Mercurial > repos > joe-usc > ceas
changeset 5:aa2296827914 draft
planemo upload
| author | joe-usc |
|---|---|
| date | Sun, 20 Nov 2016 22:56:39 -0500 |
| parents | b74697c14e8c |
| children | e281a5491a1f |
| files | ceas-4c3b7130dbbb.zip ceas-example/.ceas_wrapper.sh.swp ceas-example/README.rst ceas-example/ceas_wrapper.sh ceas-example/ceas_wrapper.xml ceas-example/data_manager/data_manager_ceas_fetch_annotations.py ceas-example/data_manager/data_manager_ceas_fetch_annotations.xml ceas-example/data_manager_conf.xml ceas-example/test-data/ceas.loc ceas-example/test-data/ceas_in.bed ceas-example/test-data/ceas_in.bigwig ceas-example/test-data/ceas_in.wig ceas-example/test-data/ceas_out1.log ceas-example/test-data/ceas_out1.log.re_match ceas-example/test-data/ceas_out1.pdf ceas-example/test-data/ceas_out1.xls ceas-example/test-data/ceas_out2.log ceas-example/test-data/ceas_out2.log.re_match ceas-example/test-data/ceas_out2.pdf ceas-example/test-data/ceas_out2.xls ceas-example/test-data/ceas_out3.log ceas-example/test-data/ceas_out3.log.re_match ceas-example/test-data/ceas_out3.pdf ceas-example/test-data/ceas_out3.xls ceas-example/test-data/galGal3.len ceas-example/test-data/galGal3.refGene ceas-example/tool-data/ceas.loc.sample ceas-example/tool_data_table_conf.xml.sample ceas-example/tool_data_table_conf.xml.test ceas-example/tool_dependencies.xml |
| diffstat | 30 files changed, 0 insertions(+), 6823 deletions(-) [+] |
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--- a/ceas-example/README.rst Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,86 +0,0 @@ -CEAS: Cis-regulatory Element Annotation System -============================================== - -Galaxy tool wrapper for the CEAS (Cis-regulatory Element Annotation System), which -can be used to annotate intervals and scores with genome features. - -This tool uses the Cistrome version of the package, which provides two versions of -the core program: in addition to the ``ceas`` program (the same as that available -from the main CEAS website), it also includes the ``ceasBW`` program (which can handle -bigwig input). - -The tool assumes that the ``ceas`` and ``ceasBW`` programs are on the Galaxy user's -path. - -The official CEAS website is at: - -- http://liulab.dfci.harvard.edu/CEAS/index.html - -The Cistrome version can be found via - -- https://bitbucket.org/cistrome/cistrome-applications-harvard/overview - -Automated installation -====================== - -Installation via the Galaxy Tool Shed will take care of installing the tool wrapper -and the CEAS programs, and setting the appropriate environment variables. - -In addition this will also install a data manager which can be used to install -reference GDB data files necessary for the tool. - -Manual Installation -=================== - -There are two files to install: - -- ``ceas_wrapper.xml`` (the Galaxy tool definition) -- ``ceas_wrapper.sh`` (the shell script wrapper) - -The suggested location is in a ``tools/ceas/`` folder. You will then -need to modify the ``tools_conf.xml`` file to tell Galaxy to offer the tool -by adding the line: - - <tool file="ceas/ceasbw_wrapper.xml" /> - -You also need to make a copy of the ``ceas.loc`` file (a sample version is -provided here) which points to the available GDB files for different genomes. - -This file should be placed in the ``tool-data`` directory of your Galaxy -installation. - -Reference Data -============== - -CEAS requires reference data in the form of GDB files (essentially, SQLite database -files) containing the RefSeq genes for the genome in question. - -A limited number of GDB files are available for download from the CEAS website; to -make new ones, see the section "Build a sqlite3 file with a gene annotation table -and genome background annotation for CEAS" in the CEAS manual: - -- http://liulab.dfci.harvard.edu/CEAS/usermanual.html - -History -======= - -========== ====================================================================== -Version Changes ----------- ---------------------------------------------------------------------- -1.0.2-3 - Switch to getting R 3.1.2 and bx-python 0.7.1 dependencies from the - toolshed (rather than installing as part of the tool) -1.0.2-2 - Major updates to fix various bugs, add tests and enable ceasBW to - be used without an existing chromosome sizes file. -1.0.2-1 - Modified to work with Cistrome-version of CEAS (includes additional - 'ceasBW' program which can take bigWig input) -1.0.2-0 - Initial version. -========== ====================================================================== - -Developers -========== - -This tool is developed on the following GitHub repository: -https://github.com/fls-bioinformatics-core/galaxy-tools/tree/master/ceas - -For making the "Galaxy Tool Shed" http://toolshed.g2.bx.psu.edu/ tarball I use -the ``package_ceas.sh`` script.
--- a/ceas-example/ceas_wrapper.sh Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,113 +0,0 @@ -#!/bin/sh -e -# -# Wrapper script to run CEAS as a Galaxy tool -# -# This runs the Cistrome versions of CEAS, which provides two executables: -# - ceas (same as the "official" version) -# - ceasBW (modified version that accepts a bigwig file as input) -# -# Usage: ceas_wrapper.sh $BED_IN $GDB_IN $LOG_OUT $PDF_OUT $XLS_OUT [OPTIONS] -# -# Initialise -CEAS=ceas -# -# Process command line -echo $* -BED_IN=$1 -GDB_IN=$2 -LOG_OUT=$3 -PDF_OUT=$4 -XLS_OUT=$5 -# -# Initialise other variables -EXTRA_BED_IN= -# -# Collect remaining args -OPTIONS= -while [ ! -z "$6" ] ; do - if [ "$6" == "--bigwig" ] ; then - # Bigwig input, need to use 'ceasBW' - CEAS=ceasBW - OPTIONS="$OPTIONS --bigwig" - elif [ "$6" == "--length" ] ; then - # Need a chrom sizes file - chrom_sizes=$7 - if [ ! -f "$chrom_sizes" ] ; then - # If chrom sizes file doesn't already exist then attempt to - # download the data from UCSC - echo "WARNING no file $chrom_sizes" - dbkey=$(echo $(basename $chrom_sizes) | cut -d'.' -f1) - if [ $dbkey == '?' ] ; then - # DBkey not set, this is fatal - echo "ERROR genome build not set, cannot get sizes for '?'" >&2 - echo "Assign a genome build to your input dataset and rerun" >&2 - exit 1 - fi - # Fetch the sizes using fetchChromSizes - echo -n "Attempting to download chromosome sizes for $dbkey..." - chrom_sizes=$(basename $chrom_sizes) - fetchChromSizes $dbkey >$chrom_sizes 2>/dev/null - if [ $? -ne 0 ] ; then - echo "failed" - echo "ERROR unable to fetch data for ${dbkey}" >&2 - echo "Please check the genome build associated with your input dataset" >&2 - echo "or update your Galaxy instance to include an appropriate .len file" >&2 - exit 1 - else - echo "ok" - fi - fi - OPTIONS="$OPTIONS --length $chrom_sizes" - shift - else - OPTIONS="$OPTIONS $6" - fi - shift -done -# -# Convenience variables for local files -base_name="ceas" -log_file=${base_name}.log -pdf_report=${base_name}.pdf -xls_file=${base_name}.xls -# -# Get CEAS version -echo Running $CEAS -$CEAS --version >$log_file 2>/dev/null -# -# Construct and run CEAS command line -ceas_cmd="$CEAS --name $base_name $OPTIONS -g $GDB_IN -b $BED_IN" -echo "Running $ceas_cmd" -$ceas_cmd >>$log_file 2>&1 -status=$? -if [ $status -ne 0 ] ; then - echo "Error: log file tail:" - tail $log_file - echo "ERROR $CEAS exited with non-zero code: $status" >&2 - exit $status -fi -# -# Move outputs to final destination -if [ -e $log_file ] ; then - echo "Moving $log_file to $LOG_OUT" - /bin/mv $log_file $LOG_OUT -else - echo ERROR failed to make log file >&2 - exit 1 -fi -if [ -e $xls_file ] ; then - echo "Moving $xls_file to $XLS_OUT" - /bin/mv $xls_file $XLS_OUT -else - echo ERROR failed to generate XLS file >&2 - exit 1 -fi -if [ -e $pdf_report ] ; then - echo "Moving $pdf_report to $PDF_OUT" - /bin/mv $pdf_report $PDF_OUT -else - echo ERROR failed to generate PDF report >&2 - exit 1 -fi -# -# Done
--- a/ceas-example/ceas_wrapper.xml Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,160 +0,0 @@ -<tool id="ceas" name="CEAS" version="1.0.2-3"> - <description>Annotate intervals and scores with genome features</description> - <requirements> - <requirement type="package" version="1.2.5">python_mysqldb</requirement> - <requirement type="package" version="0.7.1">bx-python</requirement> - <requirement type="package" version="1.0.2.d8c0751">cistrome_ceas</requirement> - <requirement type="package" version="1.0">ucsc_fetchChromSizes</requirement> - <requirement type="package" version="3.1.2">R</requirement> - </requirements> - <version_command>ceas --version 2>&1 | tail -1</version_command> - <command interpreter="bash">ceas_wrapper.sh - $bed_file ${gdb_file.fields.path} - $log_output $pdf_report $xls_output - #if (str($wig_file) != 'None') - #if (str($wig_file.ext) == 'bigwig') - --bigwig $wig_file - --length $GALAXY_DATA_INDEX_DIR/shared/ucsc/chrom/${bed_file.dbkey}.len - #else - --wig $wig_file - #end if - #end if - #if (str($extra_bed_file) != 'None') - --ebed $extra_bed_file - #end if - #if (str($span) and int(str($span)) > 0) - --span $span - #end if - --sizes $sizes_lower,$sizes_middle,$sizes_upper - --bisizes $bisizes_lower,$bisizes_upper - --pf-res $profiling_resolution - --rel-dist $relative_distance</command> - <inputs> - <param name="bed_file" type="data" format="bed" label="BED file with ChIP regions" /> - <param name="wig_file" type="data" format="wig,bigwig" label="WIG or BIGWIG file for wig profiling or genome background annotation" optional="true" - help="If not supplied then only perform ChIP region annotation and gene-centered annotation" /> - <param name="extra_bed_file" type="data" format="bed" label="BED file with extra regions of interest (e.g. non-coding regions)" optional="true" /> - <param name="gdb_file" type="select" label="Gene annotation table"> - <options from_data_table="ceas_annotations"> - </options> - <filter name="dbkey" type="data_meta" ref="bed_file" key="dbkey" column="1" /> - <validator type="no_options" message="No tables are available for the selected input"/> - </param> - <param name="span" type="integer" - label="Span from TSS and TTS in the gene-centered annotation (bp)" - help="ChIP regions within this range from TSS and TTS are considered when calculating the coverage rates of promoter and downstream by ChIP regions (--span)" value="3000" /> - <param name="sizes_lower" type="integer" - label="Lower interval for promoter/downstream sizes for ChIP region annotation (bp)" - value="1000" help=" (--sizes)" /> - <param name="sizes_middle" type="integer" - label="Middle interval for promoter/downstream sizes (bp)" value="2000" - help="Values > 10000bp are automatically fixed to 10000bp (--sizes)" /> - <param name="sizes_upper" type="integer" - label="Upper interval for promoter/downstream sizes (bp)" value="3000" - help="Values > 10000bp are automatically fixed to 10000bp (--sizes)" /> - <param name="bisizes_lower" type="integer" - label="Lower interval for bidirectional-promoter sizes for ChIP region annotation (bp)" value="2500" - help="(--bisizes)" /> - <param name="bisizes_upper" type="integer" - label="Upper interval for bidirectional-promoter sizes (bp)" value="5000" - help="Values > 20000bp are automatically fixed to 20000bp (--bisizes)" /> - <param name="profiling_resolution" type="integer" - label="Wig profiling resolution (bp)" value="50" - help="Warning: a number smaller than the wig interval (resolution) may cause aliasing error (--pf-res)" /> - <param name="relative_distance" type="integer" - label="Relative distance to TSS/TTS in wig profiling (bp)" value="3000" - help="(--rel-dist)" /> - </inputs> - <outputs> - <data name="log_output" format="txt" label="CEAS on ${on_string} (log output)" /> - <data name="pdf_report" format="pdf" label="CEAS on ${on_string} (PDF report)" /> - <data name="xls_output" format="interval" label="CEAS on ${on_string} (XLS output)" /> - </outputs> - <tests> - <test> - <!-- Test with bed input only --> - <param name="bed_file" value="ceas_in.bed" ftype="bed" dbkey="galGal3" /> - <param name="gdb_file" value="galGal3_test" /> - <output name="log_output" file="ceas_out1.log.re_match" compare="re_match"/> - <output name="pdf_report" file="ceas_out1.pdf" /> - <output name="xls_output" file="ceas_out1.xls" /> - </test> - <test> - <!-- Test with bed & very small wig input --> - <param name="bed_file" value="ceas_in.bed" ftype="bed" dbkey="galGal3" /> - <param name="wig_file" value="ceas_in.wig" ftype="wig" /> - <param name="gdb_file" value="galGal3_test" /> - <output name="log_output" file="ceas_out2.log.re_match" compare="re_match"/> - <output name="pdf_report" file="ceas_out2.pdf" /> - <output name="xls_output" file="ceas_out2.xls" /> - </test> - <test> - <!-- Test with bed & bigwig input --> - <param name="bed_file" value="ceas_in.bed" ftype="bed" dbkey="galGal3" /> - <param name="wig_file" value="ceas_in.bigwig" ftype="bigwig" /> - <param name="gdb_file" value="galGal3_test" /> - <output name="log_output" file="ceas_out3.log.re_match" compare="re_match"/> - <output name="pdf_report" file="ceas_out3.pdf" /> - <output name="xls_output" file="ceas_out3.xls" /> - </test> - </tests> - <help> -**What it does** - -CEAS (Cis-regulatory Element Annotation System) is a tool for characterizing genome-wide -protein-DNA interaction patterns from ChIP-chip and ChIP-Seq of both sharp and broad -binding factors. It provides statistics on ChIP enrichment at important genome features -such as specific chromosome, promoters, gene bodies, or exons, and infers genes most -likely to be regulated by a binding factor. - -CEAS also enables biologists to visualize the average ChIP enrichment signals over -specific genomic features, allowing continuous and broad ChIP enrichment to be perceived -which might be too subtle to detect from ChIP peaks alone. - -**Usage** - -CEAS takes the following inputs: - - * BED file with discrete ChIP regions (for example, the 'summits' output from the MACS - peak caller) - * WIG or bigWIG file with a continuous ChIP enrichment signal - * Gene annotation table (provided as reference data) - -Optionally it can also take a BED file describing extra regions of interest (for example -non-coding regions). - -The analysis modules are: - - * **ChIP region annotation:** estimates the relative enrichment level of ChIP regions in - each gene feature with respect to the whole genome. - * **Gene-centered annotation:** identifies genes associated with ChIP regions to infer - the direct regulatory gene targets of the binding factor of interest. - * **Average signal profiling within/near important genomic features:** displays continuous - ChIP enrichment signal within/around important gene features to help visualize the - average binding patterns. - -**Background** - -This tool is compatible with the ceasBW version of CEAS from the Cistrome package -obtained from - -https://bitbucket.org/cistrome/cistrome-applications-harvard/overview - -(commit id d8c0751, datestamp 20140929). The CEAS code is under the -published-packages/CEAS/ subdirectory. - -Cistrome data files and documentation can be found at - -http://liulab.dfci.harvard.edu/CEAS/index.html - -The CEAS user manual is available at http://liulab.dfci.harvard.edu/CEAS/usermanual.html - </help> - <citations> - <!-- - See https://wiki.galaxyproject.org/Admin/Tools/ToolConfigSyntax#A.3Ccitations.3E_tag_set - Can be either DOI or Bibtex - Use http://www.bioinformatics.org/texmed/ to convert PubMed to Bibtex - --> - <citation type="doi">10.1093/bioinformatics/btp479</citation> - </citations> -</tool>
