\r\n\\item Every transcript is tiled by reads.\r\n\\item The number of transcripts is the smallest required to satisfy requirement (1).\r\n\\item The resulting RNA-Seq models (in the sense of Section \\ref{sec:abundances}) are identifiable.\r\n\\end{enumerate}\r\nIn other words, we seek an assembly that parsimoniously “explains” the fragments from the RNA-Seq experiment; every\r\nfragment in the experiment (except those filtered out during a preliminary\r\nerror-control step) should have come from a {\\tt Cufflinks} transcript, and\r\n{\\tt Cufflinks} should produce as few transcripts as possible with that property.\r\nThus, {\\tt Cufflinks} seeks to optimize the criterion suggested in \\cite{Xing2004},\r\nhowever, unlike the method in that paper, {\\tt Cufflinks} leverages Dilworth's\r\nTheorem \\cite{Dilworth1950} to solve the problem by reducing it\r\nto a matching problem via the equivalence of Dilworth's and K\\\"{o}nig's theorems (Theorem \\ref{thm:dilko} in Appendix A). Our approach to isoform reconstruction is inspired by a similar approach used for haplotype reconstruction from HIV quasi-species \\cite{Eriksson2008}.\r\n\r\n\\subsection{A partial order on fragment alignments}\r\n\r\nThe {\\tt Cufflinks} program loads a set of alignments in SAM format sorted by reference\r\nposition and assembles non-overlapping sets of alignments independently. After\r\nfiltering out any erroneous spliced alignments or reads from incompletely\r\nspliced RNAs, {\\tt Cufflinks} constructs a partial order (Definition \\ref{def:po}), or equivalently a directed acyclic graph (DAG), with\r\none node for each fragment that in turn consists of an aligned pair of mated reads. First, we note that fragment alignments are of two types: those where reads align in their entirety to the genome, and reads which have a split alignment (due to an implied intron). \r\n\r\nIn the case of single reads, the partial order can be simply\r\nconstructed by checking the reads for {\\em compatibility}. Two reads\r\nare {\\em compatible} if their overlap contains the exact same implied\r\nintrons (or none). If two reads are not compatible they are {\\em\r\n incompatible}. The reads can be partially ordered by defining, for\r\ntwo reads $x,y$, that $x\\leq y$ if the starting coordinate of $x$ is\r\nat or before the starting coordinate of $y$, and if they are\r\ncompatible.\r\n\r\nIn the case of paired-end RNA-Seq the situation is more complicated\r\nbecause the unknown sequence between mate pairs. To understand this,\r\nwe first note that pairs of fragments can still be determined to be\r\nincompatible if they cannot have originated from the same\r\ntranscript. As with single reads, this happens when there is\r\ndisagreement on implied introns in the overlap. However compatibility\r\nis more subtle. We would like to define a pair of fragments $x,y$ to\r\nbe compatible if they do not overlap, or if every implied intron in one fragment overlaps an identical implied intron in the other fragment. \r\n\r\nHowever it is important to note that it may be impossible to determine\r\nthe compatibility (as defined above) or incompatibility of a pair of fragments. For example, an unknown region internal to a fragment may overlap two different introns (that are incompatible with each other). The fragment may be compatible with one of the introns (and the fragment from which it originates) in which case it is incompatible with the other. Since the opposite situation is also feasible, compatibility (or incompatibility) cannot be assigned. Fragments for which the compatibility/incompatibility cannot be determined with respect to every other fragment are called {\\em uncertain}. Finally, two fragments are called {\\em nested} if one is contained within the other.\r\n\r\n\r\n\\begin{figure}[h] \r\n \\includegraphics[scale=0.5]{pdfs/compatibility.pdf}\r\n \\caption[Compatibility and incompatibility of\r\n fragments]{Compatibility and incompatibility of\r\n fragments. End-reads are solid lines, unknown sequences within\r\n fragments are shown by dotted lines and implied introns are\r\n dashed lines. The reads in (a) are compatible, whereas the\r\n fragments in (b) are incompatible. The fragments in (c) are\r\n nested. Fragment $x_4$ in (d) is uncertain, because $y_4$ and\r\n $y_5$ are incompatible with each other.}\r\n\\end{figure}\r\n\r\nBefore constructing a partial order, fragments are extended to include\r\ntheir nested fragments and uncertain fragments are discarded. These discarded fragments\r\nare used in the abundance estimation. In theory, this may result in\r\nsuboptimal (i.e. non-minimal assemblies) but we determined empirically\r\nthat after assembly uncertain fragments are almost always consistent\r\nwith one of the transcripts. When they are not, there was no\r\ncompletely tiled transcript that contained them. Thus, we employ a\r\nheuristic that substantially speeds up the program, and that works\r\nin practice.\r\n\r\nA partial order $P$ is then constructed from the remaining fragments\r\nby declaring that $x \\leq y$ whenever the fragment corresponding to\r\n$x$ begins at, or before, the location of the fragment corresponding\r\nto $y$ and $x$ and $y$ are compatible. In what follows we identify $P$\r\nwith its Hasse diagram (or covering relation), equivalently a directed\r\nacyclic graph (DAG) that is the transitive reduction. \r\n\r\n\\begin{prop}\r\n$P$ is a partial order.\r\n\\end{prop}\r\n{\\bf Proof}: The fragments can be totally ordered according to the locations where they begin. It therefore suffices to check that if $x,y,z$ are fragments with $x$ compatible with $y$ and $y$ compatible with $z$ then $x$ is compatible with $z$. Since $x$ is not uncertain, it must be either compatible or incompatible with $z$. The latter case can occur only if $x$ and/or $z$ contain implied introns that overlap and are not identical. Since $y$ is not nested within $z$ and $x$ is not nested within $y$, it must be that $y$ contains an implied intron that is not identical with an implied intron in either $x$ or $z$. Therefore $y$ cannot be compatible with both $x$ and $z$. \\qed\r\n\r\n\\subsection{Assembling a parsimonious set of transcripts}\r\n\r\nIn order to assemble a set of transcripts, {\\tt Cufflinks} finds a\r\n(minimum) partition of $P$ into chains (see Definition\r\n\\ref{def:po}). A partition of $P$ into chains yields an assembly\r\nbecause every chain is a totally ordered set of compatible fragments\r\n$x_1,\\ldots,x_l$ and therefore there is a set of overlapping fragments\r\nthat connects them. By Dilworth's theorem (Theorem \\ref{thm:Dilworth}), the problem of finding a minimum partition $P$ into chains is equivalent to finding a maximum antichain in $P$ (an antichain is a set of mutually incompatible fragments). Subsequently, by Theorem \\ref{thm:dilko}, the problem of finding a maximum antichain in $P$ can be reduced to finding a maximum matching in a certain bipartite graph that emerges naturally in deducing Dilworth's theorem from K\\\"{o}nig's theorem \\ref{thm:konig}. \r\nWe call the key bipartite graph the ``reachability'' graph. It is the transitive closure of the DAG, i.e. it is the graph \r\nwhere each fragment $x$ has nodes $L_x$ and $R_x$ in the left and\r\nright partitions of the reachability graph respectively, and where there is an edge between $L_x$ and $R_y$ when $x \\leq y$ in $P$. The maximum matching problem is a classic problem that admits a polynomial time algorithm. The Hopcroft-Karp algorithm \\cite{Hopcroft1973} has a run time of $O(\\sqrt{V}E)$ where in our case $V$ is the number of fragments and $E$ depends on the extent of overlap, but is bounded by a constant times the coverage depth. We note that our parsimony approach to assembly therefore has a better complexity than the $O(V^3)$ PASA algorithm \\cite{Haas2003}. \r\n\r\nThe minimum cardinality chain decomposition computed using the approach above may not be unique.