--- a/ceas-example/data_manager/data_manager_ceas_fetch_annotations.py Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,157 +0,0 @@ -#!/usr/bin/env python -# - -import sys -import os -import subprocess -import tempfile -import optparse -import urllib2 -import gzip -import shutil - -# Convenience functions mapping to JSON conversion -# (this idiom borrowed from lib/galaxy/utils/json.py) -import json -to_json_string = json.dumps -from_json_string = json.loads - -# Download file from specified URL and put into local subdir - -if __name__ == '__main__': - #Parse Command Line - parser = optparse.OptionParser() - options,args = parser.parse_args() - print "options: %s" % options - print "args : %s" % args - if len(args) != 2: - p.error("Need to supply JSON file name and description text") - - # Read the JSON supplied from the data manager tool - # Results from this program will be returned via the - # same file - jsonfile = args[0] - params = from_json_string(open(jsonfile).read() ) - print "%s" % params - - # Extract the data from the input JSON - # See https://wiki.galaxyproject.org/Admin/Tools/DataManagers/HowTo/Define?highlight=%28\bAdmin%2FTools%2FDataManagers\b%29 - # for example of JSON - # - # We want the values set in the data manager XML - dbkey = params['param_dict']['dbkey'] - description = args[1].strip() - identifier = params['param_dict']['unique_id'].strip() - # Where to put the output file - # Nb we have to make this ourselves, it doesn't exist by default - target_dir = params['output_data'][0]['extra_files_path'] - os.mkdir(target_dir) - - method = params['param_dict']['reference_source']['reference_source_selector'] - - # Dictionary for returning to data manager - data_manager_dict = {} - data_manager_dict['data_tables'] = dict() - - # Download from URL - if method == 'web': - url = params['param_dict']['reference_source']['annotation_url'] - print "Downloading: %s" % url - annotation_file_name = os.path.basename(url) - annotation_file_path = os.path.join(target_dir,annotation_file_name) - print "Annotation file name: %s" % annotation_file_name - print "Annotation file path: %s" % annotation_file_path - open(annotation_file_path,'wb').write(urllib2.urlopen(url).read()) - if annotation_file_name.endswith('.gz'): - # Uncompress - uncompressed_file = annotation_file_path[:-3] - open(uncompressed_file,'wb').write(gzip.open(annotation_file_path,'rb').read()) - # Remove gzipped file - os.remove(annotation_file_path) - annotation_file_name = os.path.basename(uncompressed_file) - annotation_file_path = uncompressed_file - # Update the identifier and description - if not identifier: - identifier = "%s_ceas_web" % dbkey - if not description: - description = "%s (%s)" % (os.path.splitext(annotation_file_name)[0],dbkey) - # Update the output dictionary - data_manager_dict['data_tables']['ceas_annotations'] = { - 'value': identifier, - 'dbkey': dbkey, - 'name': description, - 'path': annotation_file_name, - } - elif method == 'server': - # Pull in a file from the server - filename = params['param_dict']['reference_source']['annotation_filename'] - create_symlink = params['param_dict']['reference_source']['create_symlink'] - print "Canonical gene list file name: %s" % filename - print "Create symlink: %s" % create_symlink - target_filename = os.path.join(target_dir,os.path.basename(filename)) - if create_symlink == 'copy_file': - shutil.copyfile(filename,target_filename) - else: - os.symlink(filename,target_filename) - # Update the identifier and description - if not identifier: - identifier = "%s_%s" % (dbkey, - os.path.splitext(os.path.basename(filename))[0]) - if not description: - description = "%s: %s" % (dbkey, - os.path.splitext(os.path.basename(filename))[0]) - # Update the output dictionary - data_manager_dict['data_tables']['ceas_annotations'] = { - 'value': identifier, - 'dbkey': dbkey, - 'name': description, - 'path': os.path.basename(filename), - } - elif method == 'from_wig': - # Make a reference file from a wig file - wig_file = params['param_dict']['reference_source']['wig_file'] - gene_annotation = params['param_dict']['reference_source']['gene_annotation'] - target_filename = os.path.join(target_dir,"%s_%s.%s" % (dbkey, - os.path.basename(wig_file), - gene_annotation)) - print "Wig file: %s" % wig_file - print "Gene annotation: %s" % gene_annotation - print "Output file: %s" % os.path.basename(target_filename) - # Make a working directory - working_dir = tempfile.mkdtemp() - # Collect stderr in a file for reporting later - stderr_filen = tempfile.NamedTemporaryFile().name - # Build the command to run - cmd = "build_genomeBG -d %s -g %s -w %s -o %s" % (dbkey, - gene_annotation, - wig_file, - target_filename) - print "Running %s" % cmd - proc = subprocess.Popen(args=cmd,shell=True,cwd=working_dir, - stderr=open(stderr_filen,'wb')) - proc.wait() - # Copy stderr to stdout - with open(stderr_filen,'r') as fp: - sys.stdout.write(fp.read()) - # Update identifier and description - if not identifier: - identifier = "%s_%s_%s" % (dbkey, - gene_annotation, - os.path.basename(wig_file)) - if not description: - description = "%s %s from %s" % (dbkey, - gene_annotation, - os.path.basename(wig_file)) - # Update the output dictionary - data_manager_dict['data_tables']['ceas_annotations'] = { - 'value': identifier, - 'dbkey': dbkey, - 'name': description, - 'path': os.path.basename(target_filename), - } - else: - raise NotImplementedError("Method '%s' not implemented" % method) - - #save info to json file - open(jsonfile,'wb').write(to_json_string(data_manager_dict)) -
--- a/ceas-example/data_manager/data_manager_ceas_fetch_annotations.xml Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,95 +0,0 @@ -<tool id="data_manager_fetch_ceas_annotations" name="Fetch CEAS annotation" version="0.0.1" tool_type="manage_data"> - <requirements> - <requirement type="package" version="1.2.5">python_mysqldb</requirement> - <requirement type="package" version="0.7.1">bx_python</requirement> - <requirement type="package" version="1.0.2.d8c0751">cistrome_ceas</requirement> - </requirements> - <description>Fetch and install annotation databases for CEAS</description> - <command interpreter="python">data_manager_ceas_fetch_annotations.py - "${out_file}" - "${description}"</command> - <inputs> - <param name="dbkey" type="genomebuild" label="DBKEY to assign to data" /> - <param type="text" name="unique_id" label="Internal identifier" - help="Identifier string to associate with the annotation e.g. 'mm9_generic'" /> - <param type="text" name="description" value="" size="50" - label="Description of annotation" - help="Text that will be displayed to the user when selecting which annotation to use" /> - <conditional name="reference_source"> - <param name="reference_source_selector" type="select" label="Choose the source for the reference genome"> - <option value="web" selected="True">CEAS website</option> - <option value="server">File on the server</option> - <option value="from_wig">Generated from wig file</option> - </param> - <when value="web"> - <param type="select" name="annotation_url"> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/hg18.refGene.gz">hg18</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/hg19.refGene.gz">hg19</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/mm8.refGene.gz">mm8</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/mm9.refGene.gz">mm9</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/dm2.refGene.gz">dm2</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/dm3.refGene.gz">dm3</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/ce4.refGene.gz">ce4</option> - <option value="http://liulab.dfci.harvard.edu/CEAS/src/ce6.refGene.gz">ce6</option> - </param> - </when> - <when value="server"> - <param type="text" name="annotation_filename" value="" size="50" - label="Full path to CEAS annotation table file on disk" optional="False" /> - <param type="boolean" name="create_symlink" truevalue="create_symlink" falsevalue="copy_file" label="Create symlink to orignal data instead of copying" checked="False" /> - </when> - <when value="from_wig"> - <param type="data" format="wig" name="wig_file" value="" - label="Wig file to use for the background genome annotation" - help="Will be used as input to build_genomeBG" /> - <param type="select" name="gene_annotation" - label="Gene annotation from UCSC"> - <option value="refGene">refGene</option> - </param> - </when> - </conditional> - </inputs> - <outputs> - <data name="out_file" format="data_manager_json"/> - </outputs> - <tests> - <test> - <!-- No tests defined, yet --> - </test> - </tests> - <help> - -.. class:: infomark - -**What it does** - -Adds a CEAS gene annotation table file to the *ceas_annotations"* data table. The -database file can be downloaded from the CEAS website, imported from a file on the -Galaxy server, or generated from a ``wig`` file in a Galaxy history. - -**Notice:** If you leave the identifier or description blank then they will be -generated automatically. - ------- - -.. class:: infomark - -**Fetching, uploading or creating gene annotation table files** - -The gene annotation table files are SQLite database files which are on the CEAS website: - - - http://liulab.dfci.harvard.edu/CEAS/usermanual.html - -The data manager allows you to fetch a pre-built table from the CEAS website; note -that these are based on the ``RefSeq`` annotation and are only available for a subset -of genome builds. - -To create an annotation table for a different genome build or a different annotation, -use the *Generated from wig* file option. This runs the CEAS utility ``build_genomeBG`` -to make a custom table from a wig file, which represents the genome background (e.g. -a wig file created from the ``control lambda`` bedgraph file output from MACS2). - -Alternatively if you already have an existing custom annotation file then this can be -imported from a path on the Galaxy server. - </help> -</tool>
--- a/ceas-example/data_manager_conf.xml Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,20 +0,0 @@ -<?xml version="1.0"?> -<data_managers> - <data_manager tool_file="data_manager/data_manager_ceas_fetch_annotations.xml" id="ceas_fetch_annotations"> - <data_table name="ceas_annotations"> - <output> - <column name="value" /> - <column name="dbkey" /> - <column name="name" /> - <column name="path" output_ref="out_file" > - <move type="file"> - <source>${path}</source> - <target base="${GALAXY_DATA_MANAGER_DATA_PATH}">ceas/${dbkey}/${path}</target> - </move> - <value_translation>${GALAXY_DATA_MANAGER_DATA_PATH}/ceas/${dbkey}/${path}</value_translation> - <value_translation type="function">abspath</value_translation> - </column> - </output> - </data_table> - </data_manager> -</data_managers>
--- a/ceas-example/test-data/ceas.loc Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1 +0,0 @@ -galGal3_test galGal3 galGal3 ${__HERE__}/galGal3.refGene
--- a/ceas-example/test-data/ceas_in.bed Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1 +0,0 @@ -chr26 4119129 4119130 test_MACS2.1.0_peak_1 2.51561
--- a/ceas-example/test-data/ceas_in.wig Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,4312 +0,0 @@ -track type=wiggle_0 name="test_MACS2.1.0_treat_pileup.bdg" description="test_MACS2.1.0_treat_pileup.bdg" visibility=full -fixedStep chrom=chr26 start=0 step=1000 span=1 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 -0.00000 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--- a/ceas-example/test-data/ceas_out1.log Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,184 +0,0 @@ -ceas -- 0.9.9.7 (package version 1.0.2) -INFO @ Tue, 23 Jun 2015 09:12:22: -# ARGUMENTS: -# name = ceas -# gene annotation table = galGal3.refGene -# BED file = ceas_in.bed -# WIG file = None -# extra BED file = None -# ChIP annotation = On -# gene-centered annotation = On -# average profiling = Off -# dump profiles = Off -# re-annotation for genome background (ChIP region annotation) = False -# promoter sizes (ChIP region annotation) = 1000,2000,3000 bp -# downstream sizes (ChIP region annotation) = 1000,2000,3000 bp -# bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp -# span size (gene-centered annotation) = 3000 bp -INFO @ Tue, 23 Jun 2015 09:12:22: #1 read the gene table... -INFO @ Tue, 23 Jun 2015 09:12:22: #2 read the bed file of ChIP regions... -INFO @ Tue, 23 Jun 2015 09:12:22: #3 perform gene-centered annotation... -INFO @ Tue, 23 Jun 2015 09:12:22: #4 See ceas.xls for gene-centered annotation! -INFO @ Tue, 23 Jun 2015 09:12:22: #5 read the pre-computed genome bg annotation... -INFO @ Tue, 23 Jun 2015 09:12:22: #6 perform ChIP region annotation... -INFO @ Tue, 23 Jun 2015 09:12:22: #7 write a R script of ChIP region annotation... - -R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet" -Copyright (C) 2014 The R Foundation for Statistical Computing -Platform: x86_64-redhat-linux-gnu (64-bit) - -R is free software and comes with ABSOLUTELY NO WARRANTY. -You are welcome to redistribute it under certain conditions. -Type 'license()' or 'licence()' for distribution details. - - Natural language support but running in an English locale - -R is a collaborative project with many contributors. -Type 'contributors()' for more information and -'citation()' on how to cite R or R packages in publications. - -Type 'demo()' for some demos, 'help()' for on-line help, or -'help.start()' for an HTML browser interface to help. -Type 'q()' to quit R. - -> # ARGUMENTS: -> # name = ceas -> # gene annotation table = galGal3.refGene -> # BED file = ceas_in.bed -> # WIG file = None -> # extra BED file = None -> # ChIP annotation = On -> # gene-centered annotation = On -> # average profiling = Off -> # dump profiles = Off -> # re-annotation for genome background (ChIP region annotation) = False -> # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp -> # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp -> # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp -> # span size (gene-centered annotation) = 3000 bp -> pdf("ceas.pdf",height=11.5,width=8.5) -> -> # 09:12:22 Tue, 23 Jun 2015 -> # -> # ChIP annotation -> # -> -> -> # -> # Chromosomal Distribution -> # -> -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> r0<-c(100.0) -> r1<-c(100.0) -> height<-rbind(r0,r1) -> names=c("26") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=TRUE,col=c("#5FA1C1","#EB9D86"),main="Chromosomal Distribution of ChIP Regions",xlab="Percentage %",ylab="Chromosome",border=FALSE,xlim=c(0.000000,183.333333),cex.names=1) -> text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9) -> text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9) -> legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> -> # -> # Promoter,Bipromoter,Downstream, Gene and Regions of interest -> # -> -> par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416) -> r1<-c(0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=1000 bp","<=2000 bp","<=3000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.757585),cex.names=1) -> text(x=mp[1,],y=c(1.8532425688606797, 3.616851183410451, 5.322318854623416),label=c("1.9 %","3.6 %","5.3 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 % -+ (0.981)","0.000 % -+ (0.964)","0.000 % -+ (0.947)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(0.03876062889120376, 0.03876062889120376) -> r1<-c(0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=2500 bp","<=5000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Bidirectional Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,0.071061),cex.names=1) -> text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 % -+ (1.000)","0.000 % -+ (1.000)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683) -> r1<-c(0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=1000 bp","<=2000 bp","<=3000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Downstream",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.131358),cex.names=1) -> text(x=mp[1,],y=c(1.8290171758036773, 3.4690762857627364, 4.980740812519683),label=c("1.8 %","3.5 %","5.0 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 % -+ (0.982)","0.000 % -+ (0.965)","0.000 % -+ (0.950)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054) -> r1<-c(0.0, 0.0, 0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("5'UTR","3'UTR","Coding Exon","Intron","All") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Gene",ylab="Percentage %",border=FALSE,ylim=c(0.000000,43.440571),cex.names=1) -> text(x=mp[1,],y=c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054),label=c("0.2 %","1.4 %","2.4 %","19.7 %","23.7 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 % -+ (0.998)","0.000 % -+ (0.986)","0.000 % -+ (0.976)","0.000 % -+ (0.803)","0.000 % -+ (0.763)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> -> # -> # Distribution of Genome and ChIP regions over cis-regulatory element -> # Note that the x may be modified for better graphics in case a value is too small -> # Thus, look at the labels of the pie chart to get the real percentage values -> # -> -> par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2)) -> x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292) -> pie(x=x,labels=c("1.9 %","1.7 %","1.6 %","1.8 %","1.5 %","1.4 %","0.2 %","1.4 %","2.3 %","19.3 %","67.0 %"),main="Genome",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100) -> x<-c(0.000000,1.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s") -> legend("top",legend=c("Promoter (<=1000 bp): 1.9 %","Promoter (1000-2000 bp): 1.7 %","Promoter (2000-3000 bp): 1.6 %","Downstream (<=1000 bp): 1.8 %","Downstream (1000-2000 bp): 1.5 %","Downstream (2000-3000 bp): 1.4 %","5'UTR: 0.2 %","3'UTR: 1.4 %","Coding exon: 2.3 %","Intron: 19.3 %","Distal intergenic: 67.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n") -> x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000) -> pie(x=x,labels=c("0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","100.0 %"),main="ChIP",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100) -> x<-c(0.000000,1.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s") -> legend("top",legend=c("Promoter (<=1000 bp): 0.000 %","Promoter (1000-2000 bp): 0.000 %","Promoter (2000-3000 bp): 0.000 %","Downstream (<=1000 bp): 0.000 %","Downstream (1000-2000 bp): 0.000 %","Downstream (2000-3000 bp): 0.000 %","5'UTR: 0.000 %","3'UTR: 0.000 %","Coding exon: 0.000 %","Intron: 0.000 %","Distal intergenic: 100.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n") -> -> # -> # ChIP regions over the genome -> # -> -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5)) -> x<-c(0.000000,2.515610) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="Distribution of Peak Heights",xlab="",ylab="",xlim=c(0.000000,2.515610),ylim=c(0.000000,1.000000),frame=FALSE,xaxt="s",yaxt="n",cex=0.9) -> x<-c(0.000000,2.515610,2.515610,0.000000) -> y<-c(0.000000,0.000000,1.000000,1.000000) -> polygon(x,y,col=c("black")) -> x <- c(0.000000,0.169726,0.339451,0.509177,0.678903,0.848628,1.018354,1.188079,1.357805,1.527531,1.697256,1.866982,2.036708,2.206433,2.376159) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.800000) -> lines(x, y,xlim=c(0, 2.51561),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2) -> x<-c(4119129.000000,4119130.000000) -> y<-c(0.855556,1.144444) -> plot(x, y,type="n",main="ChIP Regions (Peaks) over Chromosomes",xlab="Chromosome Size (bp)",ylab="Chromosome",xlim=c(4119129.000000,4119130.000000),ylim=c(0.855556,1.144444),frame=FALSE,xaxt="s",yaxt="n") -> start <- c(4119129) -> end <- c(4119130) -> vals <- c(2.51561) -> vals[vals > 2.51561] <- 2.51561 -> vals[vals < 0] <- 0 -> heights <- 0.288889 * ((vals - 0)/(2.51561 - 0)) + 0.855555555556 -> for (i in 1:length(heights)) { -+ polygon(x=c(start[i], end[i], end[i], start[i]), y=c(0.855555555556, 0.855555555556, heights[i], heights[i]), col=c("#CC0000"), border=c("#CC0000")) -+ } -> mtext("26",side=2,line=0,outer=FALSE,at=1.0) -> dev.off() -null device - 1 -> -INFO @ Tue, 23 Jun 2015 09:12:22: #... cong! See ceas.pdf for the graphical results of CEAS!