\r\nFor example, a locus may contain two putative distinct initial exons (defined by overlapping incompatible fragments), and one\r\nof two distinct terminal and a constitutive exon in between that is longer than any\r\nread or insert in the RNA-Seq experiment. In such a case, the parsimonious assembly will consist of two transcripts, but there are four possible solutions that are all minimal. In order to ``phase'' distant exons, we leverage the fact that abundance inhomogeneities can link distant exons via their coverage. We therefore weight the edges of the bipartite reachability graph based on the percent-spliced-in metric introduced by\r\nWang \\emph{et al.} in \\cite{Wang2008}. In our setting, the percent-spliced-in\r\n$\\psi_x$ for an alignment $x$ is computed by counting the alignments\r\noverlapping $x$ in the genome that are compatible with $x$ and dividing by the\r\ntotal number of alignments that overlap $x$, and normalizing for the length of\r\nthe $x$. The cost $C(y,z)$ assigned to an edge between alignments $y$ and $z$ reflects\r\nthe belief that they originate from different transcripts:\r\n\r\n\r\n\\begin{equation}\r\n C(y,z) = -\\log(1 - |\\psi_y - \\psi_z|).\r\n \\label{eq:match_cost} \r\n\\end{equation}\r\n\r\nRather than using the Hopcroft-Karp algorithm, a modified version of the {\\tt\r\nLEMON} \\cite{LEMON} and {\\tt Boost} \\cite{Boost} graph libraries are used to\r\ncompute a {\\em min-cost} maximum cardinality matching on the bipartite compatibility\r\ngraph. Even with the presence of weighted edges, our algorithm is\r\nvery fast. The best known algorithm for weighted matching is $O(V^2logV+VE)$.\r\n\r\nBecause we isolated total RNA, we\r\nexpected that a small fraction of our reads would come from the intronic\r\nregions of incompletely processed primary transcripts. Moreover, transcribed\r\nrepetitive elements and low-complexity sequence result in ``shadow''\r\ntransfrags that we wished to discard as artifacts. Thus, {\\tt Cufflinks}\r\nheuristically identifies artifact transfrags and suppresses them in its\r\noutput. We also filter extremely low-abundance minor isoforms of alternatively\r\nspliced genes, using the model described in Section \\ref {abundances} as a\r\nmeans of reducing the variance of estimates for more abundant transcripts. A\r\ntranscript $x$ meeting any of the following criteria is suppressed:\r\n\r\n\\begin{enumerate}\r\n \\item $x$ aligns to the genome entirely within an intronic region of the alignment for a transcript $y$, and the abundance of $x$ is less than 15\\% of $y$'s abundance.\r\n \\item $x$ is supported by only a single fragment alignment to the genome.\r\n \\item More than 75\\% of the fragment alignments supporting $x$, are mappable to multiple genomic loci.\r\n \\item $x$ is an isoform of an alternatively spliced gene, and has an estimated abundance less than 5\\% of the major isoform of the gene. \r\n\\end{enumerate}\r\n\r\nPrior to transcript assembly, {\\tt Cufflinks} also filters out some of the alignments\r\nfor fragments that are likely to originate from incompletely spliced nuclear\r\nRNA, as these can reduce the accuracy abundance estimates for fully spliced\r\nmRNAs. These filters and the output filters above are detailed in the source\r\nfile \\verb!filters.cpp! of the source code for {\\tt Cufflinks}.\r\n\r\nIn the overview of this Section, we mentioned that our assembly algorithm has the property that the resulting models are identifiable. This is a convenient property that emerges naturally from the parsimony criterion for a ``minimal explanation'' of the fragment alignments. Formally, it is a corollary of Dilworth's theorem:\r\n\r\n\\begin{prop}\r\nThe assembly produced by the {\\tt Cufflinks} algorithm always results in an\r\nidentifiable RNA-Seq model.\r\n\\end{prop}\r\n{\\bf Proof}: By Dilworth's theorem, the minimum chain decomposition (assembly)\r\nwe obtain has the same size as the maximum antichain in the partially\r\nordered set we\r\nconstruct from the reads. An antichain consists of reads that are\r\npairwise incompatible, and therefore those reads must form a\r\npermutation sub-matrix in the fragment-transcript matrix $A_{R,T}$ with columns corresponding to the transcripts in a locus, and with rows corresponding to the fragments in the antichain. The matrix $A_{R,T}$ therefore contains permutation sub-matrices that together span all the columns, and the matrix is full-rank.\r\n\r\n\\subsection{Assessment of assembly quality}\r\n\r\nTo compare {\\tt Cufflinks} transfrags against annotated transcriptomes, and\r\nalso to find transfrags common to multiple assemblies, we developed a tool\r\ncalled {\\tt Cuffcompare} that builds structural equivalence classes of\r\ntranscripts. We ran {\\tt Cuffcompare} on each the assembly from each time point against the\r\ncombined annotated transcriptomes of the {UCSC known genes}, {\\tt Ensembl},\r\nand {\\tt Vega}. Because of the stochastic nature of sequencing, \\emph{ab\r\ninitio} assembly of the same transcript in two different samples may result in\r\ntransfrags of slightly different lengths. A {\\tt Cufflinks} transfrag was\r\nconsidered a complete match when there was a transcript with an identical\r\nchain of introns in the combined annotation.\r\n\r\nWhen no complete match is found between a {\\tt Cufflinks} transfrag and the\r\ntranscripts in the combined annotation, {\\tt Cuffcompare} determines and reports if\r\nanother substantial relationship exists with any of the annotation\r\ntranscripts that can be found in or around the same genomic locus. For\r\nexample, when all the introns of a transfrag match perfectly a part of the\r\nintron chain (sub-chain) of an annotation transcript, a ``containment''\r\nrelationship is reported. For single-exon transfrags, containment is also\r\nreported when the exon appears fully overlapped by any of the exons of an\r\nannotation transcript. If there is no perfect match for the intron chain of a\r\ntransfrag but only some exons overlap and there is at least one intron-exon\r\njunction match, {\\tt Cuffcompare} classifies the transfrag as a putative ``novel''\r\nisoform of an annotated gene. When a transfrag is unspliced (single-exon) and\r\nit overlaps the intronic genomic space of a reference annotation transcript,\r\nthe transfrag is classified as potential pre-mRNA fragment. Finally, when no\r\nother relationship is found between a {\\tt Cufflinks} transfrag and an\r\nannotation transcript, {\\tt Cuffcompare} can check the repeat content of the\r\ntransfrag's genomic region (assuming the soft-masked genomic sequence was also\r\nprovided) and it would classify the transfrag as ``repeat'' if most of its\r\nbases are found to be repeat-masked.\r\n\r\nWhen provided multiple time point assemblies, {\\tt Cuffcompare} matches transcripts\r\n\r\n\\subsubsection{Testing for changes in absolute expression} \r\n\r\nBetween any two consecutive time points, we tested whether a transcript was\r\nsignificantly (after FDR control \\cite{Benjamini1995}) up or down regulated with respect to the\r\nnull hypothesis of no change, with variability in expression due solely to the\r\nuncertainties resulting from our abundance estimation procedure. This was done\r\nusing the following testing procedure for absolute differential expression:\r\n\r\nWe employed the standard method used in microarray-based expression analysis\r\nand proposed for RNA-Seq in \\cite{Bullard2010}, which is to compute the\r\nlogarithm of the ratio of intensities (in our case FPKM), and to then use the\r\ndelta method to estimate the variance of the log odds. We describe this for\r\ntesting differential expression of individual transcripts and also groups of\r\ntranscripts (e.g. grouped by TSS).\r\n\r\nWe recall that the MLE FPKM for a transcript $t$ in a locus $g$ is given by \r\n\\begin{equation}\r\n\\frac{10^9X_g\\hat{\\gamma}_t}{\\tilde{l}(t)M}.