--- a/ceas-example/test-data/ceas_out1.log.re_match Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,184 +0,0 @@ -ceas\ \-\-\ 0\.9\.9\.7\ \(package\ version\ 1\.0\.2\) -INFO\ \ \@\ .* -\#\ ARGUMENTS\:\ -\#\ name\ \=\ ceas -\#\ gene\ annotation\ table\ \=\ .*galGal3\.refGene -\#\ BED\ file\ \=\ .* -\#\ WIG\ file\ \=\ None -\#\ extra\ BED\ file\ \=\ None -\#\ ChIP\ annotation\ \=\ On -\#\ gene\-centered\ annotation\ \=\ \ On -\#\ average\ profiling\ \=\ Off -\#\ dump\ profiles\ \=\ Off -\#\ re\-annotation\ for\ genome\ background\ \(ChIP\ region\ annotation\)\ \=\ False -\#\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\#\ downstream\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\#\ bidrectional\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 2500\,5000\ bp -\#\ span\ size\ \(gene\-centered\ annotation\)\ \=\ 3000\ bp\ -INFO\ \ \@\ .*\ \#1\ read\ the\ gene\ table\.\.\.\ -INFO\ \ \@\ .*\ \#2\ read\ the\ bed\ file\ of\ ChIP\ regions\.\.\.\ -INFO\ \ \@\ .*\ \#3\ perform\ gene\-centered\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#4\ See\ ceas\.xls\ for\ gene\-centered\ annotation\!\ -INFO\ \ \@\ .*\ \#5\ read\ the\ pre\-computed\ genome\ bg\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#6\ perform\ ChIP\ region\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#7\ write\ a\ R\ script\ of\ ChIP\ region\ annotation\.\.\.\ - -R\ version\ 3\.1\.2\ \(2014\-10\-31\)\ \-\-\ \"Pumpkin\ Helmet\" -Copyright\ \(C\)\ 2014\ The\ R\ Foundation\ for\ Statistical\ Computing -Platform\:\ .* - -R\ is\ free\ software\ and\ comes\ with\ ABSOLUTELY\ NO\ WARRANTY\. -You\ are\ welcome\ to\ redistribute\ it\ under\ certain\ conditions\. -Type\ \'license\(\)\'\ or\ \'licence\(\)\'\ for\ distribution\ details\. - -\ \ Natural\ language\ support\ but\ running\ in\ an\ English\ locale - -R\ is\ a\ collaborative\ project\ with\ many\ contributors\. -Type\ \'contributors\(\)\'\ for\ more\ information\ and -\'citation\(\)\'\ on\ how\ to\ cite\ R\ or\ R\ packages\ in\ publications\. - -Type\ \'demo\(\)\'\ for\ some\ demos\,\ \'help\(\)\'\ for\ on\-line\ help\,\ or -\'help\.start\(\)\'\ for\ an\ HTML\ browser\ interface\ to\ help\. -Type\ \'q\(\)\'\ to\ quit\ R\. - -\>\ \#\ ARGUMENTS\:\ -\>\ \#\ name\ \=\ ceas -\>\ \#\ gene\ annotation\ table\ \=\ .*galGal3\.refGene -\>\ \#\ BED\ file\ \=\ .* -\>\ \#\ WIG\ file\ \=\ None -\>\ \#\ extra\ BED\ file\ \=\ None -\>\ \#\ ChIP\ annotation\ \=\ On -\>\ \#\ gene\-centered\ annotation\ \=\ \ On -\>\ \#\ average\ profiling\ \=\ Off -\>\ \#\ dump\ profiles\ \=\ Off -\>\ \#\ re\-annotation\ for\ genome\ background\ \(ChIP\ region\ annotation\)\ \=\ False -\>\ \#\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\>\ \#\ downstream\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\>\ \#\ bidrectional\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 2500\,5000\ bp -\>\ \#\ span\ size\ \(gene\-centered\ annotation\)\ \=\ 3000\ bp -\>\ pdf\(\"ceas\.pdf\"\,height\=11\.5\,width\=8\.5\) -\>\ -\>\ \#\ .* -\>\ \#\ -\>\ \#\ ChIP\ annotation -\>\ \#\ -\>\ -\>\ -\>\ \#\ -\>\ \#\ Chromosomal\ Distribution -\>\ \#\ -\>\ -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ r0\<\-c\(100\.0\) -\>\ r1\<\-c\(100\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"26\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=TRUE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Chromosomal\ Distribution\ of\ ChIP\ Regions\"\,xlab\=\"Percentage\ \%\"\,ylab\=\"Chromosome\"\,border\=FALSE\,xlim\=c\(0\.000000\,183\.333333\)\,cex\.names\=1\) -\>\ text\(x\=c\(100\.0\)\,y\=mp\[1\,\]\,label\=c\(\"100\.0\ \%\"\)\,pos\=4\,offset\=0\.2\,cex\=0\.9\) -\>\ text\(x\=c\(100\.0\)\,y\=mp\[2\,\]\,label\=c\(\"100\.0\ \%\ \(\<\=4\.9e\-324\)\"\)\,pos\=4\,offset\=0\.2\,cex\=0\.9\) -\>\ legend\(\"right\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ Promoter\,Bipromoter\,Downstream\,\ Gene\ and\ Regions\ of\ interest -\>\ \#\ -\>\ -\>\ par\(mfrow\=c\(4\,\ 1\)\,mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ r0\<\-c\(1\.8532425688606797\,\ 3\.616851183410451\,\ 5\.322318854623416\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=1000\ bp\"\,\"\<\=2000\ bp\"\,\"\<\=3000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Promoter\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,9\.757585\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(1\.8532425688606797\,\ 3\.616851183410451\,\ 5\.322318854623416\)\,label\=c\(\"1\.9\ \%\"\,\"3\.6\ \%\"\,\"5\.3\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.981\)\"\,\"0\.000\ \% -\+\ \(0\.964\)\"\,\"0\.000\ \% -\+\ \(0\.947\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(0\.03876062889120376\,\ 0\.03876062889120376\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=2500\ bp\"\,\"\<\=5000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Bidirectional\ Promoter\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,0\.071061\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(0\.03876062889120376\,\ 0\.03876062889120376\)\,label\=c\(\"0\.04\ \%\"\,\"0\.04\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(1\.000\)\"\,\"0\.000\ \% -\+\ \(1\.000\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(1\.8290171758036773\,\ 3\.4690762857627364\,\ 4\.980740812519683\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=1000\ bp\"\,\"\<\=2000\ bp\"\,\"\<\=3000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Downstream\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,9\.131358\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(1\.8290171758036773\,\ 3\.4690762857627364\,\ 4\.980740812519683\)\,label\=c\(\"1\.8\ \%\"\,\"3\.5\ \%\"\,\"5\.0\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.982\)\"\,\"0\.000\ \% -\+\ \(0\.965\)\"\,\"0\.000\ \% -\+\ \(0\.950\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(0\.2034933016788197\,\ 1\.3978051793890356\,\ 2\.359553283752029\,\ 19\.734005184234114\,\ 23\.694856949054\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"5\'UTR\"\,\"3\'UTR\"\,\"Coding\ Exon\"\,\"Intron\"\,\"All\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Gene\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,43\.440571\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(0\.2034933016788197\,\ 1\.3978051793890356\,\ 2\.359553283752029\,\ 19\.734005184234114\,\ 23\.694856949054\)\,label\=c\(\"0\.2\ \%\"\,\"1\.4\ \%\"\,\"2\.4\ \%\"\,\"19\.7\ \%\"\,\"23\.7\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.998\)\"\,\"0\.000\ \% -\+\ \(0\.986\)\"\,\"0\.000\ \% -\+\ \(0\.976\)\"\,\"0\.000\ \% -\+\ \(0\.803\)\"\,\"0\.000\ \% -\+\ \(0\.763\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ Distribution\ of\ Genome\ and\ ChIP\ regions\ over\ cis\-regulatory\ element -\>\ \#\ Note\ that\ the\ x\ may\ be\ modified\ for\ better\ graphics\ in\ case\ a\ value\ is\ too\ small -\>\ \#\ Thus\,\ look\ at\ the\ labels\ of\ the\ pie\ chart\ to\ get\ the\ real\ percentage\ values -\>\ \#\ -\>\ -\>\ par\(mfcol\=c\(2\,\ 2\)\,mar\=c\(3\,\ 3\,\ 4\,\ 2\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ x\<\-c\(0\.018532\,0\.017055\,0\.016037\,0\.017830\,0\.015092\,0\.014051\,0\.010000\,0\.013833\,0\.023014\,0\.192592\,0\.670292\) -\>\ pie\(x\=x\,labels\=c\(\"1\.9\ \%\"\,\"1\.7\ \%\"\,\"1\.6\ \%\"\,\"1\.8\ \%\"\,\"1\.5\ \%\"\,\"1\.4\ \%\"\,\"0\.2\ \%\"\,\"1\.4\ \%\"\,\"2\.3\ \%\"\,\"19\.3\ \%\"\,\"67\.0\ \%\"\)\,main\=\"Genome\"\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,clockwise\=TRUE\,border\=FALSE\,radius\=0\.9\,cex\=0\.8\,init\.angle\=90\,density\=100\) -\>\ x\<\-c\(0\.000000\,1\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,frame\=FALSE\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ legend\(\"top\"\,legend\=c\(\"Promoter\ \(\<\=1000\ bp\)\:\ 1\.9\ \%\"\,\"Promoter\ \(1000\-2000\ bp\)\:\ 1\.7\ \%\"\,\"Promoter\ \(2000\-3000\ bp\)\:\ 1\.6\ \%\"\,\"Downstream\ \(\<\=1000\ bp\)\:\ 1\.8\ \%\"\,\"Downstream\ \(1000\-2000\ bp\)\:\ 1\.5\ \%\"\,\"Downstream\ \(2000\-3000\ bp\)\:\ 1\.4\ \%\"\,\"5\'UTR\:\ 0\.2\ \%\"\,\"3\'UTR\:\ 1\.4\ \%\"\,\"Coding\ exon\:\ 2\.3\ \%\"\,\"Intron\:\ 19\.3\ \%\"\,\"Distal\ intergenic\:\ 67\.0\ \%\"\)\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,pch\=15\,bty\=\"n\"\) -\>\ x\<\-c\(0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,1\.000000\) -\>\ pie\(x\=x\,labels\=c\(\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"100\.0\ \%\"\)\,main\=\"ChIP\"\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,clockwise\=TRUE\,border\=FALSE\,radius\=0\.9\,cex\=0\.8\,init\.angle\=90\,density\=100\) -\>\ x\<\-c\(0\.000000\,1\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,frame\=FALSE\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ legend\(\"top\"\,legend\=c\(\"Promoter\ \(\<\=1000\ bp\)\:\ 0\.000\ \%\"\,\"Promoter\ \(1000\-2000\ bp\)\:\ 0\.000\ \%\"\,\"Promoter\ \(2000\-3000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(\<\=1000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(1000\-2000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(2000\-3000\ bp\)\:\ 0\.000\ \%\"\,\"5\'UTR\:\ 0\.000\ \%\"\,\"3\'UTR\:\ 0\.000\ \%\"\,\"Coding\ exon\:\ 0\.000\ \%\"\,\"Intron\:\ 0\.000\ \%\"\,\"Distal\ intergenic\:\ 100\.0\ \%\"\)\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ ChIP\ regions\ over\ the\ genome -\>\ \#\ -\>\ -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ layout\(matrix\(c\(1\,\ 0\,\ 2\,\ 2\)\,\ 2\,\ 2\,\ byrow\ \=\ TRUE\)\,widths\=c\(1\,\ 1\)\,heights\=c\(1\,\ 5\)\) -\>\ x\<\-c\(0\.000000\,2\.515610\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"Distribution\ of\ Peak\ Heights\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(0\.000000\,2\.515610\)\,ylim\=c\(0\.000000\,1\.000000\)\,frame\=FALSE\,xaxt\=\"s\"\,yaxt\=\"n\"\,cex\=0\.9\) -\>\ x\<\-c\(0\.000000\,2\.515610\,2\.515610\,0\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,1\.000000\,1\.000000\) -\>\ polygon\(x\,y\,col\=c\(\"black\"\)\) -\>\ x\ \<\-\ c\(0\.000000\,0\.169726\,0\.339451\,0\.509177\,0\.678903\,0\.848628\,1\.018354\,1\.188079\,1\.357805\,1\.527531\,1\.697256\,1\.866982\,2\.036708\,2\.206433\,2\.376159\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.800000\) -\>\ lines\(x\,\ y\,xlim\=c\(0\,\ 2\.51561\)\,ylim\=c\(0\,\ 1\)\,type\=\"l\"\,col\=c\(\"cyan\"\)\,lwd\=2\) -\>\ x\<\-c\(4119129\.000000\,4119130\.000000\) -\>\ y\<\-c\(0\.855556\,1\.144444\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"ChIP\ Regions\ \(Peaks\)\ over\ Chromosomes\"\,xlab\=\"Chromosome\ Size\ \(bp\)\"\,ylab\=\"Chromosome\"\,xlim\=c\(4119129\.000000\,4119130\.000000\)\,ylim\=c\(0\.855556\,1\.144444\)\,frame\=FALSE\,xaxt\=\"s\"\,yaxt\=\"n\"\) -\>\ start\ \<\-\ c\(4119129\) -\>\ end\ \<\-\ c\(4119130\) -\>\ vals\ \<\-\ c\(2\.51561\) -\>\ vals\[vals\ \>\ 2\.51561\]\ \<\-\ 2\.51561 -\>\ vals\[vals\ \<\ 0\]\ \<\-\ 0 -\>\ heights\ \<\-\ 0\.288889\ \*\ \(\(vals\ \-\ 0\)\/\(2\.51561\ \-\ 0\)\)\ \+\ 0\.855555555556 -\>\ for\ \(i\ in\ 1\:length\(heights\)\)\ \{ -\+\ \ polygon\(x\=c\(start\[i\]\,\ end\[i\]\,\ end\[i\]\,\ start\[i\]\)\,\ y\=c\(0\.855555555556\,\ 0\.855555555556\,\ heights\[i\]\,\ heights\[i\]\)\,\ col\=c\(\"\#CC0000\"\)\,\ border\=c\(\"\#CC0000\"\)\) -\+\ \} -\>\ mtext\(\"26\"\,side\=2\,line\=0\,outer\=FALSE\,at\=1\.0\) -\>\ dev\.off\(\) -null\ device\ -\ \ \ \ \ \ \ \ \ \ 1\ -\>\ -INFO\ \ \@\ .*\ \#\.\.\.\ cong\!\ See\ ceas\.pdf\ for\ the\ graphical\ results\ of\ CEAS\!\
--- a/ceas-example/test-data/ceas_out1.xls Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,133 +0,0 @@ -# RefSeq: RefSeq ID -# chr: chromosome of a RefSeq gene -# txStart: 5' end of a RefSeq gene -# txEnd: 3' end site of a RefSeq gene -# strand: strand of a RefSeq gene -# dist u TSS: Distance to the nearest ChIP region's center upstream of transcription start site (bp) -# dist d TSS: Distance to the nearest ChIP region's center downstream of transcription start site (bp) -# dist u TTS: Distance to the nearest ChIP region's center upstream of transcription end site (bp) -# dist d TTS: Distance to the nearest ChIP region's center downstream of transcription end (bp) -# 3000bp u TSS: Occupancy rate of ChIP region in 3000bp upstream of transcription start site (0.0 - 1.0) -# 3000bp d TSS: Occupancy rate of ChIP region in 3000bp downstream of transcription start site (0.0 - 1.0) -# 1/3 gene: Occupancy rate of ChIP region in 1/3 gene (0.0 - 1.0) -# 2/3 gene: Occupancy rate of ChIP region in 2/3 gene (0.0 - 1.0) -# 3/3 gene: Occupancy rate of ChIP region in 3/3 gene (0.0 - 1.0) -# 3000bp d TTS: Occupancy rate of ChIP region in 3000bp downstream of transcriptino end (0.0 - 1.0) -# exons: Occupancy rate of ChIP regions in exons (0.0-1.0) -# Note that txStart and txEnd indicate 5' and 3' ends of genes whereas TSS and TTS transcription start and end sites in consideration of strand. -#name chr txStart txEnd strand dist u TSS dist d TSS dist u TTS dist d TTS 3000bp u TSS 3000bp d TSS 1/3 gene 2/3 gene 3/3 gene 3000bp d TTS exons -NM_001031576 chr26 19281 27136 + NA 4099848 NA 4091993 0 0 0 0 0 0 0 -NM_204615 chr26 57466 61594 - 4057535 NA 4061663 NA 0 0 0 0 0 0 0 -NM_001005431 chr26 65800 76175 - 4042954 NA 4053329 NA 0 0 0 0 0 0 0 -NM_001145491 chr26 91618 92166 - 4026963 NA 4027511 NA 0 0 0 0 0 0 0 -NM_204398 chr26 93069 97423 + NA 4026060 NA 4021706 0 0 0 0 0 0 0 -NM_001305147 chr26 254661 282571 + NA 3864468 NA 3836558 0 0 0 0 0 0 0 -NM_001305148 chr26 254661 282571 + NA 3864468 NA 3836558 0 0 0 0 0 0 0 -NM_001012868 chr26 350397 355252 - 3763877 NA 3768732 NA 0 0 0 0 0 0 0 -NM_001031030 chr26 479573 493561 + NA 3639556 NA 3625568 0 0 0 0 0 0 0 -NM_001305140 chr26 520705 526012 + NA 3598424 NA 3593117 0 0 0 0 0 0 0 -NM_001031029 chr26 537101 565572 + NA 3582028 NA 3553557 0 0 0 0 0 0 0 -NM_205248 chr26 537250 760479 + NA 3581879 NA 3358650 0 0 0 0 0 0 0 -NM_204727 chr26 662969 683066 - 3436063 NA 3456160 NA 0 0 0 0 0 0 0 -NR_105475 chr26 669012 669122 - 3450007 NA 3450117 NA 0 0 0 0 0 0 0 -NM_205449 chr26 785617 794076 + NA 3333512 NA 3325053 0 0 0 0 0 0 0 -NM_204681 chr26 897458 902049 - 3217080 NA 3221671 NA 0 0 0 0 0 0 0 -NM_001278156 chr26 905313 917094 - 3202035 NA 3213816 NA 0 0 0 0 0 0 0 -NM_204184 chr26 960209 964268 - 3154861 NA 3158920 NA 0 0 0 0 0 0 0 -NM_204316 chr26 974223 991244 + NA 3144906 NA 3127885 0 0 0 0 0 0 0 -NM_001031028 chr26 993815 1003847 - 3115282 NA 3125314 NA 0 0 0 0 0 0 0 -NM_001006392 chr26 1024606 1047756 - 3071373 NA 3094523 NA 0 0 0 0 0 0 0 -NM_001039596 chr26 1073550 1080033 - 3039096 NA 3045579 NA 0 0 0 0 0 0 0 -NM_001031027 chr26 1090460 1096137 + NA 3028669 NA 3022992 0 0 0 0 0 0 0 -NM_001031026 chr26 1096566 1104751 - 3014378 NA 3022563 NA 0 0 0 0 0 0 0 -NM_001030913 chr26 1122296 1132596 + NA 2996833 NA 2986533 0 0 0 0 0 0 0 -NM_001171886 chr26 1220031 1222791 - 2896338 NA 2899098 NA 0 0 0 0 0 0 0 -NM_205054 chr26 1229703 1232833 + NA 2889426 NA 2886296 0 0 0 0 0 0 0 -NM_204462 chr26 1234686 1236536 - 2882593 NA 2884443 NA 0 0 0 0 0 0 0 -NM_204463 chr26 1265724 1267989 - 2851140 NA 2853405 NA 0 0 0 0 0 0 0 -NM_001030378 chr26 1289091 1290386 - 2828743 NA 2830038 NA 0 0 0 0 0 0 0 -NM_001195554 chr26 1382510 1388447 + NA 2736619 NA 2730682 0 0 0 0 0 0 0 -NM_001012548 chr26 1406905 1428443 + NA 2712224 NA 2690686 0 0 0 0 0 0 0 -NR_031486 chr26 1442696 1442779 - 2676350 NA 2676433 NA 0 0 0 0 0 0 0 -NR_031487 chr26 1442896 1442979 - 2676150 NA 2676233 NA 0 0 0 0 0 0 0 -NM_205250 chr26 1472137 1474003 + NA 2646992 NA 2645126 0 0 0 0 0 0 0 -NR_105486 chr26 1566398 1566508 - 2552621 NA 2552731 NA 0 0 0 0 0 0 0 -NM_001160320 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004709 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160324 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004493 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160323 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160322 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160321 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004395 chr26 1776431 1800083 + NA 2342698 NA 2319046 0 0 0 0 0 0 0 -NM_001030914 chr26 1811042 1820368 - 2298761 NA 2308087 NA 0 0 0 0 0 0 0 -NM_204506 chr26 1823407 1843085 - 2276044 NA 2295722 NA 0 0 0 0 0 0 0 -NR_031488 chr26 1925941 1926037 - 2193092 NA 2193188 NA 0 0 0 0 0 0 0 -NM_213581 chr26 2070404 2084478 - 2034651 NA 2048725 NA 0 0 0 0 0 0 0 -NR_035298 chr26 2086590 2086691 - 2032438 NA 2032539 NA 0 0 0 0 0 0 0 -NR_105470 chr26 2094750 2094860 - 2024269 NA 2024379 NA 0 0 0 0 0 0 0 -NM_001030915 chr26 2117140 2128322 - 1990807 NA 2001989 NA 0 0 0 0 0 0 0 -NM_001031498 chr26 2175178 2177159 - 1941970 NA 1943951 NA 0 0 0 0 0 0 0 -NM_001008452 chr26 2305308 2315138 + NA 1813821 NA 1803991 0 0 0 0 0 0 0 -NM_001006322 chr26 2315293 2325130 - 1793999 NA 1803836 NA 0 0 0 0 0 0 0 -NM_001004414 chr26 2373245 2375480 - 1743649 NA 1745884 NA 0 0 0 0 0 0 0 -NM_001044644 chr26 2390696 2401255 - 1717874 NA 1728433 NA 0 0 0 0 0 0 0 -NM_001031499 chr26 2425841 2429413 - 1689716 NA 1693288 NA 0 0 0 0 0 0 0 -NM_001033642 chr26 2445710 2453125 + NA 1673419 NA 1666004 0 0 0 0 0 0 0 -NM_001033643 chr26 2469318 2475028 + NA 1649811 NA 1644101 0 0 0 0 0 0 0 -NM_204664 chr26 2498398 2509349 + NA 1620731 NA 1609780 0 0 0 0 0 0 0 -NR_031489 chr26 2511657 2511746 - 1607383 NA 1607472 NA 0 0 0 0 0 0 0 -NR_031490 chr26 2512568 2512648 - 1606481 NA 1606561 NA 0 0 0 0 0 0 0 -NR_105523 chr26 2669792 2669902 - 1449227 NA 1449337 NA 0 0 0 0 0 0 0 -NR_031491 chr26 2896046 2896142 + NA 1223083 NA 1222987 0 0 0 0 0 0 0 -NM_001190924 chr26 2961382 2962268 + NA 1157747 NA 1156861 0 0 0 0 0 0 0 -NM_001007881 