\r\n\\end{equation}\r\nGiven two different experiments resulting in $X^a_g,M^a$ and $X^b_g,M^b$ respectively, as well as $\\hat{\\gamma}^a_t$ and $\\hat{\\gamma}^b_t$, we would like to test the significance of departures from unity of the ratio of MLE FPKMS, i.e. \r\n\\begin{eqnarray}\r\n& & \\left(\\frac{10^9X^a_g\\hat{\\gamma}^a_t}{\\tilde{l}(t)M^a}\\right) / \\left(\\frac{10^9X^b_g\\hat{\\gamma}^b_t}{\\tilde{l}(t)M^b}\\right)\\\\\r\n& = & \\frac{X_g^a\\hat{\\gamma}^a_tM^b}{X_g^b \\hat{\\gamma}^b_tM^a}.\r\n\\end{eqnarray}\r\n\r\nThis can be turned into a test statistic that is approximately normal by\r\ntaking the logarithm, and normalizing by the variance. We recall that using\r\nthe delta method, if $X$ is a random variable then $Var[log(X)] \\approx\r\n\\frac{Var[X]}{E[X]^2}$.\r\n\r\nTherefore, our test statistic is \r\n\\begin{equation}\r\n\\frac{log(X^a_g)+log(\\hat{\\gamma}^a_t)+log(M^b)-log(X^b_g)-log(\\hat{\\gamma}^b_t)-log(M^a)}\r\n{ \\sqrt{\\frac{\\left( \\Psi^{g,a}_{t,t}(1+X^a_{g}) + (\\hat{\\gamma}^a_t)^2\\right)}{X^a_g \\left(\\hat{\\gamma}^a_t \\right)^2}+\r\n\\frac{\\left( \\Psi^{g,b}_{t,t}(1+X^b_{g}) + (\\hat{\\gamma}^b_t)^2\\right)}{X^b_{g}\\left( \\hat{\\gamma}^b_t\\right)^2} } }.\r\n\\end{equation}\r\n\r\nIn order to test for differential expression of a group of transcripts, we\r\nreplace the numerator and denominator above by those from Equations\r\n(\\ref{eq:expectTSS}) and (\\ref{eq:varTSS}).\r\n\r\nIt is has been noted that the power of differential expression tests in RNA-Seq depend on the length of the transcripts being tested, because longer transcripts accumulate more reads \\cite{Oshlack2009}. This means that the results we report are biased towards discovering longer differentially expressed transcripts and genes.\r\n\r\n\\subsection{Quantifying transcriptional and post-transcriptional overloading}\r\n\r\nIn order to infer the extent of differential promoter usage, we\r\ndecided to measure changes in relative abundances of primary\r\ntranscripts of single genes. Similarly, we investigated changes in\r\nrelative abundances of transcripts grouped by TSS in order to infer\r\ndifferential splicing. These inferences required two ingredients: \r\n\\begin{enumerate}\r\n\\item A metric on probability distributions (derived from relative\r\n abundances).\r\n\\item A test statistic for assessing significant changes in\r\n differential promoter usage and splicing as measured using the\r\n metric referred to above. \r\n\\end{enumerate}\r\n\r\nIn order to address the first requirement, namely a metric on\r\nprobability distributions, we turned to an entropy-based metric. This\r\nwas motivated by the methods in \\cite{Ritchie2008} where tests for\r\ndifferences in relative isoform abundances were performed to\r\ndistinguish cancer cells from normal cells. We extend\r\nthis approach to be able to test for relative isoform abundance changes among multiple\r\nexperiments in RNA-Seq.\r\n\r\n\\begin{defn}[Entropy]\r\nThe entropy of a discrete probability distribution\r\n$p=(p_1,\\ldots,p_n)$ ($0 \\leq p_i \\leq 1$ and $\\sum_{i=1}^n p_i = 1$) is \r\n\\begin{equation}\r\nH(p) = -\\sum_{i=1}^n p_i log p_i.\r\n\\end{equation}\r\nIf $p_i=0$ for some $i$ the value of $p_i log p_i$ is taken to be $0$.\r\n\\end{defn}\r\n\\begin{defn}[The Jensen-Shannon divergence]\r\nThe Jensen-Shannon divergence of $m$ discrete probability distributions $p^1,\\ldots,p^m$ is defined to be:\r\n\\begin{equation}\r\nJS(p^1,\\ldots,p^m) = H\\left(\\frac{p^1 + \\cdots + p^m}{m}\\right) - \\frac{\\sum_{j=1}^m H(p^j)}{m}.\r\n\\end{equation}\r\n\r\nIn other words, the Jensen-Shannon divergence of a set of probability\r\ndistributions is the entropy of their average minus the average of their\r\nentropies. \\end{defn}\r\n\r\nIn the case where $m=2$, we remark that the Jensen-Shannon divergence can also\r\nbe described in terms of the Kullback-Leibler divergence of two discrete\r\nprobability distributions. If we denote Kullback-Leibler divergence by\r\n\r\n\\begin{equation}\r\nD(p^1\\|p^2) = \\sum_i p^1_i log \\frac{p^1_i}{p^2_i},\r\n\\end{equation}\r\nthen\r\n\\begin{equation}\r\nJS(p^1,p^2) = \\frac{1}{2}D(p^1\\|m)+\\frac{1}{2}D(p^2\\|m)\r\n\\end{equation}\r\nwhere $m=\\frac{1}{2}(p^1+p^2)$. In other words the Jensen-Shannon\r\ndivergence is a symmetrized variant of the Kullback-Leibler divergence.\r\n\r\nThe Jensen-Shannon divergence has a number of useful properties: for\r\nexample it is symmetric and non-negative. However it is {\\em not} a\r\nmetric. The following theorem shows how to construct a metric\r\nfrom the Jensen-Shannon divergence:\r\n\r\n\\begin{thm}[Fuglede and Tops{\\o}e 2004 \\cite{Fuglede2004}]\r\nThe square root of the Jensen-Shannon divergence is a metric.\r\n\\end{thm}\r\n\r\nThe proof of this result is based on a harmonic analysis argument that is the basis for\r\nthe remark in the main paper that ``transcript abundances move in time along a\r\nlogarithmic spiral in Hilbert space''. We therefore call the square root of the Jensen-Shannon divergence the {\\em Jensen-Shannon metric}. We employed this metric in\r\norder to quantify relative changes in expression in (groups of) transcripts.\r\n\r\nIn order to test for significance, we introduce a bit of notation.\r\nSuppose that $S$ is a collection of transcripts (for example, they may\r\n share a common TSS). We define\r\n\\begin{equation}\r\n\\kappa_t = \\frac{ \\frac{\\gamma_t}{\\tilde{l}(t)}}{\\sum_{u \\in S} \\frac{\\gamma_u}{\\tilde{l}(u)}}\r\n\\end{equation}\r\nto be the proportion of transcript $t$ among all the transcripts in a\r\ngroup $S$. We let \r\n$Z=\\sum_{u \\in S} \\hat{\\gamma}_u / \\tilde{l}(u)$ so that $\\hat{\\kappa}_t = \\frac{\\gamma_t}{\\tilde{l}(t)Z}$. We therefore have that\r\n\\begin{eqnarray}\r\n\\label{eq:variancekappa1}\r\nVar[\\hat{\\kappa}_t] & = &\\frac{Var[\\hat{\\gamma}_t]}{\\tilde{l}(t)^2Z^2}, \\\\\r\nCov[\\hat{\\kappa}_t,\\hat{\\kappa}_u] & = & \\frac{Cov[\\hat{\\gamma}_t,\\hat{\\gamma}_u]}{\\tilde{l}(t)\\tilde{l}(u)Z^2}.\\label{eq:variancekappa2}\r\n\\end{eqnarray}\r\n\r\nOur test statistic for divergent relative expression was the\r\nJensen-Shannon metric. The test could be applied to multiple time\r\npoints simultaneously, but we focused on pairwise tests (involving\r\nconsecutive time points). Under the null hypothesis of no change in\r\nrelative expression, the Jensen-Shannon metric should be\r\nzero. We tested for this using a one-sided $t$-test, based on an\r\nasymptotic derivation of the distribution of the Jensen-Shannon metric\r\nunder the null hypothesis. This asymptotic distribution is normal by applying the delta method approximation,\r\nwhich involves computing the linear component of the Taylor expansion of the\r\nvariance of $\\sqrt{JS}$.\r\n\r\nIn order to simplify notation, we let $f(p^1,\\ldots,p^m)$ be the\r\nJensen-Shannon metric for $m$ probability distributions $p^1,\\ldots,p^m$.\r\n\r\n\\begin{lemma}\r\nThe partial derivatives of the Jensen-Shannon metric are give by\r\n\\begin{equation}\r\n\\frac{\\partial f}{\\partial p^k_l} = \\frac{1}{2m\\sqrt{f(p^1,\\ldots,p^m)}} log \\left( \\frac{p^k_l}{\\frac{1}{m} \\sum_{j=1}^m p_l^j} \\right).\r\n\\end{equation}\r\n\\end{lemma}\r\n\r\nLet $\\hat{\\kappa}^1,\\ldots,\\hat{\\kappa}^m$ denote $m$ probability\r\ndistributions on the set of transcripts $S$, for example the MLE for the transcript abundances in a time course. Then from the delta method we\r\nhave that $\\sqrt{JS(\\hat{\\kappa}^1,\\ldots,\\hat{\\kappa}^m)}$ is approximately normally\r\ndistributed with variance given by\r\n\r\n\\begin{equation}\r\nVar[\\sqrt{JS(\\hat{\\kappa}^1,\\ldots,\\hat{\\kappa}^m)}] \\approx (\\bigtriangledown f)^T \\Sigma (\\bigtriangledown f),\r\n\\end{equation}\r\n\r\nwhere $\\Sigma$ is the variance-covariance matrix for the\r\n$\\kappa^1,\\ldots,\\kappa^m$, i.e., it is a block diagonal matrix where the\r\n$i$th block is the variance-covariance matrix for the $\\kappa^i_t$ given by\r\nEquations (\\ref{eq:variancekappa1},\\ref{eq:variancekappa2}).