chr26 2999189 3002725 + NA 1119940 NA 1116404 0 0 0 0 0 0 0 -NM_204320 chr26 3006741 3011817 - 1107312 NA 1112388 NA 0 0 0 0 0 0 0 -NM_001030916 chr26 3035271 3039335 - 1079794 NA 1083858 NA 0 0 0 0 0 0 0 -NM_204151 chr26 3047964 3050306 - 1068823 NA 1071165 NA 0 0 0 0 0 0 0 -NM_204326 chr26 3124816 3214381 - 904748 NA 994313 NA 0 0 0 0 0 0 0 -NM_204336 chr26 3320769 3332327 + NA 798360 NA 786802 0 0 0 0 0 0 0 -NM_204622 chr26 3339753 3358863 - 760266 NA 779376 NA 0 0 0 0 0 0 0 -NM_205515 chr26 3359016 3368964 + NA 760113 NA 750165 0 0 0 0 0 0 0 -NM_001012843 chr26 3370712 3377196 + NA 748417 NA 741933 0 0 0 0 0 0 0 -NM_001029849 chr26 3377655 3382628 - 736501 NA 741474 NA 0 0 0 0 0 0 0 -NM_001006323 chr26 3439841 3454783 - 664346 NA 679288 NA 0 0 0 0 0 0 0 -NR_035162 chr26 3455233 3455324 + NA 663896 NA 663805 0 0 0 0 0 0 0 -NM_001012697 chr26 3516478 3545774 + NA 602651 NA 573355 0 0 0 0 0 0 0 -NM_001030917 chr26 3590932 3597509 - 521620 NA 528197 NA 0 0 0 0 0 0 0 -NM_001031500 chr26 3597231 3600802 + NA 521898 NA 518327 0 0 0 0 0 0 0 -NM_001040018 chr26 3629575 3631171 + NA 489554 NA 487958 0 0 0 0 0 0 0 -NM_001257295 chr26 3698350 3701362 - 417767 NA 420779 NA 0 0 0 0 0 0 0 -NM_001257296 chr26 3701377 3715857 - 403272 NA 417752 NA 0 0 0 0 0 0 0 -NM_001012549 chr26 3735643 3742472 - 376657 NA 383486 NA 0 0 0 0 0 0 0 -NM_001030918 chr26 3742618 3760175 - 358954 NA 376511 NA 0 0 0 0 0 0 0 -NM_001006324 chr26 3760758 3765368 - 353761 NA 358371 NA 0 0 0 0 0 0 0 -NM_205063 chr26 3809805 3812700 + NA 309324 NA 306429 0 0 0 0 0 0 0 -NM_001293109 chr26 3859074 3879130 - 239999 NA 260055 NA 0 0 0 0 0 0 0 -NM_001293108 chr26 3859074 3882051 - 237078 NA 260055 NA 0 0 0 0 0 0 0 -NR_102328 chr26 3916006 3918143 - 200986 NA 203123 NA 0 0 0 0 0 0 0 -NM_204728 chr26 3920817 3937442 - 181687 NA 198312 NA 0 0 0 0 0 0 0 -NM_001244905 chr26 4104910 4108376 + NA 14219 NA 10753 0 0 0 0 0 0 0 -NM_001293166 chr26 4138324 4142325 - NA 23196 NA 19195 0 0 0 0 0 0 0 -NM_001030919 chr26 4144091 4175943 + 24962 NA 56814 NA 0 0 0 0 0 0 0 -NM_001257297 chr26 4209891 4216177 + 90762 NA 97048 NA 0 0 0 0 0 0 0 -NM_001257298 chr26 4218028 4238067 + 98899 NA 118938 NA 0 0 0 0 0 0 0 -NM_205490 chr26 4375371 4380959 - NA 261830 NA 256242 0 0 0 0 0 0 0 -NM_001305129 chr26 4391940 4397490 + 272811 NA 278361 NA 0 0 0 0 0 0 0 -NM_001271612 chr26 4433568 4438784 + 314439 NA 319655 NA 0 0 0 0 0 0 0 -NM_001030920 chr26 4498991 4730550 + 379862 NA 611421 NA 0 0 0 0 0 0 0 -NM_001030921 chr26 4541748 4544997 - NA 425868 NA 422619 0 0 0 0 0 0 0 -NM_001006325 chr26 4548211 4559974 + 429082 NA 440845 NA 0 0 0 0 0 0 0 -NM_001037832 chr26 4571684 4576072 - NA 456943 NA 452555 0 0 0 0 0 0 0 -NM_001080870 chr26 4578266 4580646 - NA 461517 NA 459137 0 0 0 0 0 0 0 -NM_001080868 chr26 4578266 4580647 - NA 461518 NA 459137 0 0 0 0 0 0 0 -NM_001030922 chr26 4730394 4744364 - NA 625235 NA 611265 0 0 0 0 0 0 0 -NM_204877 chr26 4751619 4755464 - NA 636335 NA 632490 0 0 0 0 0 0 0 -NM_001302134 chr26 4791084 4792014 + 671955 NA 672885 NA 0 0 0 0 0 0 0 -NM_001006327 chr26 4828534 4833077 - NA 713948 NA 709405 0 0 0 0 0 0 0 -NM_001008453 chr26 4838545 4850970 - NA 731841 NA 719416 0 0 0 0 0 0 0 -NM_001030923 chr26 4876559 4884910 + 757430 NA 765781 NA 0 0 0 0 0 0 0 -NM_204429 chr26 4897094 4901738 - NA 782609 NA 777965 0 0 0 0 0 0 0 -NM_204967 chr26 4946735 4952662 - NA 833533 NA 827606 0 0 0 0 0 0 0 -NM_204473 chr26 4990765 4993729 + 871636 NA 874600 NA 0 0 0 0 0 0 0 -NR_105623 chr26 5087926 5087980 - NA 968851 NA 968797 0 0 0 0 0 0 0
--- a/ceas-example/test-data/ceas_out2.log Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,236 +0,0 @@ -ceas -- 0.9.9.7 (package version 1.0.2) -INFO @ Tue, 23 Jun 2015 11:24:09: -# ARGUMENTS: -# name = ceas -# gene annotation table = galGal3.refGene -# BED file = ceas_in.bed -# WIG file = ceas_in_stp1000.wig -# extra BED file = None -# ChIP annotation = On -# gene-centered annotation = On -# average profiling = On -# dump profiles = Off -# re-annotation for genome background (ChIP region annotation) = False -# promoter sizes (ChIP region annotation) = 1000,2000,3000 bp -# downstream sizes (ChIP region annotation) = 1000,2000,3000 bp -# bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp -# span size (gene-centered annotation) = 3000 bp -# profiling resolution (average profiling) = 50 bp -# relative distance wrt TSS and TTS (average profiling) = 3000 bp -INFO @ Tue, 23 Jun 2015 11:24:09: #1 read the gene table... -INFO @ Tue, 23 Jun 2015 11:24:09: #2 read the bed file of ChIP regions... -INFO @ Tue, 23 Jun 2015 11:24:09: #3 perform gene-centered annotation... -INFO @ Tue, 23 Jun 2015 11:24:09: #4 See ceas.xls for gene-centered annotation! -INFO @ Tue, 23 Jun 2015 11:24:09: #5 read the pre-computed genome bg annotation... -INFO @ Tue, 23 Jun 2015 11:24:09: #6 perform ChIP region annotation... -INFO @ Tue, 23 Jun 2015 11:24:09: #7 write a R script of ChIP region annotation... -INFO @ Tue, 23 Jun 2015 11:24:09: #8-1 run wig profiling of chr26... -INFO @ Tue, 23 Jun 2015 11:24:09: #9 append an R script of wig profiling... - -R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet" -Copyright (C) 2014 The R Foundation for Statistical Computing -Platform: x86_64-redhat-linux-gnu (64-bit) - -R is free software and comes with ABSOLUTELY NO WARRANTY. -You are welcome to redistribute it under certain conditions. -Type 'license()' or 'licence()' for distribution details. - - Natural language support but running in an English locale - -R is a collaborative project with many contributors. -Type 'contributors()' for more information and -'citation()' on how to cite R or R packages in publications. - -Type 'demo()' for some demos, 'help()' for on-line help, or -'help.start()' for an HTML browser interface to help. -Type 'q()' to quit R. - -> # ARGUMENTS: -> # name = ceas -> # gene annotation table = galGal3.refGene -> # BED file = ceas_in.bed -> # WIG file = ceas_in_stp1000.wig -> # extra BED file = None -> # ChIP annotation = On -> # gene-centered annotation = On -> # average profiling = On -> # dump profiles = Off -> # re-annotation for genome background (ChIP region annotation) = False -> # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp -> # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp -> # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp -> # span size (gene-centered annotation) = 3000 bp -> # profiling resolution (average profiling) = 50 bp -> # relative distance wrt TSS and TTS (average profiling) = 3000 bp -> pdf("ceas.pdf",height=11.5,width=8.5) -> -> # 11:24:09 Tue, 23 Jun 2015 -> # -> # ChIP annotation -> # -> -> -> # -> # Chromosomal Distribution -> # -> -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> r0<-c(100.0) -> r1<-c(100.0) -> height<-rbind(r0,r1) -> names=c("26") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=TRUE,col=c("#5FA1C1","#EB9D86"),main="Chromosomal Distribution of ChIP Regions",xlab="Percentage %",ylab="Chromosome",border=FALSE,xlim=c(0.000000,183.333333),cex.names=1) -> text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9) -> text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9) -> legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> -> # -> # Promoter,Bipromoter,Downstream, Gene and Regions of interest -> # -> -> par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416) -> r1<-c(0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=1000 bp","<=2000 bp","<=3000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.757585),cex.names=1) -> text(x=mp[1,],y=c(1.8532425688606797, 3.616851183410451, 5.322318854623416),label=c("1.9 %","3.6 %","5.3 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 % -+ (0.981)","0.000 % -+ (0.964)","0.000 % -+ (0.947)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(0.03876062889120376, 0.03876062889120376) -> r1<-c(0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=2500 bp","<=5000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Bidirectional Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,0.071061),cex.names=1) -> text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 % -+ (1.000)","0.000 % -+ (1.000)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683) -> r1<-c(0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=1000 bp","<=2000 bp","<=3000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Downstream",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.131358),cex.names=1) -> text(x=mp[1,],y=c(1.8290171758036773, 3.4690762857627364, 4.980740812519683),label=c("1.8 %","3.5 %","5.0 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 % -+ (0.982)","0.000 % -+ (0.965)","0.000 % -+ (0.950)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054) -> r1<-c(0.0, 0.0, 0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("5'UTR","3'UTR","Coding Exon","Intron","All") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Gene",ylab="Percentage %",border=FALSE,ylim=c(0.000000,43.440571),cex.names=1) -> text(x=mp[1,],y=c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054),label=c("0.2 %","1.4 %","2.4 %","19.7 %","23.7 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 % -+ (0.998)","0.000 % -+ (0.986)","0.000 % -+ (0.976)","0.000 % -+ (0.803)","0.000 % -+ (0.763)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> -> # -> # Distribution of Genome and ChIP regions over cis-regulatory element -> # Note that the x may be modified for better graphics in case a value is too small -> # Thus, look at the labels of the pie chart to get the real percentage values -> # -> -> par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2)) -> x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292) -> pie(x=x,labels=c("1.9 %","1.7 %","1.6 %","1.8 %","1.5 %","1.4 %","0.2 %","1.4 %","2.3 %","19.3 %","67.0 %"),main="Genome",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100) -> x<-c(0.000000,1.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s") -> legend("top",legend=c("Promoter (<=1000 bp): 1.9 %","Promoter (1000-2000 bp): 1.7 %","Promoter (2000-3000 bp): 1.6 %","Downstream (<=1000 bp): 1.8 %","Downstream (1000-2000 bp): 1.5 %","Downstream (2000-3000 bp): 1.4 %","5'UTR: 0.2 %","3'UTR: 1.4 %","Coding exon: 2.3 %","Intron: 19.3 %","Distal intergenic: 67.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n") -> x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000) -> pie(x=x,labels=c("0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","100.0 %"),main="ChIP",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100) -> x<-c(0.000000,1.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s") -> legend("top",legend=c("Promoter (<=1000 bp): 0.000 %","Promoter (1000-2000 bp): 0.000 %","Promoter (2000-3000 bp): 0.000 %","Downstream (<=1000 bp): 0.000 %","Downstream (1000-2000 bp): 0.000 %","Downstream (2000-3000 bp): 0.000 %","5'UTR: 0.000 %","3'UTR: 0.000 %","Coding exon: 0.000 %","Intron: 0.000 %","Distal intergenic: 100.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n") -> -> # -> # ChIP regions over the genome -> # -> -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5)) -> x<-c(0.000000,0.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="Distribution of Peak Heights",xlab="",ylab="",xlim=c(0.000000,0.000000),ylim=c(0.000000,1.000000),frame=FALSE,xaxt="s",yaxt="n",cex=0.9) -> x<-c(0.000000,0.000000,0.000000,0.000000) -> y<-c(0.000000,0.000000,1.000000,1.000000) -> polygon(x,y,col=c("black")) -> x <- c(0.000000) -> y<-c(0.800000) -> lines(x, y,xlim=c(0, 0.0),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2) -> x<-c(0.000000,4127518.000000) -> y<-c(0.855556,1.144444) -> plot(x, y,type="n",main="ChIP Regions (Peaks) over Chromosomes",xlab="Chromosome Size (bp)",ylab="Chromosome",xlim=c(0.000000,4127518.000000),ylim=c(0.855556,1.144444),frame=FALSE,xaxt="s",yaxt="n") -> start <- c(4119129) -> end <- c(4119130) -> vals <- c(0.0) -> vals[vals > 0.0] <- 0.0 -> vals[vals < 0] <- 0 -> heights <- 0.288889 * ((vals - 0)/(0.0 - 0)) + 0.855555555556 -> for (i in 1:length(heights)) { -+ polygon(x=c(start[i], end[i], end[i], start[i]), y=c(0.855555555556, 0.855555555556, heights[i], heights[i]), col=c("#CC0000"), border=c("#CC0000")) -+ } -> mtext("26",side=2,line=0,outer=FALSE,at=1.0) -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> layout(matrix(c(1, 2, 3, 3, 4, 5), 3, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 1, 1)) -> x<-c(-3000.000000,-2950.000000,-2900.000000,-2850.000000,-2800.000000,-2750.000000,-2700.000000,-2650.000000,-2600.000000,-2550.000000,-2500.000000,-2450.000000,-2400.000000,-2350.000000,-2300.000000,-2250.000000,-2200.000000,-2150.000000,-2100.000000,-2050.000000,-2000.000000,-1950.000000,-1900.000000,-1850.000000,-1800.000000,-1750.000000,-1700.000000,-1650.000000,-1600.000000,-1550.000000,-1500.000000,-1450.000000,-1400.000000,-1350.000000,-1300.000000,-1250.000000,-1200.000000,-1150.000000,-1100.000000,-1050.000000,-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Profile near TSS",xlab="Relative Distance to TSS (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2) -> abline(v=0.000000,lty=2,col=c("black")) -> x<-c(-3000.000000,-2950.000000,-2900.000000,-2850.000000,-2800.000000,-2750.000000,-2700.000000,-2650.000000,-2600.000000,-2550.000000,-2500.000000,-2450.000000,-2400.000000,-2350.000000,-2300.000000,-2250.000000,-2200.000000,-2150.000000,-2100.000000,-2050.000000,-2000.000000,-1950.000000,-1900.000000,-1850.000000,-1800.000000,-1750.000000,-1700.000000,-1650.000000,-1600.000000,-1550.000000,-1500.000000,-1450.000000,-1400.000000,-1350.000000,-1300.000000,-1250.000000,-1200.000000,-1150.000000,-1100.000000,-1050.000000,-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000) -> plot(x, y,type="l",main="Average Profile near TTS",xlab="Relative Distance to TTS (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2) -> abline(v=0.000000,lty=2,col=c("black")) -> x<-c(-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000,3050.000000,3100.000000,3150.000000,3200.000000,3250.000000,3300.000000,3350.000000,3400.000000,3450.000000,3500.000000,3550.000000,3600.000000,3650.000000,3700.000000,3750.000000,3800.000000,3850.000000,3900.000000,3950.000000,4000.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,238.095238,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,217.391304,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Gene Profile",xlab="Upstream (bp), 3000 bp of Meta-gene, Downstream (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2) -> abline(v=0.000000,lty=2,col=c("black")) -> abline(v=3000.000000,lty=2,col=c("black")) -> x<-c(0.000000,3.333333,6.666667,10.000000,13.333333,16.666667,20.000000,23.333333,26.666667,30.000000,33.333333,36.666667,40.000000,43.333333,46.666667,50.000000,53.333333,56.666667,60.000000,63.333333,66.666667,70.000000,73.333333,76.666667,80.000000,83.333333,86.666667,90.000000,93.333333,96.666667,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,250.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Concatenated Exon Profile",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,250.000000),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,3.333333,6.666667,10.000000,13.333333,16.666667,20.000000,23.333333,26.666667,30.000000,33.333333,36.666667,40.000000,43.333333,46.666667,50.000000,53.333333,56.666667,60.000000,63.333333,66.666667,70.000000,73.333333,76.666667,80.000000,83.333333,86.666667,90.000000,93.333333,96.666667,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Concatenated Intron Profile",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,250.000000),xaxt="s",yaxt="s",lwd=2) -> par(mfrow=c(3, 2),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> x<-c(0.000000,50.000000,100.000000) -> y<-c(0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Exon Profile -+ (56 <= length < 109 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,132.596685),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,25.000000,50.000000,75.000000,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Intron Profile -+ (110 <= length < 345 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,132.596685),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,50.000000,100.000000) -> y<-c(0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Exon Profile -+ (109 <= length < 160 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,132.596685),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,11.111111,22.222222,33.333333,44.444444,55.555556,66.666667,77.777778,88.888889,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Intron Profile -+ (344 <= length < 686 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,132.596685),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,33.333333,66.666667,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Exon Profile -+ (160 <= length < 375 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,132.596685),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,4.761905,9.523810,14.285714,19.047619,23.809524,28.571429,33.333333,38.095238,42.857143,47.619048,52.380952,57.142857,61.904762,66.666667,71.428571,76.190476,80.952381,85.714286,90.476190,95.238095,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,110.497238,0.000000) -> plot(x, y,type="l",main="Average Intron Profile -+ (685 <= length < 2653 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,132.596685),xaxt="s",yaxt="s",lwd=2) -> dev.off() -null device - 1 -> -INFO @ Tue, 23 Jun 2015 11:24:09: #... cong! See ceas.pdf for the graphical results of CEAS!