\r\n\r\n\r\nThere are two biologically meaningful groupings of transcripts whose\r\nrelative abundances are interesting to track in a\r\ntime course. Transcripts that share a TSS are likely to be regulated by\r\nthe same promoter, and therefore tracking the change in relative\r\nabundances of groups of transcripts sharing a TSS may reveal how\r\ntranscriptional regulation is affecting expression over\r\ntime. Similarly, transcripts that share a TSS and exhibit changes in\r\nexpression relative to each other are likely to be affected by\r\nsplicing or other post-transcriptional regulation. We therefore\r\ngrouped transcripts by TSS and compared relative abundance changes\r\nwithin and between groups. \r\n\r\nWe define ``overloading'' to be a significant change in relative\r\nabundances for a set of transcripts (as determined by the Jensen-Shannon metric, see below). The term is intended to generalize the simple notion of\r\n``isoform switching'' that is well-defined in the case of two\r\ntranscripts, to multiple transcripts. It is complementary to absolute\r\ndifferential changes in expression: the overall expression of a gene\r\nmay remain constant while individual transcripts change drastically in\r\nrelative abundances resulting in overloading. The term is borrowed from\r\ncomputer science, where in some statically-typed programming\r\nlanguages, a function may be used in multiple, specialized instances\r\nvia ``method overloading''. \r\n\r\nWe tested for overloaded genes by performing a one-sided $t$-test based on the asymptotics of the Jensen-Shannon metric under the null hypothesis of no change in relative abundnaces of isoforms (either grouped by shared TSS for for post-transcriptional overloading, or by comparison of groups of isoforms with shared TSS for transcriptional overloading). Type I errors were controlled with the Benjamini-Hochberg \\cite{Benjamini1995} correction for multiple testing. \r\n A selection of overloaded genes are displayed in Supplemental Figs. \\ref{splice_overloaded} and \\ref{promoter_overloaded}.\r\n\r\n\\newpage\r\n\r\n\\begin{figure}[!ht] \r\n \\includegraphics{pdfs/splice_overloaded}\r\n \\caption[Selected genes with post-transcriptional\r\n overloading]{Selected genes with post-transcriptional\r\n overloading. Trajectories indicate the expression of individual\r\n isoforms in FPKM ($y$ axis) over time in hours ($x$\r\n axis). Dashed isoforms have not been previously\r\n annotated. Isoform trajectories are colored by TSS, so isoforms\r\n with the same color presumably share a common promoter and are\r\n processed from the same primary transcript. It is evident that\r\n total gene expression may remain constant during isoforms\r\n switching (Eya3) while in other cases changes in relative\r\n abundance are accompanied by changes in absolute expression. \r\nThe Jensen-Shannon metric generalizes the notion of ``isoform\r\nswitching'' and is useful in cases with multiple isoforms\r\n(e.g. Ddx17). \r\n \\label{splice_overloaded}}\r\n\\end{figure}\r\n\r\n\\begin{figure}[!ht] \r\n \\includegraphics{pdfs/promoter_overloaded}\r\n \\caption[Selected genes with transcriptional overloading]{Selected\r\n genes with transcriptional overloading. Trajectories indicate\r\n the expression of individual isoforms in FPKM (y axis) over time\r\n in hours (x axis). Dashed isoforms have not been previously\r\n annotated. Isoform trajectories are colored by TSS, so that\r\n isoforms with different colors presumably vary in their promoter\r\n and are processed from different primary transcripts. \\label{promoter_overloaded}}\r\n\\end{figure}\r\n\r\nWe can visualize overloading and expression dynamics with a plot that\r\nsuperimposes transcriptional and post-transcriptional overloading and\r\ngene-level expression over the time course. We refer to these as\r\n``Minard plots'', after Charles Joseph Minard's famous depiction\r\nof the advance and retreat of\r\nNapoleon's armies in the campaign against Russia in 1812\r\n\\cite{Tufte2001}. Minard made use of multiple visual cues to display\r\nnumerous varying quantities in one diagram. An example of a Minard\r\nplot for the gene\r\nMyc is shown in Figure 3c, and others are given in Appendix B. The dotted line indicates gene-level FPKM, with measured\r\nFPKM indicated by black circles. Grey circles indicate the arithmetic mean of\r\ngene-level FPKM between consecutive measured time points, interpolating FPKM\r\nat intermediate time points. The total gene expression overloading is\r\nvisualized as a swatch centered around the interpolated expression curve.\r\nThe width of the swatch encodes the amount of expression overloading between\r\nsuccessive time points. The color of the swatch indicates the relative\r\ncontributions of transcriptional and post-transcriptional expression\r\noverloading.\r\n\r\nSome genes, such as tropomyosin I and II, feature a single primary\r\ntranscript, and so all overloading is by definition post-transcriptional.\r\nOthers, like Fhl3, have two primary transcripts, but only a single isoform\r\narises from each, so all overloading is transcriptional. Genes with multiple\r\nprimary transcripts, one or more of which are alternatively spliced, such as\r\nMyc or RTN4, display both forms.\r\n\r\n\r\n\\subsection{The {\\tt Cufflinks} software}\r\n\r\nThe transcript assembly and abundance estimation algorithms are implemented in\r\nfreely available open source software called {\\tt Cufflinks} that is available\r\nfrom \\newline {\\tt http://cufflinks.cbcb.umd.edu/} \\newline Furthermore\r\nmethods for comparing annotations across time points, and for performing the\r\ndifferential expression, promoter usage and splicing tests are implemented in\r\nthe companion programs {\\tt Cuffdiff} and {\\tt Cuffcompare}. Instructions on\r\nhow to install and run the software are provided on the website.\r\n\r\nThe input to {\\tt Cufflinks} consists of fragment alignments in the SAM format\r\n\\cite{Li2009a}. These may consist of either single fragment alignments, or alignments of mate-pairs (paired-end reads produce better assemblies and more accurate abundance estimates than single reads). {\\tt Cufflinks} will assemble the transcripts using the\r\nalgorithm in Section \\ref{sec:assembly}, and transcript abundances will be\r\nestimated using the model in Section \\ref{sec:abundances}. Transcript\r\ncoordinates and abundances are reported in the Gene Transfer Format (GTF).\r\nUser supplied annotations may be provided to {\\tt Cufflinks} (optional input) in\r\nwhich case they form the basis for the transcript abundance estimation.\r\n\r\nSome of the algorithms here rely on sufficient depth of sequencing in order to\r\nproduce reliable output. {\\tt Cufflinks} determines that depth is sufficient where\r\npossible to check that required assumptions hold. For example, in loci where one\r\nor more isoforms have extremely low relative expression, the observed Fisher\r\nInformation Matrix may not be positive definite after rounding errors. In this\r\ncase, it is not possible to produce a reliable variance-covariance matrix for\r\nisoform fragment abundances. {\\tt Cufflinks} will report a numerical\r\nexception in this and similar cases. When an exception is reported, the\r\nconfidence intervals for the isoforms' abundances will be set from 0\r\nFPKM to the FPKM for the whole gene. If such an exception is generated during\r\na {\\tt Cuffdiff} run, no differential analysis involving the problematic\r\nsample will be performed on that locus. \r\n\\newpage \r\n\\section{Appendix A: Lemmas and Theorems}\r\n\r\nThe following elementary/classical results are required for our methods and we include them so that the supplement is self-contained.\r\n\r\n\\begin{lemma}\r\n\\label{lemma:varsum}\r\nLet $X_1,\\ldots,X_n$ be random variables and $a_1,\\ldots,a_n$ real numbers with $Y=\\sum_{i=1}^na_iX_i$. Then\r\n\\begin{equation}\r\nVar[Y] = \\sum_{i=1}^n a_i^2Var[X_i]+2\\sum_{i