--- a/ceas-example/test-data/ceas_out2.log.re_match Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,236 +0,0 @@ -ceas\ \-\-\ 0\.9\.9\.7\ \(package\ version\ 1\.0\.2\) -INFO\ \ \@\ .* -\#\ ARGUMENTS\:\ -\#\ name\ \=\ ceas -\#\ gene\ annotation\ table\ \=\ .*galGal3\.refGene -\#\ BED\ file\ \=\ .* -\#\ WIG\ file\ \=\ .* -\#\ extra\ BED\ file\ \=\ None -\#\ ChIP\ annotation\ \=\ On -\#\ gene\-centered\ annotation\ \=\ \ On -\#\ average\ profiling\ \=\ On -\#\ dump\ profiles\ \=\ Off -\#\ re\-annotation\ for\ genome\ background\ \(ChIP\ region\ annotation\)\ \=\ False -\#\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\#\ downstream\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\#\ bidrectional\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 2500\,5000\ bp -\#\ span\ size\ \(gene\-centered\ annotation\)\ \=\ 3000\ bp -\#\ profiling\ resolution\ \(average\ profiling\)\ \=\ 50\ bp -\#\ relative\ distance\ wrt\ TSS\ and\ TTS\ \(average\ profiling\)\ \=\ 3000\ bp\ -INFO\ \ \@\ .*\ \#1\ read\ the\ gene\ table\.\.\.\ -INFO\ \ \@\ .*\ \#2\ read\ the\ bed\ file\ of\ ChIP\ regions\.\.\.\ -INFO\ \ \@\ .*\ \#3\ perform\ gene\-centered\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#4\ See\ ceas\.xls\ for\ gene\-centered\ annotation\!\ -INFO\ \ \@\ .*\ \#5\ read\ the\ pre\-computed\ genome\ bg\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#6\ perform\ ChIP\ region\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#7\ write\ a\ R\ script\ of\ ChIP\ region\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#8\-1\ run\ wig\ profiling\ of\ chr26\.\.\.\ -INFO\ \ \@\ .*\ \#9\ append\ an\ R\ script\ of\ wig\ profiling\.\.\.\ - -R\ version\ 3\.1\.2\ \(2014\-10\-31\)\ \-\-\ \"Pumpkin\ Helmet\" -Copyright\ \(C\)\ 2014\ The\ R\ Foundation\ for\ Statistical\ Computing -Platform\:\ .* - -R\ is\ free\ software\ and\ comes\ with\ ABSOLUTELY\ NO\ WARRANTY\. -You\ are\ welcome\ to\ redistribute\ it\ under\ certain\ conditions\. -Type\ \'license\(\)\'\ or\ \'licence\(\)\'\ for\ distribution\ details\. - -\ \ Natural\ language\ support\ but\ running\ in\ an\ English\ locale - -R\ is\ a\ collaborative\ project\ with\ many\ contributors\. -Type\ \'contributors\(\)\'\ for\ more\ information\ and -\'citation\(\)\'\ on\ how\ to\ cite\ R\ or\ R\ packages\ in\ publications\. - -Type\ \'demo\(\)\'\ for\ some\ demos\,\ \'help\(\)\'\ for\ on\-line\ help\,\ or -\'help\.start\(\)\'\ for\ an\ HTML\ browser\ interface\ to\ help\. -Type\ \'q\(\)\'\ to\ quit\ R\. - -\>\ \#\ ARGUMENTS\:\ -\>\ \#\ name\ \=\ ceas -\>\ \#\ gene\ annotation\ table\ \=\ .*galGal3\.refGene -\>\ \#\ BED\ file\ \=\ .* -\>\ \#\ WIG\ file\ \=\ .* -\>\ \#\ extra\ BED\ file\ \=\ None -\>\ \#\ ChIP\ annotation\ \=\ On -\>\ \#\ gene\-centered\ annotation\ \=\ \ On -\>\ \#\ average\ profiling\ \=\ On -\>\ \#\ dump\ profiles\ \=\ Off -\>\ \#\ re\-annotation\ for\ genome\ background\ \(ChIP\ region\ annotation\)\ \=\ False -\>\ \#\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\>\ \#\ downstream\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\>\ \#\ bidrectional\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 2500\,5000\ bp -\>\ \#\ span\ size\ \(gene\-centered\ annotation\)\ \=\ 3000\ bp -\>\ \#\ profiling\ resolution\ \(average\ profiling\)\ \=\ 50\ bp -\>\ \#\ relative\ distance\ wrt\ TSS\ and\ TTS\ \(average\ profiling\)\ \=\ 3000\ bp -\>\ pdf\(\"ceas\.pdf\"\,height\=11\.5\,width\=8\.5\) -\>\ -\>\ \#\ .* -\>\ \#\ -\>\ \#\ ChIP\ annotation -\>\ \#\ -\>\ -\>\ -\>\ \#\ -\>\ \#\ Chromosomal\ Distribution -\>\ \#\ -\>\ -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ r0\<\-c\(100\.0\) -\>\ r1\<\-c\(100\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"26\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=TRUE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Chromosomal\ Distribution\ of\ ChIP\ Regions\"\,xlab\=\"Percentage\ \%\"\,ylab\=\"Chromosome\"\,border\=FALSE\,xlim\=c\(0\.000000\,183\.333333\)\,cex\.names\=1\) -\>\ text\(x\=c\(100\.0\)\,y\=mp\[1\,\]\,label\=c\(\"100\.0\ \%\"\)\,pos\=4\,offset\=0\.2\,cex\=0\.9\) -\>\ text\(x\=c\(100\.0\)\,y\=mp\[2\,\]\,label\=c\(\"100\.0\ \%\ \(\<\=4\.9e\-324\)\"\)\,pos\=4\,offset\=0\.2\,cex\=0\.9\) -\>\ legend\(\"right\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ Promoter\,Bipromoter\,Downstream\,\ Gene\ and\ Regions\ of\ interest -\>\ \#\ -\>\ -\>\ par\(mfrow\=c\(4\,\ 1\)\,mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ r0\<\-c\(1\.8532425688606797\,\ 3\.616851183410451\,\ 5\.322318854623416\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=1000\ bp\"\,\"\<\=2000\ bp\"\,\"\<\=3000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Promoter\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,9\.757585\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(1\.8532425688606797\,\ 3\.616851183410451\,\ 5\.322318854623416\)\,label\=c\(\"1\.9\ \%\"\,\"3\.6\ \%\"\,\"5\.3\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.981\)\"\,\"0\.000\ \% -\+\ \(0\.964\)\"\,\"0\.000\ \% -\+\ \(0\.947\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(0\.03876062889120376\,\ 0\.03876062889120376\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=2500\ bp\"\,\"\<\=5000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Bidirectional\ Promoter\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,0\.071061\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(0\.03876062889120376\,\ 0\.03876062889120376\)\,label\=c\(\"0\.04\ \%\"\,\"0\.04\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(1\.000\)\"\,\"0\.000\ \% -\+\ \(1\.000\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(1\.8290171758036773\,\ 3\.4690762857627364\,\ 4\.980740812519683\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=1000\ bp\"\,\"\<\=2000\ bp\"\,\"\<\=3000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Downstream\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,9\.131358\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(1\.8290171758036773\,\ 3\.4690762857627364\,\ 4\.980740812519683\)\,label\=c\(\"1\.8\ \%\"\,\"3\.5\ \%\"\,\"5\.0\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.982\)\"\,\"0\.000\ \% -\+\ \(0\.965\)\"\,\"0\.000\ \% -\+\ \(0\.950\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(0\.2034933016788197\,\ 1\.3978051793890356\,\ 2\.359553283752029\,\ 19\.734005184234114\,\ 23\.694856949054\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"5\'UTR\"\,\"3\'UTR\"\,\"Coding\ Exon\"\,\"Intron\"\,\"All\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Gene\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,43\.440571\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(0\.2034933016788197\,\ 1\.3978051793890356\,\ 2\.359553283752029\,\ 19\.734005184234114\,\ 23\.694856949054\)\,label\=c\(\"0\.2\ \%\"\,\"1\.4\ \%\"\,\"2\.4\ \%\"\,\"19\.7\ \%\"\,\"23\.7\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.998\)\"\,\"0\.000\ \% -\+\ \(0\.986\)\"\,\"0\.000\ \% -\+\ \(0\.976\)\"\,\"0\.000\ \% -\+\ \(0\.803\)\"\,\"0\.000\ \% -\+\ \(0\.763\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ Distribution\ of\ Genome\ and\ ChIP\ regions\ over\ cis\-regulatory\ element -\>\ \#\ Note\ that\ the\ x\ may\ be\ modified\ for\ better\ graphics\ in\ case\ a\ value\ is\ too\ small -\>\ \#\ Thus\,\ look\ at\ the\ labels\ of\ the\ pie\ chart\ to\ get\ the\ real\ percentage\ values -\>\ \#\ -\>\ -\>\ par\(mfcol\=c\(2\,\ 2\)\,mar\=c\(3\,\ 3\,\ 4\,\ 2\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ x\<\-c\(0\.018532\,0\.017055\,0\.016037\,0\.017830\,0\.015092\,0\.014051\,0\.010000\,0\.013833\,0\.023014\,0\.192592\,0\.670292\) -\>\ pie\(x\=x\,labels\=c\(\"1\.9\ \%\"\,\"1\.7\ \%\"\,\"1\.6\ \%\"\,\"1\.8\ \%\"\,\"1\.5\ \%\"\,\"1\.4\ \%\"\,\"0\.2\ \%\"\,\"1\.4\ \%\"\,\"2\.3\ \%\"\,\"19\.3\ \%\"\,\"67\.0\ \%\"\)\,main\=\"Genome\"\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,clockwise\=TRUE\,border\=FALSE\,radius\=0\.9\,cex\=0\.8\,init\.angle\=90\,density\=100\) -\>\ x\<\-c\(0\.000000\,1\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,frame\=FALSE\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ legend\(\"top\"\,legend\=c\(\"Promoter\ \(\<\=1000\ bp\)\:\ 1\.9\ \%\"\,\"Promoter\ \(1000\-2000\ bp\)\:\ 1\.7\ \%\"\,\"Promoter\ \(2000\-3000\ bp\)\:\ 1\.6\ \%\"\,\"Downstream\ \(\<\=1000\ bp\)\:\ 1\.8\ \%\"\,\"Downstream\ \(1000\-2000\ bp\)\:\ 1\.5\ \%\"\,\"Downstream\ \(2000\-3000\ bp\)\:\ 1\.4\ \%\"\,\"5\'UTR\:\ 0\.2\ \%\"\,\"3\'UTR\:\ 1\.4\ \%\"\,\"Coding\ exon\:\ 2\.3\ \%\"\,\"Intron\:\ 19\.3\ \%\"\,\"Distal\ intergenic\:\ 67\.0\ \%\"\)\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,pch\=15\,bty\=\"n\"\) -\>\ x\<\-c\(0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,1\.000000\) -\>\ pie\(x\=x\,labels\=c\(\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"100\.0\ \%\"\)\,main\=\"ChIP\"\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,clockwise\=TRUE\,border\=FALSE\,radius\=0\.9\,cex\=0\.8\,init\.angle\=90\,density\=100\) -\>\ x\<\-c\(0\.000000\,1\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,frame\=FALSE\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ legend\(\"top\"\,legend\=c\(\"Promoter\ \(\<\=1000\ bp\)\:\ 0\.000\ \%\"\,\"Promoter\ \(1000\-2000\ bp\)\:\ 0\.000\ \%\"\,\"Promoter\ \(2000\-3000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(\<\=1000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(1000\-2000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(2000\-3000\ bp\)\:\ 0\.000\ \%\"\,\"5\'UTR\:\ 0\.000\ \%\"\,\"3\'UTR\:\ 0\.000\ \%\"\,\"Coding\ exon\:\ 0\.000\ \%\"\,\"Intron\:\ 0\.000\ \%\"\,\"Distal\ intergenic\:\ 100\.0\ \%\"\)\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ ChIP\ regions\ over\ the\ genome -\>\ \#\ -\>\ -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ layout\(matrix\(c\(1\,\ 0\,\ 2\,\ 2\)\,\ 2\,\ 2\,\ byrow\ \=\ TRUE\)\,widths\=c\(1\,\ 1\)\,heights\=c\(1\,\ 5\)\) -\>\ x\<\-c\(0\.000000\,0\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"Distribution\ of\ Peak\ Heights\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(0\.000000\,0\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,frame\=FALSE\,xaxt\=\"s\"\,yaxt\=\"n\"\,cex\=0\.9\) -\>\ x\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,1\.000000\,1\.000000\) -\>\ polygon\(x\,y\,col\=c\(\"black\"\)\) -\>\ x\ \<\-\ c\(0\.000000\) -\>\ y\<\-c\(0\.800000\) -\>\ lines\(x\,\ y\,xlim\=c\(0\,\ 0\.0\)\,ylim\=c\(0\,\ 1\)\,type\=\"l\"\,col\=c\(\"cyan\"\)\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,4127518\.000000\) -\>\ y\<\-c\(0\.855556\,1\.144444\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"ChIP\ Regions\ \(Peaks\)\ over\ Chromosomes\"\,xlab\=\"Chromosome\ Size\ \(bp\)\"\,ylab\=\"Chromosome\"\,xlim\=c\(0\.000000\,4127518\.000000\)\,ylim\=c\(0\.855556\,1\.144444\)\,frame\=FALSE\,xaxt\=\"s\"\,yaxt\=\"n\"\) -\>\ start\ \<\-\ c\(4119129\) -\>\ end\ \<\-\ c\(4119130\) -\>\ vals\ \<\-\ c\(0\.0\) -\>\ vals\[vals\ \>\ 0\.0\]\ \<\-\ 0\.0 -\>\ vals\[vals\ \<\ 0\]\ \<\-\ 0 -\>\ heights\ \<\-\ 0\.288889\ \*\ \(\(vals\ \-\ 0\)\/\(0\.0\ \-\ 0\)\)\ \+\ 0\.855555555556 -\>\ for\ \(i\ in\ 1\:length\(heights\)\)\ \{ -\+\ \ polygon\(x\=c\(start\[i\]\,\ end\[i\]\,\ end\[i\]\,\ start\[i\]\)\,\ y\=c\(0\.855555555556\,\ 0\.855555555556\,\ heights\[i\]\,\ heights\[i\]\)\,\ col\=c\(\"\#CC0000\"\)\,\ border\=c\(\"\#CC0000\"\)\) -\+\ \} -\>\ mtext\(\"26\"\,side\=2\,line\=0\,outer\=FALSE\,at\=1\.0\) -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ layout\(matrix\(c\(1\,\ 2\,\ 3\,\ 3\,\ 4\,\ 5\)\,\ 3\,\ 2\,\ byrow\ \=\ TRUE\)\,widths\=c\(1\,\ 1\)\,heights\=c\(1\,\ 1\,\ 1\)\) -\>\ 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y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Profile\ near\ TSS\"\,xlab\=\"Relative\ Distance\ to\ TSS\ \(bp\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ abline\(v\=0\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ x\<\-c\(\-3000\.000000\,\-2950\.000000\,\-2900\.000000\,\-2850\.000000\,\-2800\.000000\,\-2750\.000000\,\-2700\.000000\,\-2650\.000000\,\-2600\.000000\,\-2550\.000000\,\-2500\.000000\,\-2450\.000000\,\-2400\.000000\,\-2350\.000000\,\-2300\.000000\,\-2250\.000000\,\-2200\.000000\,\-2150\.000000\,\-2100\.000000\,\-2050\.000000\,\-2000\.000000\,\-1950\.000000\,\-1900\.000000\,\-1850\.000000\,\-1800\.000000\,\-1750\.000000\,\-1700\.000000\,\-1650\.000000\,\-1600\.000000\,\-1550\.000000\,\-1500\.000000\,\-1450\.000000\,\-1400\.000000\,\-1350\.000000\,\-1300\.000000\,\-1250\.000000\,\-1200\.000000\,\-1150\.000000\,\-1100\.000000\,\-1050\.000000\,\-1000\.000000\,\-950\.000000\,\-900\.000000\,\-850\.000000\,\-800\.000000\,\-750\.000000\,\-700\.000000\,\-650\.000000\,\-600\.000000\,\-550\.000000\,\-500\.000000\,\-450\.000000\,\-400\.000000\,\-350\.000000\,\-300\.000000\,\-250\.000000\,\-200\.000000\,\-150\.000000\,\-100\.000000\,\-50\.000000\,0\.000000\,50\.000000\,100\.000000\,150\.000000\,200\.000000\,250\.000000\,300\.000000\,350\.000000\,400\.000000\,450\.000000\,500\.000000\,550\.000000\,600\.000000\,650\.000000\,700\.000000\,750\.000000\,800\.000000\,850\.000000\,900\.000000\,950\.000000\,1000\.000000\,1050\.000000\,1100\.000000\,1150\.000000\,1200\.000000\,1250\.000000\,1300\.000000\,1350\.000000\,1400\.000000\,1450\.000000\,1500\.000000\,1550\.000000\,1600\.000000\,1650\.000000\,1700\.000000\,1750\.000000\,1800\.000000\,1850\.000000\,1900\.000000\,1950\.000000\,2000\.000000\,2050\.000000\,2100\.000000\,2150\.000000\,2200\.000000\,2250\.000000\,2300\.000000\,2350\.000000\,2400\.000000\,2450\.000000\,2500\.000000\,2550\.000000\,2600\.000000\,2650\.000000\,2700\.000000\,2750\.000000\,2800\.000000\,2850\.000000\,2900\.000000\,2950\.000000\,3000\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Profile\ near\ TTS\"\,xlab\=\"Relative\ Distance\ to\ TTS\ \(bp\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ abline\(v\=0\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ x\<\-c\(\-1000\.000000\,\-950\.000000\,\-900\.000000\,\-850\.000000\,\-800\.000000\,\-750\.000000\,\-700\.000000\,\-650\.000000\,\-600\.000000\,\-550\.000000\,\-500\.000000\,\-450\.000000\,\-400\.000000\,\-350\.000000\,\-300\.000000\,\-250\.000000\,\-200\.000000\,\-150\.000000\,\-100\.000000\,\-50\.000000\,0\.000000\,50\.000000\,100\.000000\,150\.000000\,200\.000000\,250\.000000\,300\.000000\,350\.000000\,400\.000000\,450\.000000\,500\.000000\,550\.000000\,600\.000000\,650\.000000\,700\.000000\,750\.000000\,800\.000000\,850\.000000\,900\.000000\,950\.000000\,1000\.000000\,1050\.000000\,1100\.000000\,1150\.000000\,1200\.000000\,1250\.000000\,1300\.000000\,1350\.000000\,1400\.000000\,1450\.000000\,1500\.000000\,1550\.000000\,1600\.000000\,1650\.000000\,1700\.000000\,1750\.000000\,1800\.000000\,1850\.000000\,1900\.000000\,1950\.000000\,2000\.000000\,2050\.000000\,2100\.000000\,2150\.000000\,2200\.000000\,2250\.000000\,2300\.000000\,2350\.000000\,2400\.000000\,2450\.000000\,2500\.000000\,2550\.000000\,2600\.000000\,2650\.000000\,2700\.000000\,2750\.000000\,2800\.000000\,2850\.000000\,2900\.000000\,2950\.000000\,3000\.000000\,3050\.000000\,3100\.000000\,3150\.000000\,3200\.000000\,3250\.000000\,3300\.000000\,3350\.000000\,3400\.000000\,3450\.000000\,3500\.000000\,3550\.000000\,3600\.000000\,3650\.000000\,3700\.000000\,3750\.000000\,3800\.000000\,3850\.000000\,3900\.000000\,3950\.000000\,4000\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,238\.095238\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,217\.391304\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Gene\ Profile\"\,xlab\=\"Upstream\ \(bp\)\,\ 3000\ bp\ of\ Meta\-gene\,\ Downstream\ \(bp\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ abline\(v\=0\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ abline\(v\=3000\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ x\<\-c\(0\.000000\,3\.333333\,6\.666667\,10\.000000\,13\.333333\,16\.666667\,20\.000000\,23\.333333\,26\.666667\,30\.000000\,33\.333333\,36\.666667\,40\.000000\,43\.333333\,46\.666667\,50\.000000\,53\.333333\,56\.666667\,60\.000000\,63\.333333\,66\.666667\,70\.000000\,73\.333333\,76\.666667\,80\.000000\,83\.333333\,86\.666667\,90\.000000\,93\.333333\,96\.666667\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,250\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Concatenated\ Exon\ Profile\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,250\.000000\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,3\.333333\,6\.666667\,10\.000000\,13\.333333\,16\.666667\,20\.000000\,23\.333333\,26\.666667\,30\.000000\,33\.333333\,36\.666667\,40\.000000\,43\.333333\,46\.666667\,50\.000000\,53\.333333\,56\.666667\,60\.000000\,63\.333333\,66\.666667\,70\.000000\,73\.333333\,76\.666667\,80\.000000\,83\.333333\,86\.666667\,90\.000000\,93\.333333\,96\.666667\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Concatenated\ Intron\ Profile\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,250\.000000\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ par\(mfrow\=c\(3\,\ 2\)\,mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ x\<\-c\(0\.000000\,50\.000000\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Exon\ Profile -\+\ \(56\ \<\=\ length\ \<\ 109\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,132\.596685\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,25\.000000\,50\.000000\,75\.000000\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Intron\ Profile -\+\ \(110\ \<\=\ length\ \<\ 345\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,132\.596685\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,50\.000000\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Exon\ Profile -\+\ \(109\ \<\=\ length\ \<\ 160\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,132\.596685\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,11\.111111\,22\.222222\,33\.333333\,44\.444444\,55\.555556\,66\.666667\,77\.777778\,88\.888889\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Intron\ Profile -\+\ \(344\ \<\=\ length\ \<\ 686\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,132\.596685\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,33\.333333\,66\.666667\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Exon\ Profile -\+\ \(160\ \<\=\ length\ \<\ 375\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,132\.596685\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,4\.761905\,9\.523810\,14\.285714\,19\.047619\,23\.809524\,28\.571429\,33\.333333\,38\.095238\,42\.857143\,47\.619048\,52\.380952\,57\.142857\,61\.904762\,66\.666667\,71\.428571\,76\.190476\,80\.952381\,85\.714286\,90\.476190\,95\.238095\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,110\.497238\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Intron\ Profile -\+\ \(685\ \<\=\ length\ \<\ 2653\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,132\.596685\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ dev\.off\(\) -null\ device\ -\ \ \ \ \ \ \ \ \ \ 1\ -\>\ -INFO\ \ \@\ .*\ \#\.\.\.\ cong\!\ See\ ceas\.pdf\ for\ the\ graphical\ results\ of\ CEAS\!\
--- a/ceas-example/test-data/ceas_out2.xls Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,133 +0,0 @@ -# RefSeq: RefSeq ID -# chr: chromosome of a RefSeq gene -# txStart: 5' end of a RefSeq gene -# txEnd: 3' end site of a RefSeq gene -# strand: strand of a RefSeq gene -# dist u TSS: Distance to the nearest ChIP region's center upstream of transcription start site (bp) -# dist d TSS: Distance to the nearest ChIP region's center downstream of transcription start site (bp) -# dist u TTS: Distance to the nearest ChIP region's center upstream of transcription end site (bp) -# dist d TTS: Distance to the nearest ChIP region's center downstream of transcription end (bp) -# 3000bp u TSS: Occupancy rate of ChIP region in 3000bp upstream of transcription start site (0.0 - 1.0) -# 3000bp d TSS: Occupancy rate of ChIP region in 3000bp downstream of transcription start site (0.0 - 1.0) -# 1/3 gene: Occupancy rate of ChIP region in 1/3 gene (0.0 - 1.0) -# 2/3 gene: Occupancy rate of ChIP region in 2/3 gene (0.0 - 1.0) -# 3/3 gene: Occupancy rate of ChIP region in 3/3 gene (0.0 - 1.0) -# 3000bp d TTS: Occupancy rate of ChIP region in 3000bp downstream of transcriptino end (0.0 - 1.0) -# exons: Occupancy rate of ChIP regions in exons (0.0-1.0) -# Note that txStart and txEnd indicate 5' and 3' ends of genes whereas TSS and TTS transcription start and end sites in consideration of strand. -#name chr txStart txEnd strand dist u TSS dist d TSS dist u TTS dist d TTS 3000bp u TSS 3000bp d TSS 1/3 gene 2/3 gene 3/3 gene 3000bp d TTS exons -NM_001031576 chr26 19281 27136 + NA 4099848 NA 4091993 0 0 0 0 0 0 0 -NM_204615 chr26 57466 61594 - 4057535 NA 4061663 NA 0 0 0 0 0 0 0 -NM_001005431 chr26 65800 76175 - 4042954 NA 4053329 NA 0 0 0 0 0 0 0 -NM_001145491 chr26 91618 92166 - 4026963 NA 4027511 NA 0 0 0 0 0 0 0 -NM_204398 chr26 93069 97423 + NA 4026060 NA 4021706 0 0 0 0 0 0 0 -NM_001305147 chr26 254661 282571 + NA 3864468 NA 3836558 0 0 0 0 0 0 0 -NM_001305148 chr26 254661 282571 + NA 3864468 NA 3836558 0 0 0 0 0 0 0 -NM_001012868 chr26 350397 355252 - 3763877 NA 3768732 NA 0 0 0 0 0 0 0 -NM_001031030 chr26 479573 493561 + NA 3639556 NA 3625568 0 0 0 0 0 0 0 -NM_001305140 chr26 520705 526012 + NA 3598424 NA 3593117 0 0 0 0 0 0 0 -NM_001031029 chr26 537101 565572 + NA 3582028 NA 3553557 0 0 0 0 0 0 0 -NM_205248 chr26 537250 760479 + NA 3581879 NA 3358650 0 0 0 0 0 0 0 -NM_204727 chr26 662969 683066 - 3436063 NA 3456160 NA 0 0 0 0 0 0 0 -NR_105475 chr26 669012 669122 - 3450007 NA 3450117 NA 0 0 0 0 0 0 0 -NM_205449 chr26 785617 794076 + NA 3333512 NA 3325053 0 0 0 0 0 0 0 -NM_204681 chr26 897458 902049 - 3217080 NA 3221671 NA 0 0 0 0 0 0 0 -NM_001278156 chr26 905313 917094 - 3202035 NA 3213816 NA 0 0 0 0 0 0 0 -NM_204184 chr26 960209 964268 - 3154861 NA 3158920 NA 0 0 0 0 0 0 0 -NM_204316 chr26 974223 991244 + NA 3144906 NA 3127885 0 0 0 0 0 0 0 -NM_001031028 chr26 993815 1003847 - 3115282 NA 3125314 NA 0 0 0 0 0 0 0 -NM_001006392 chr26 1024606 1047756 - 3071373 NA 3094523 NA 0 0 0 0 0 0 0 -NM_001039596 chr26 1073550 1080033 - 3039096 NA 3045579 NA 0 0 0 0 0 0 0 -NM_001031027 chr26 1090460 1096137 + NA 3028669 NA 3022992 0 0 0 0 0 0 0 -NM_001031026 chr26 1096566 1104751 - 3014378 NA 3022563 NA 0 0 0 0 0 0 0 -NM_001030913 chr26 1122296 1132596 + NA 2996833 NA 2986533 0 0 0 0 0 0 0 -NM_001171886 chr26 1220031 1222791 - 2896338 NA 2899098 NA 0 0 0 0 0 0 0 -NM_205054 chr26 1229703 1232833 + NA 2889426 NA 2886296 0 0 0 0 0 0 0 -NM_204462 chr26 1234686 1236536 - 2882593 NA 2884443 NA 0 0 0 0 0 0 0 -NM_204463 chr26 1265724 1267989 - 2851140 NA 2853405 NA 0 0 0 0 0 0 0 -NM_001030378 chr26 1289091 1290386 - 2828743 NA 2830038 NA 0 0 0 0 0 0 0 -NM_001195554 chr26 1382510 1388447 + NA 2736619 NA 2730682 0 0 0 0 0 0 0 -NM_001012548 chr26 1406905 1428443 + NA 2712224 NA 2690686 0 0 0 0 0 0 0 -NR_031486 chr26 1442696 1442779 - 2676350 NA 2676433 NA 0 0 0 0 0 0 0 -NR_031487 chr26 1442896 1442979 - 2676150 NA 2676233 NA 0 0 0 0 0 0 0 -NM_205250 chr26 1472137 1474003 + NA 2646992 NA 2645126 0 0 0 0 0 0 0 -NR_105486 chr26 1566398 1566508 - 2552621 NA 2552731 NA 0 0 0 0 0 0 0 -NM_001160320 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004709 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160324 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004493 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160323 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160322 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160321 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004395 chr26 1776431 1800083 + NA 2342698 NA 2319046 0 0 0 0 0 0 0 -NM_001030914 chr26 1811042 1820368 - 2298761 NA 2308087 NA 0 0 0 0 0 0 0 -NM_204506 chr26 1823407 1843085 - 2276044 NA 2295722 NA 0 0 0 0 0 0 0 -NR_031488 chr26 1925941 1926037 - 2193092 NA 2193188 NA 0 0 0 0 0 0 0 -NM_213581 chr26 2070404 2084478 - 2034651 NA 2048725 NA 0 0 0 0 0 0 0 -NR_035298 chr26 2086590 2086691 - 2032438 NA 2032539 NA 0 0 0 0 0 0 0 -NR_105470 chr26 2094750 2094860 - 2024269 NA 2024379 NA 0 0 0 0 0 0 0 -NM_001030915 chr26 2117140 2128322 - 1990807 NA 2001989 NA 0 0 0 0 0 0 0 -NM_001031498 chr26 2175178 2177159 - 1941970 NA 1943951 NA 0 0 0 0 0 0 0 -NM_001008452 chr26 2305308 2315138 + NA 1813821 NA 1803991 0 0 0 0 0 0 0 -NM_001006322 chr26 2315293 2325130 - 1793999 NA 1803836 NA 0 0 0 0 0 0 0 -NM_001004414 chr26 2373245 2375480 - 1743649 NA 1745884 NA 0 0 0 0 0 0 0 -NM_001044644 chr26 2390696 2401255 - 1717874 NA 1728433 NA 0 0 0 0 0 0 0 -NM_001031499 chr26 2425841 2429413 - 1689716 NA 1693288 NA 0 0 0 0 0 0 0 -NM_001033642 chr26 2445710 2453125 + NA 1673419 NA 1666004 0 0 0 0 0 0 0 -NM_001033643 chr26 2469318 2475028 + NA 1649811 NA 1644101 0 0 0 0 0 0 0 -NM_204664 chr26 2498398 2509349 + NA 1620731 NA 1609780 0 0 0 0 0 0 0 -NR_031489 chr26 2511657 2511746 - 1607383 NA 1607472 NA 0 0 0 0 0 0 0 -NR_031490 chr26 2512568 2512648 - 1606481 NA 1606561 NA 0 0 0 0 0 0 0 -NR_105523 chr26 2669792 2669902 - 1449227 NA 1449337 NA 0 0 0 0 0 0 0 -NR_031491 chr26 2896046 2896142 + NA 1223083 NA 1222987 0 0 0 0 0 0 0 -NM_001190924 chr26 2961382 2962268 + NA 1157747 NA 1156861 0 0 0 0 0 0 0 -NM_001007881 chr26 2999189 3002725 + NA 1119940 NA 1116404 0 0 0 0 0 0 0 -NM_204320 chr26 3006741 3011817 - 1107312 NA 1112388 NA 0 0 0 0 0 0 0 -NM_001030916 chr26 3035271 3039335 - 1079794 NA 1083858 NA 0 0 0 0 0 0 0 -NM_204151 chr26 3047964 3050306 - 1068823 NA 1071165 NA 0 0 0 0 0 0 0 -NM_204326 chr26 3124816 3214381 - 904748 NA 994313 NA 0 0 0 0 0 0 0 -NM_204336 chr26 3320769 3332327 + NA 798360 NA 786802 0 0 0 0 0 0 0 -NM_204622 chr26 3339753 3358863 - 760266 NA 779376 NA 0 0 0 0 0 0 0 -NM_205515 chr26 3359016 3368964 + NA 760113 NA 750165 0 0 0 0 0 0 0 -NM_001012843 chr26 3370712 3377196 + NA 748417 NA 741933 0 0 0 0 0 0 0 -NM_001029849 chr26 3377655 3382628 - 736501 NA 741474 NA 0 0 0 0 0 0 0 -NM_001006323 chr26 3439841 3454783 - 664346 NA 679288 NA 0 0 0 0 0 0 0 -NR_035162 chr26 3455233 3455324 + NA 663896 NA 663805 0 0 0 0 0 0 0 -NM_001012697 chr26 3516478 3545774 + NA 602651 NA 573355 0 0 0 0 0 0 0 -NM_001030917 chr26 3590932 3597509 - 521620 NA 528197 NA 0 0 0 0 0 0 0 -NM_001031500 chr26 3597231 3600802 + NA 521898 NA 518327 0 0 0 0 0 0 0 -NM_001040018 chr26 3629575 3631171 + NA 489554 NA 487958 0 0 0 0 0 0 0 -NM_001257295 chr26 3698350 3701362 - 417767 NA 420779 NA 0 0 0 0 0 0 0 -NM_001257296 chr26 3701377 3715857 - 403272 NA 417752 NA 0 0 0 0 0 0 0 -NM_001012549 chr26 3735643 3742472 - 376657 NA 383486 NA 0 0 0 0 0 0 0 -NM_001030918 chr26 3742618 3760175 - 358954 NA 376511 NA 0 0 0 0 0 0 0 -NM_001006324 chr26 3760758 3765368 - 353761 NA 358371 NA 0 0 0 0 0 0 0 -NM_205063 chr26 3809805 3812700 + NA 309324 NA 306429 0 0 0 0 0 0 0 -NM_001293109 chr26 3859074 3879130 - 239999 NA 260055 NA 0 0 0 0 0 0 0 -NM_001293108 chr26 3859074 3882051 - 237078 NA 260055 NA 0 0 0 0 0 0 0 -NR_102328 chr26 3916006 3918143 - 200986 NA 203123 NA 0 0 0 0 0 0 0 -NM_204728 chr26 3920817 3937442 - 181687 NA 198312 NA 0 0 0 0 0 0 0 -NM_001244905 chr26 4104910 4108376 + NA 14219 NA 10753 0 0 0 0 0 0 0 -NM_001293166 chr26 4138324 4142325 - NA 23196 NA 19195 0 0 0 0 0 0 0 -NM_001030919 chr26 4144091 4175943 + 24962 NA 56814 NA 0 0 0 0 0 0 0 -NM_001257297 chr26 4209891 4216177 + 90762 NA 97048 NA 0 0 0 0 0 0 0 -NM_001257298 chr26 4218028 4238067 + 98899 NA 118938 NA 0 0 0 0 0 0 0 -NM_205490 chr26 4375371 4380959 - NA 261830 NA 256242 0 0 0 0 0 0 0 -NM_001305129 chr26 4391940 4397490 + 272811 NA 278361 NA 0 0 0 0 0 0 0 -NM_001271612 chr26 4433568 4438784 + 314439 NA 319655 NA 0 0 0 0 0 0 0 -NM_001030920 chr26 4498991 4730550 + 379862 NA 611421 NA 0 0 0 0 0 0 0 -NM_001030921 chr26 4541748 4544997 - NA 425868 NA 422619 0 0 0 0 0 0 0 -NM_001006325 chr26 4548211 4559974 + 429082 NA 440845 NA 0 0 0 0 0 0 0 -NM_001037832 chr26 4571684 4576072 - NA 456943 NA 452555 0 0 0 0 0 0 0 -NM_001080870 chr26 4578266 4580646 - NA 461517 NA 459137 0 0 0 0 0 0 0 -NM_001080868 chr26 4578266 4580647 - NA 461518 NA 459137 0 0 0 0 0 0 0 -NM_001030922 chr26 4730394 4744364 - NA 625235 NA 611265 0 0 0 0 0 0 0 -NM_204877 chr26 4751619 4755464 - NA 636335 NA 632490 0 0 0 0 0 0 0 -NM_001302134 chr26 4791084 4792014 + 671955 NA 672885 NA 0 0 0 0 0 0 0 -NM_001006327 chr26 4828534 4833077 - NA 713948 NA 709405 0 0 0 0 0 0 0 -NM_001008453 chr26 4838545 4850970 - NA 731841 NA 719416 0 0 0 0 0 0 0 -NM_001030923 chr26 4876559 4884910 + 757430 NA 765781 NA 0 0 0 0 0 0 0 -NM_204429 chr26 4897094 4901738 - NA 782609 NA 777965 0 0 0 0 0 0 0 -NM_204967 chr26 4946735 4952662 - NA 833533 NA 827606 0 0 0 0 0 0 0 -NM_204473 chr26 4990765 4993729 + 871636 NA 874600 NA 0 0 0 0 0 0 0 -NR_105623 chr26 5087926 5087980 - NA 968851 NA 968797 0 0 0 0 0 0 0
--- a/ceas-example/test-data/ceas_out3.log Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,235 +0,0 @@ -/home/pjb/galaxy-tools/ceas/test.tool_dependencies.ceas/bx_python/0.7.1/lib/python2.7/site-packages/pkg_resources.py:1054: UserWarning: /home/pjb/.python-eggs is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable). - warnings.warn(msg, UserWarning) -ceasBW -- 0.9.9.7 (package version 1.0.2) -/home/pjb/galaxy-tools/ceas/test.tool_dependencies.ceas/bx_python/0.7.1/lib/python2.7/site-packages/pkg_resources.py:1054: UserWarning: /home/pjb/.python-eggs is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable). - warnings.warn(msg, UserWarning) -INFO @ Tue, 23 Jun 2015 13:03:32: -# ARGUMENTS: -# name = ceas -# gene annotation table = galGal3.refGene -# BED file = ceas_in.bed -# WIG file = ceas_in.bigwig -# extra BED file = None -# ChIP annotation = On -# gene-centered annotation = On -# average profiling = On -# dump profiles = Off -# re-annotation for genome background (ChIP region annotation) = False -# promoter sizes (ChIP region annotation) = 1000,2000,3000 bp -# downstream sizes (ChIP region annotation) = 1000,2000,3000 bp -# bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp -# span size (gene-centered annotation) = 3000 bp -# profiling resolution (average profiling) = 50 bp -# relative distance wrt TSS and TTS (average profiling) = 3000 bp -INFO @ Tue, 23 Jun 2015 13:03:32: #1 read the gene table... -INFO @ Tue, 23 Jun 2015 13:03:32: #2 read the bed file of ChIP regions... -INFO @ Tue, 23 Jun 2015 13:03:32: #3 perform gene-centered annotation... -INFO @ Tue, 23 Jun 2015 13:03:32: #4 See ceas.xls for gene-centered annotation! -INFO @ Tue, 23 Jun 2015 13:03:32: #5 read the pre-computed genome bg annotation... -INFO @ Tue, 23 Jun 2015 13:03:32: #6 perform ChIP region annotation... -INFO @ Tue, 23 Jun 2015 13:03:32: #7 write a R script of ChIP region annotation... -INFO @ Tue, 23 Jun 2015 13:03:32: #8-1 run wig profiling of chr26... -INFO @ Tue, 23 Jun 2015 13:03:32: #9 append an R script of wig profiling... - -R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet" -Copyright (C) 2014 The R Foundation for Statistical Computing -Platform: x86_64-redhat-linux-gnu (64-bit) - -R is free software and comes with ABSOLUTELY NO WARRANTY. -You are welcome to redistribute it under certain conditions. -Type 'license()' or 'licence()' for distribution details. - - Natural language support but running in an English locale - -R is a collaborative project with many contributors. -Type 'contributors()' for more information and -'citation()' on how to cite R or R packages in publications. - -Type 'demo()' for some demos, 'help()' for on-line help, or -'help.start()' for an HTML browser interface to help. -Type 'q()' to quit R. - -> # ARGUMENTS: -> # name = ceas -> # gene annotation table = galGal3.refGene -> # BED file = ceas_in.bed -> # WIG file = ceas_in.bigwig -> # extra BED file = None -> # ChIP annotation = On -> # gene-centered annotation = On -> # average profiling = On -> # dump profiles = Off -> # re-annotation for genome background (ChIP region annotation) = False -> # promoter sizes (ChIP region annotation) = 1000,2000,3000 bp -> # downstream sizes (ChIP region annotation) = 1000,2000,3000 bp -> # bidrectional promoter sizes (ChIP region annotation) = 2500,5000 bp -> # span size (gene-centered annotation) = 3000 bp -> # profiling resolution (average profiling) = 50 bp -> # relative distance wrt TSS and TTS (average profiling) = 3000 bp -> pdf("ceas.pdf",height=11.5,width=8.5) -> -> # 13:03:32 Tue, 23 Jun 2015 -> # -> # ChIP annotation -> # -> -> -> # -> # Chromosomal Distribution -> # -> -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> r0<-c(100.0) -> r1<-c(100.0) -> height<-rbind(r0,r1) -> names=c("26") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=TRUE,col=c("#5FA1C1","#EB9D86"),main="Chromosomal Distribution of ChIP Regions",xlab="Percentage %",ylab="Chromosome",border=FALSE,xlim=c(0.000000,183.333333),cex.names=1) -> text(x=c(100.0),y=mp[1,],label=c("100.0 %"),pos=4,offset=0.2,cex=0.9) -> text(x=c(100.0),y=mp[2,],label=c("100.0 % (<=4.9e-324)"),pos=4,offset=0.2,cex=0.9) -> legend("right",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> -> # -> # Promoter,Bipromoter,Downstream, Gene and Regions of interest -> # -> -> par(mfrow=c(4, 1),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> r0<-c(1.8532425688606797, 3.616851183410451, 5.322318854623416) -> r1<-c(0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=1000 bp","<=2000 bp","<=3000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.757585),cex.names=1) -> text(x=mp[1,],y=c(1.8532425688606797, 3.616851183410451, 5.322318854623416),label=c("1.9 %","3.6 %","5.3 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 % -+ (0.981)","0.000 % -+ (0.964)","0.000 % -+ (0.947)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(0.03876062889120376, 0.03876062889120376) -> r1<-c(0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=2500 bp","<=5000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Bidirectional Promoter",ylab="Percentage %",border=FALSE,ylim=c(0.000000,0.071061),cex.names=1) -> text(x=mp[1,],y=c(0.03876062889120376, 0.03876062889120376),label=c("0.04 %","0.04 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0),label=c("0.000 % -+ (1.000)","0.000 % -+ (1.000)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(1.8290171758036773, 3.4690762857627364, 4.980740812519683) -> r1<-c(0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("<=1000 bp","<=2000 bp","<=3000 bp") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Downstream",ylab="Percentage %",border=FALSE,ylim=c(0.000000,9.131358),cex.names=1) -> text(x=mp[1,],y=c(1.8290171758036773, 3.4690762857627364, 4.980740812519683),label=c("1.8 %","3.5 %","5.0 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0),label=c("0.000 % -+ (0.982)","0.000 % -+ (0.965)","0.000 % -+ (0.950)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> r0<-c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054) -> r1<-c(0.0, 0.0, 0.0, 0.0, 0.0) -> height<-rbind(r0,r1) -> names=c("5'UTR","3'UTR","Coding Exon","Intron","All") -> mp<-barplot(height=height,names=names,beside=TRUE,horiz=FALSE,col=c("#5FA1C1","#EB9D86"),main="Gene",ylab="Percentage %",border=FALSE,ylim=c(0.000000,43.440571),cex.names=1) -> text(x=mp[1,],y=c(0.2034933016788197, 1.3978051793890356, 2.359553283752029, 19.734005184234114, 23.694856949054),label=c("0.2 %","1.4 %","2.4 %","19.7 %","23.7 %"),pos=3,offset=0.2) -> text(x=mp[2,],y=c(0.0, 0.0, 0.0, 0.0, 0.0),label=c("0.000 % -+ (0.998)","0.000 % -+ (0.986)","0.000 % -+ (0.976)","0.000 % -+ (0.803)","0.000 % -+ (0.763)"),pos=3,offset=0.2) -> legend("topleft",legend=c("Genome","ChIP (p-value)"),col=c("#5FA1C1","#EB9D86"),pch=15,bty="n") -> -> # -> # Distribution of Genome and ChIP regions over cis-regulatory element -> # Note that the x may be modified for better graphics in case a value is too small -> # Thus, look at the labels of the pie chart to get the real percentage values -> # -> -> par(mfcol=c(2, 2),mar=c(3, 3, 4, 2.8),oma=c(4, 2, 4, 2)) -> x<-c(0.018532,0.017055,0.016037,0.017830,0.015092,0.014051,0.010000,0.013833,0.023014,0.192592,0.670292) -> pie(x=x,labels=c("1.9 %","1.7 %","1.6 %","1.8 %","1.5 %","1.4 %","0.2 %","1.4 %","2.3 %","19.3 %","67.0 %"),main="Genome",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100) -> x<-c(0.000000,1.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s") -> legend("top",legend=c("Promoter (<=1000 bp): 1.9 %","Promoter (1000-2000 bp): 1.7 %","Promoter (2000-3000 bp): 1.6 %","Downstream (<=1000 bp): 1.8 %","Downstream (1000-2000 bp): 1.5 %","Downstream (2000-3000 bp): 1.4 %","5'UTR: 0.2 %","3'UTR: 1.4 %","Coding exon: 2.3 %","Intron: 19.3 %","Distal intergenic: 67.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n") -> x<-c(0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,0.010000,1.000000) -> pie(x=x,labels=c("0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","0.000 %","100.0 %"),main="ChIP",col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),clockwise=TRUE,border=FALSE,radius=0.9,cex=0.8,init.angle=90,density=100) -> x<-c(0.000000,1.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",frame=FALSE,axes=FALSE,xaxt="s",yaxt="s") -> legend("top",legend=c("Promoter (<=1000 bp): 0.000 %","Promoter (1000-2000 bp): 0.000 %","Promoter (2000-3000 bp): 0.000 %","Downstream (<=1000 bp): 0.000 %","Downstream (1000-2000 bp): 0.000 %","Downstream (2000-3000 bp): 0.000 %","5'UTR: 0.000 %","3'UTR: 0.000 %","Coding exon: 0.000 %","Intron: 0.000 %","Distal intergenic: 100.0 %"),col=c("#445FA2","#EB9D86","#799F7A","#6C527F","#5FA1C1","#E8BB77","#A8C5EF","#FDCDB9","#C6E6B5","#F1D5EE","#B4E1F6"),pch=15,bty="n") -> -> # -> # ChIP regions over the genome -> # -> -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> layout(matrix(c(1, 0, 2, 2), 2, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 5)) -> x<-c(0.000000,0.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="Distribution of Peak Heights",xlab="",ylab="",xlim=c(0.000000,0.000000),ylim=c(0.000000,1.000000),frame=FALSE,xaxt="s",yaxt="n",cex=0.9) -> x<-c(0.000000,0.000000,0.000000,0.000000) -> y<-c(0.000000,0.000000,1.000000,1.000000) -> polygon(x,y,col=c("black")) -> x <- c(0.000000) -> y<-c(0.800000) -> lines(x, y,xlim=c(0, 0.0),ylim=c(0, 1),type="l",col=c("cyan"),lwd=2) -> x<-c(0.000000,4127518.000000) -> y<-c(0.855556,1.144444) -> plot(x, y,type="n",main="ChIP Regions (Peaks) over Chromosomes",xlab="Chromosome Size (bp)",ylab="Chromosome",xlim=c(0.000000,4127518.000000),ylim=c(0.855556,1.144444),frame=FALSE,xaxt="s",yaxt="n") -> start <- c(4119129) -> end <- c(4119130) -> vals <- c(0.0) -> vals[vals > 0.0] <- 0.0 -> vals[vals < 0] <- 0 -> heights <- 0.288889 * ((vals - 0)/(0.0 - 0)) + 0.855555555556 -> for (i in 1:length(heights)) { -+ polygon(x=c(start[i], end[i], end[i], start[i]), y=c(0.855555555556, 0.855555555556, heights[i], heights[i]), col=c("#CC0000"), border=c("#CC0000")) -+ } -> mtext("26",side=2,line=0,outer=FALSE,at=1.0) -> par(mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> layout(matrix(c(1, 2, 3, 3, 4, 5), 3, 2, byrow = TRUE),widths=c(1, 1),heights=c(1, 1, 1)) -> x<-c(-3000.000000,-2950.000000,-2900.000000,-2850.000000,-2800.000000,-2750.000000,-2700.000000,-2650.000000,-2600.000000,-2550.000000,-2500.000000,-2450.000000,-2400.000000,-2350.000000,-2300.000000,-2250.000000,-2200.000000,-2150.000000,-2100.000000,-2050.000000,-2000.000000,-1950.000000,-1900.000000,-1850.000000,-1800.000000,-1750.000000,-1700.000000,-1650.000000,-1600.000000,-1550.000000,-1500.000000,-1450.000000,-1400.000000,-1350.000000,-1300.000000,-1250.000000,-1200.000000,-1150.000000,-1100.000000,-1050.000000,-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Profile near TSS",xlab="Relative Distance to TSS (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2) -> abline(v=0.000000,lty=2,col=c("black")) -> x<-c(-3000.000000,-2950.000000,-2900.000000,-2850.000000,-2800.000000,-2750.000000,-2700.000000,-2650.000000,-2600.000000,-2550.000000,-2500.000000,-2450.000000,-2400.000000,-2350.000000,-2300.000000,-2250.000000,-2200.000000,-2150.000000,-2100.000000,-2050.000000,-2000.000000,-1950.000000,-1900.000000,-1850.000000,-1800.000000,-1750.000000,-1700.000000,-1650.000000,-1600.000000,-1550.000000,-1500.000000,-1450.000000,-1400.000000,-1350.000000,-1300.000000,-1250.000000,-1200.000000,-1150.000000,-1100.000000,-1050.000000,-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Profile near TTS",xlab="Relative Distance to TTS (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2) -> abline(v=0.000000,lty=2,col=c("black")) -> x<-c(-1000.000000,-950.000000,-900.000000,-850.000000,-800.000000,-750.000000,-700.000000,-650.000000,-600.000000,-550.000000,-500.000000,-450.000000,-400.000000,-350.000000,-300.000000,-250.000000,-200.000000,-150.000000,-100.000000,-50.000000,0.000000,50.000000,100.000000,150.000000,200.000000,250.000000,300.000000,350.000000,400.000000,450.000000,500.000000,550.000000,600.000000,650.000000,700.000000,750.000000,800.000000,850.000000,900.000000,950.000000,1000.000000,1050.000000,1100.000000,1150.000000,1200.000000,1250.000000,1300.000000,1350.000000,1400.000000,1450.000000,1500.000000,1550.000000,1600.000000,1650.000000,1700.000000,1750.000000,1800.000000,1850.000000,1900.000000,1950.000000,2000.000000,2050.000000,2100.000000,2150.000000,2200.000000,2250.000000,2300.000000,2350.000000,2400.000000,2450.000000,2500.000000,2550.000000,2600.000000,2650.000000,2700.000000,2750.000000,2800.000000,2850.000000,2900.000000,2950.000000,3000.000000,3050.000000,3100.000000,3150.000000,3200.000000,3250.000000,3300.000000,3350.000000,3400.000000,3450.000000,3500.000000,3550.000000,3600.000000,3650.000000,3700.000000,3750.000000,3800.000000,3850.000000,3900.000000,3950.000000,4000.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Gene Profile",xlab="Upstream (bp), 3000 bp of Meta-gene, Downstream (bp)",ylab="Average Profile",col=c("#C8524D"),xaxt="s",yaxt="s",lwd=2) -> abline(v=0.000000,lty=2,col=c("black")) -> abline(v=3000.000000,lty=2,col=c("black")) -> x<-c(0.000000,3.333333,6.666667,10.000000,13.333333,16.666667,20.000000,23.333333,26.666667,30.000000,33.333333,36.666667,40.000000,43.333333,46.666667,50.000000,53.333333,56.666667,60.000000,63.333333,66.666667,70.000000,73.333333,76.666667,80.000000,83.333333,86.666667,90.000000,93.333333,96.666667,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Concatenated Exon Profile",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,20000.000000),xaxt="s",yaxt="s",lwd=2) -> x<-c(0.000000,3.333333,6.666667,10.000000,13.333333,16.666667,20.000000,23.333333,26.666667,30.000000,33.333333,36.666667,40.000000,43.333333,46.666667,50.000000,53.333333,56.666667,60.000000,63.333333,66.666667,70.000000,73.333333,76.666667,80.000000,83.333333,86.666667,90.000000,93.333333,96.666667,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000) -> plot(x, y,type="l",main="Average Concatenated Intron Profile",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,20000.000000),xaxt="s",yaxt="s",lwd=2) -> par(mfrow=c(3, 2),mar=c(4, 4, 5, 3.8),oma=c(4, 2, 4, 2)) -> x<-c(-3.000000,3.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s") -> x<-c(-3.000000,3.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s") -> x<-c(-3.000000,3.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s") -> x<-c(-3.000000,3.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s") -> x<-c(-3.000000,3.000000) -> y<-c(0.000000,1.000000) -> plot(x, y,type="n",main="",xlab="",ylab="",xlim=c(-3.000000,3.000000),ylim=c(0.000000,1.000000),axes=FALSE,xaxt="s",yaxt="s") -> x<-c(0.000000,4.761905,9.523810,14.285714,19.047619,23.809524,28.571429,33.333333,38.095238,42.857143,47.619048,52.380952,57.142857,61.904762,66.666667,71.428571,76.190476,80.952381,85.714286,90.476190,95.238095,100.000000) -> y<-c(0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,20000.000000,0.000000) -> plot(x, y,type="l",main="Average Intron Profile -+ (685 <= length < 2653 bp)",xlab="Relative Location (%)",ylab="Average Profile",col=c("#C8524D"),ylim=c(0.000000,24000.000000),xaxt="s",yaxt="s",lwd=2) -> dev.off() -null device - 1 -> -INFO @ Tue, 23 Jun 2015 13:03:33: #... cong! See ceas.pdf for the graphical results of CEAS!
--- a/ceas-example/test-data/ceas_out3.log.re_match Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,231 +0,0 @@ -ceasBW\ \-\-\ 0\.9\.9\.7\ \(package\ version\ 1\.0\.2\) -INFO\ \ \@\ .*\ -\#\ ARGUMENTS\:\ -\#\ name\ \=\ ceas -\#\ gene\ annotation\ table\ \=\ .*galGal3\.refGene -\#\ BED\ file\ \=\ .* -\#\ WIG\ file\ \=\ .* -\#\ extra\ BED\ file\ \=\ None -\#\ ChIP\ annotation\ \=\ On -\#\ gene\-centered\ annotation\ \=\ \ On -\#\ average\ profiling\ \=\ On -\#\ dump\ profiles\ \=\ Off -\#\ re\-annotation\ for\ genome\ background\ \(ChIP\ region\ annotation\)\ \=\ False -\#\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\#\ downstream\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\#\ bidrectional\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 2500\,5000\ bp -\#\ span\ size\ \(gene\-centered\ annotation\)\ \=\ 3000\ bp -\#\ profiling\ resolution\ \(average\ profiling\)\ \=\ 50\ bp -\#\ relative\ distance\ wrt\ TSS\ and\ TTS\ \(average\ profiling\)\ \=\ 3000\ bp\ -INFO\ \ \@\ .*\ \#1\ read\ the\ gene\ table\.\.\.\ -INFO\ \ \@\ .*\ \#2\ read\ the\ bed\ file\ of\ ChIP\ regions\.\.\.\ -INFO\ \ \@\ .*\ \#3\ perform\ gene\-centered\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#4\ See\ ceas\.xls\ for\ gene\-centered\ annotation\!\ -INFO\ \ \@\ .*\ \#5\ read\ the\ pre\-computed\ genome\ bg\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#6\ perform\ ChIP\ region\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#7\ write\ a\ R\ script\ of\ ChIP\ region\ annotation\.\.\.\ -INFO\ \ \@\ .*\ \#8\-1\ run\ wig\ profiling\ of\ chr26\.\.\.\ -INFO\ \ \@\ .*\ \#9\ append\ an\ R\ script\ of\ wig\ profiling\.\.\.\ - -R\ version\ 3\.1\.2\ \(2014\-10\-31\)\ \-\-\ \"Pumpkin\ Helmet\" -Copyright\ \(C\)\ 2014\ The\ R\ Foundation\ for\ Statistical\ Computing -Platform\:\ .* - -R\ is\ free\ software\ and\ comes\ with\ ABSOLUTELY\ NO\ WARRANTY\. -You\ are\ welcome\ to\ redistribute\ it\ under\ certain\ conditions\. -Type\ \'license\(\)\'\ or\ \'licence\(\)\'\ for\ distribution\ details\. - -\ \ Natural\ language\ support\ but\ running\ in\ an\ English\ locale - -R\ is\ a\ collaborative\ project\ with\ many\ contributors\. -Type\ \'contributors\(\)\'\ for\ more\ information\ and -\'citation\(\)\'\ on\ how\ to\ cite\ R\ or\ R\ packages\ in\ publications\. - -Type\ \'demo\(\)\'\ for\ some\ demos\,\ \'help\(\)\'\ for\ on\-line\ help\,\ or -\'help\.start\(\)\'\ for\ an\ HTML\ browser\ interface\ to\ help\. -Type\ \'q\(\)\'\ to\ quit\ R\. - -\>\ \#\ ARGUMENTS\:\ -\>\ \#\ name\ \=\ ceas -\>\ \#\ gene\ annotation\ table\ \=\ .*galGal3\.refGene -\>\ \#\ BED\ file\ \=\ .* -\>\ \#\ WIG\ file\ \=\ .* -\>\ \#\ extra\ BED\ file\ \=\ None -\>\ \#\ ChIP\ annotation\ \=\ On -\>\ \#\ gene\-centered\ annotation\ \=\ \ On -\>\ \#\ average\ profiling\ \=\ On -\>\ \#\ dump\ profiles\ \=\ Off -\>\ \#\ re\-annotation\ for\ genome\ background\ \(ChIP\ region\ annotation\)\ \=\ False -\>\ \#\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\>\ \#\ downstream\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 1000\,2000\,3000\ bp -\>\ \#\ bidrectional\ promoter\ sizes\ \(ChIP\ region\ annotation\)\ \=\ 2500\,5000\ bp -\>\ \#\ span\ size\ \(gene\-centered\ annotation\)\ \=\ 3000\ bp -\>\ \#\ profiling\ resolution\ \(average\ profiling\)\ \=\ 50\ bp -\>\ \#\ relative\ distance\ wrt\ TSS\ and\ TTS\ \(average\ profiling\)\ \=\ 3000\ bp -\>\ pdf\(\"ceas\.pdf\"\,height\=11\.5\,width\=8\.5\) -\>\ -\>\ \#\ .* -\>\ \#\ -\>\ \#\ ChIP\ annotation -\>\ \#\ -\>\ -\>\ -\>\ \#\ -\>\ \#\ Chromosomal\ Distribution -\>\ \#\ -\>\ -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ r0\<\-c\(100\.0\) -\>\ r1\<\-c\(100\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"26\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=TRUE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Chromosomal\ Distribution\ of\ ChIP\ Regions\"\,xlab\=\"Percentage\ \%\"\,ylab\=\"Chromosome\"\,border\=FALSE\,xlim\=c\(0\.000000\,183\.333333\)\,cex\.names\=1\) -\>\ text\(x\=c\(100\.0\)\,y\=mp\[1\,\]\,label\=c\(\"100\.0\ \%\"\)\,pos\=4\,offset\=0\.2\,cex\=0\.9\) -\>\ text\(x\=c\(100\.0\)\,y\=mp\[2\,\]\,label\=c\(\"100\.0\ \%\ \(\<\=4\.9e\-324\)\"\)\,pos\=4\,offset\=0\.2\,cex\=0\.9\) -\>\ legend\(\"right\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ Promoter\,Bipromoter\,Downstream\,\ Gene\ and\ Regions\ of\ interest -\>\ \#\ -\>\ -\>\ par\(mfrow\=c\(4\,\ 1\)\,mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ r0\<\-c\(1\.8532425688606797\,\ 3\.616851183410451\,\ 5\.322318854623416\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=1000\ bp\"\,\"\<\=2000\ bp\"\,\"\<\=3000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Promoter\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,9\.757585\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(1\.8532425688606797\,\ 3\.616851183410451\,\ 5\.322318854623416\)\,label\=c\(\"1\.9\ \%\"\,\"3\.6\ \%\"\,\"5\.3\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.981\)\"\,\"0\.000\ \% -\+\ \(0\.964\)\"\,\"0\.000\ \% -\+\ \(0\.947\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(0\.03876062889120376\,\ 0\.03876062889120376\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=2500\ bp\"\,\"\<\=5000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Bidirectional\ Promoter\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,0\.071061\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(0\.03876062889120376\,\ 0\.03876062889120376\)\,label\=c\(\"0\.04\ \%\"\,\"0\.04\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(1\.000\)\"\,\"0\.000\ \% -\+\ \(1\.000\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(1\.8290171758036773\,\ 3\.4690762857627364\,\ 4\.980740812519683\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"\<\=1000\ bp\"\,\"\<\=2000\ bp\"\,\"\<\=3000\ bp\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Downstream\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,9\.131358\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(1\.8290171758036773\,\ 3\.4690762857627364\,\ 4\.980740812519683\)\,label\=c\(\"1\.8\ \%\"\,\"3\.5\ \%\"\,\"5\.0\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.982\)\"\,\"0\.000\ \% -\+\ \(0\.965\)\"\,\"0\.000\ \% -\+\ \(0\.950\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ r0\<\-c\(0\.2034933016788197\,\ 1\.3978051793890356\,\ 2\.359553283752029\,\ 19\.734005184234114\,\ 23\.694856949054\) -\>\ r1\<\-c\(0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\) -\>\ height\<\-rbind\(r0\,r1\) -\>\ names\=c\(\"5\'UTR\"\,\"3\'UTR\"\,\"Coding\ Exon\"\,\"Intron\"\,\"All\"\) -\>\ mp\<\-barplot\(height\=height\,names\=names\,beside\=TRUE\,horiz\=FALSE\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,main\=\"Gene\"\,ylab\=\"Percentage\ \%\"\,border\=FALSE\,ylim\=c\(0\.000000\,43\.440571\)\,cex\.names\=1\) -\>\ text\(x\=mp\[1\,\]\,y\=c\(0\.2034933016788197\,\ 1\.3978051793890356\,\ 2\.359553283752029\,\ 19\.734005184234114\,\ 23\.694856949054\)\,label\=c\(\"0\.2\ \%\"\,\"1\.4\ \%\"\,\"2\.4\ \%\"\,\"19\.7\ \%\"\,\"23\.7\ \%\"\)\,pos\=3\,offset\=0\.2\) -\>\ text\(x\=mp\[2\,\]\,y\=c\(0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\,\ 0\.0\)\,label\=c\(\"0\.000\ \% -\+\ \(0\.998\)\"\,\"0\.000\ \% -\+\ \(0\.986\)\"\,\"0\.000\ \% -\+\ \(0\.976\)\"\,\"0\.000\ \% -\+\ \(0\.803\)\"\,\"0\.000\ \% -\+\ \(0\.763\)\"\)\,pos\=3\,offset\=0\.2\) -\>\ legend\(\"topleft\"\,legend\=c\(\"Genome\"\,\"ChIP\ \(p\-value\)\"\)\,col\=c\(\"\#5FA1C1\"\,\"\#EB9D86\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ Distribution\ of\ Genome\ and\ ChIP\ regions\ over\ cis\-regulatory\ element -\>\ \#\ Note\ that\ the\ x\ may\ be\ modified\ for\ better\ graphics\ in\ case\ a\ value\ is\ too\ small -\>\ \#\ Thus\,\ look\ at\ the\ labels\ of\ the\ pie\ chart\ to\ get\ the\ real\ percentage\ values -\>\ \#\ -\>\ -\>\ par\(mfcol\=c\(2\,\ 2\)\,mar\=c\(3\,\ 3\,\ 4\,\ 2\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ x\<\-c\(0\.018532\,0\.017055\,0\.016037\,0\.017830\,0\.015092\,0\.014051\,0\.010000\,0\.013833\,0\.023014\,0\.192592\,0\.670292\) -\>\ pie\(x\=x\,labels\=c\(\"1\.9\ \%\"\,\"1\.7\ \%\"\,\"1\.6\ \%\"\,\"1\.8\ \%\"\,\"1\.5\ \%\"\,\"1\.4\ \%\"\,\"0\.2\ \%\"\,\"1\.4\ \%\"\,\"2\.3\ \%\"\,\"19\.3\ \%\"\,\"67\.0\ \%\"\)\,main\=\"Genome\"\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,clockwise\=TRUE\,border\=FALSE\,radius\=0\.9\,cex\=0\.8\,init\.angle\=90\,density\=100\) -\>\ x\<\-c\(0\.000000\,1\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,frame\=FALSE\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ legend\(\"top\"\,legend\=c\(\"Promoter\ \(\<\=1000\ bp\)\:\ 1\.9\ \%\"\,\"Promoter\ \(1000\-2000\ bp\)\:\ 1\.7\ \%\"\,\"Promoter\ \(2000\-3000\ bp\)\:\ 1\.6\ \%\"\,\"Downstream\ \(\<\=1000\ bp\)\:\ 1\.8\ \%\"\,\"Downstream\ \(1000\-2000\ bp\)\:\ 1\.5\ \%\"\,\"Downstream\ \(2000\-3000\ bp\)\:\ 1\.4\ \%\"\,\"5\'UTR\:\ 0\.2\ \%\"\,\"3\'UTR\:\ 1\.4\ \%\"\,\"Coding\ exon\:\ 2\.3\ \%\"\,\"Intron\:\ 19\.3\ \%\"\,\"Distal\ intergenic\:\ 67\.0\ \%\"\)\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,pch\=15\,bty\=\"n\"\) -\>\ x\<\-c\(0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,0\.010000\,1\.000000\) -\>\ pie\(x\=x\,labels\=c\(\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"0\.000\ \%\"\,\"100\.0\ \%\"\)\,main\=\"ChIP\"\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,clockwise\=TRUE\,border\=FALSE\,radius\=0\.9\,cex\=0\.8\,init\.angle\=90\,density\=100\) -\>\ x\<\-c\(0\.000000\,1\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,frame\=FALSE\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ legend\(\"top\"\,legend\=c\(\"Promoter\ \(\<\=1000\ bp\)\:\ 0\.000\ \%\"\,\"Promoter\ \(1000\-2000\ bp\)\:\ 0\.000\ \%\"\,\"Promoter\ \(2000\-3000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(\<\=1000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(1000\-2000\ bp\)\:\ 0\.000\ \%\"\,\"Downstream\ \(2000\-3000\ bp\)\:\ 0\.000\ \%\"\,\"5\'UTR\:\ 0\.000\ \%\"\,\"3\'UTR\:\ 0\.000\ \%\"\,\"Coding\ exon\:\ 0\.000\ \%\"\,\"Intron\:\ 0\.000\ \%\"\,\"Distal\ intergenic\:\ 100\.0\ \%\"\)\,col\=c\(\"\#445FA2\"\,\"\#EB9D86\"\,\"\#799F7A\"\,\"\#6C527F\"\,\"\#5FA1C1\"\,\"\#E8BB77\"\,\"\#A8C5EF\"\,\"\#FDCDB9\"\,\"\#C6E6B5\"\,\"\#F1D5EE\"\,\"\#B4E1F6\"\)\,pch\=15\,bty\=\"n\"\) -\>\ -\>\ \#\ -\>\ \#\ ChIP\ regions\ over\ the\ genome -\>\ \#\ -\>\ -\>\ par\(mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ layout\(matrix\(c\(1\,\ 0\,\ 2\,\ 2\)\,\ 2\,\ 2\,\ byrow\ \=\ TRUE\)\,widths\=c\(1\,\ 1\)\,heights\=c\(1\,\ 5\)\) -\>\ x\<\-c\(0\.000000\,0\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"Distribution\ of\ Peak\ Heights\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(0\.000000\,0\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,frame\=FALSE\,xaxt\=\"s\"\,yaxt\=\"n\"\,cex\=0\.9\) -\>\ x\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,1\.000000\,1\.000000\) -\>\ polygon\(x\,y\,col\=c\(\"black\"\)\) -\>\ x\ \<\-\ c\(0\.000000\) -\>\ y\<\-c\(0\.800000\) -\>\ lines\(x\,\ y\,xlim\=c\(0\,\ 0\.0\)\,ylim\=c\(0\,\ 1\)\,type\=\"l\"\,col\=c\(\"cyan\"\)\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,4127518\.000000\) -\>\ y\<\-c\(0\.855556\,1\.144444\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"ChIP\ Regions\ \(Peaks\)\ over\ Chromosomes\"\,xlab\=\"Chromosome\ Size\ \(bp\)\"\,ylab\=\"Chromosome\"\,xlim\=c\(0\.000000\,4127518\.000000\)\,ylim\=c\(0\.855556\,1\.144444\)\,frame\=FALSE\,xaxt\=\"s\"\,yaxt\=\"n\"\) -\>\ start\ \<\-\ c\(4119129\) -\>\ end\ \<\-\ c\(4119130\) -\>\ vals\ \<\-\ c\(0\.0\) -\>\ vals\[vals\ \>\ 0\.0\]\ \<\-\ 0\.0 -\>\ vals\[vals\ \<\ 0\]\ \<\-\ 0 -\>\ heights\ \<\-\ 0\.288889\ \*\ \(\(vals\ \-\ 0\)\/\(0\.0\ \-\ 0\)\)\ \+\ 0\.855555555556 -\>\ for\ \(i\ in\ 1\:length\(heights\)\)\ \{ -\+\ \ polygon\(x\=c\(start\[i\]\,\ end\[i\]\,\ end\[i\]\,\ start\[i\]\)\,\ y\=c\(0\.855555555556\,\ 0\.855555555556\,\ heights\[i\]\,\ heights\[i\]\)\,\ col\=c\(\"\#CC0000\"\)\,\ border\=c\(\"\#CC0000\"\)\) -\+\ \} -\>\ mtext\(\"26\"\,side\=2\,line\=0\,outer\=FALSE\,at\=1\.0\) 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-\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Profile\ near\ TTS\"\,xlab\=\"Relative\ Distance\ to\ TTS\ \(bp\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ abline\(v\=0\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ x\<\-c\(\-1000\.000000\,\-950\.000000\,\-900\.000000\,\-850\.000000\,\-800\.000000\,\-750\.000000\,\-700\.000000\,\-650\.000000\,\-600\.000000\,\-550\.000000\,\-500\.000000\,\-450\.000000\,\-400\.000000\,\-350\.000000\,\-300\.000000\,\-250\.000000\,\-200\.000000\,\-150\.000000\,\-100\.000000\,\-50\.000000\,0\.000000\,50\.000000\,100\.000000\,150\.000000\,200\.000000\,250\.000000\,300\.000000\,350\.000000\,400\.000000\,450\.000000\,500\.000000\,550\.000000\,600\.000000\,650\.000000\,700\.000000\,750\.000000\,800\.000000\,850\.000000\,900\.000000\,950\.000000\,1000\.000000\,1050\.000000\,1100\.000000\,1150\.000000\,1200\.000000\,1250\.000000\,1300\.000000\,1350\.000000\,1400\.000000\,1450\.000000\,1500\.000000\,1550\.000000\,1600\.000000\,1650\.000000\,1700\.000000\,1750\.000000\,1800\.000000\,1850\.000000\,1900\.000000\,1950\.000000\,2000\.000000\,2050\.000000\,2100\.000000\,2150\.000000\,2200\.000000\,2250\.000000\,2300\.000000\,2350\.000000\,2400\.000000\,2450\.000000\,2500\.000000\,2550\.000000\,2600\.000000\,2650\.000000\,2700\.000000\,2750\.000000\,2800\.000000\,2850\.000000\,2900\.000000\,2950\.000000\,3000\.000000\,3050\.000000\,3100\.000000\,3150\.000000\,3200\.000000\,3250\.000000\,3300\.000000\,3350\.000000\,3400\.000000\,3450\.000000\,3500\.000000\,3550\.000000\,3600\.000000\,3650\.000000\,3700\.000000\,3750\.000000\,3800\.000000\,3850\.000000\,3900\.000000\,3950\.000000\,4000\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,20000\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,20000\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,20000\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Gene\ Profile\"\,xlab\=\"Upstream\ \(bp\)\,\ 3000\ bp\ of\ Meta\-gene\,\ Downstream\ \(bp\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ abline\(v\=0\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ abline\(v\=3000\.000000\,lty\=2\,col\=c\(\"black\"\)\) -\>\ x\<\-c\(0\.000000\,3\.333333\,6\.666667\,10\.000000\,13\.333333\,16\.666667\,20\.000000\,23\.333333\,26\.666667\,30\.000000\,33\.333333\,36\.666667\,40\.000000\,43\.333333\,46\.666667\,50\.000000\,53\.333333\,56\.666667\,60\.000000\,63\.333333\,66\.666667\,70\.000000\,73\.333333\,76\.666667\,80\.000000\,83\.333333\,86\.666667\,90\.000000\,93\.333333\,96\.666667\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,20000\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Concatenated\ Exon\ Profile\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,20000\.000000\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ x\<\-c\(0\.000000\,3\.333333\,6\.666667\,10\.000000\,13\.333333\,16\.666667\,20\.000000\,23\.333333\,26\.666667\,30\.000000\,33\.333333\,36\.666667\,40\.000000\,43\.333333\,46\.666667\,50\.000000\,53\.333333\,56\.666667\,60\.000000\,63\.333333\,66\.666667\,70\.000000\,73\.333333\,76\.666667\,80\.000000\,83\.333333\,86\.666667\,90\.000000\,93\.333333\,96\.666667\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Concatenated\ Intron\ Profile\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,20000\.000000\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ par\(mfrow\=c\(3\,\ 2\)\,mar\=c\(4\,\ 4\,\ 5\,\ 3\.8\)\,oma\=c\(4\,\ 2\,\ 4\,\ 2\)\) -\>\ x\<\-c\(\-3\.000000\,3\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(\-3\.000000\,3\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ x\<\-c\(\-3\.000000\,3\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(\-3\.000000\,3\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ x\<\-c\(\-3\.000000\,3\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(\-3\.000000\,3\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ x\<\-c\(\-3\.000000\,3\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(\-3\.000000\,3\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ x\<\-c\(\-3\.000000\,3\.000000\) -\>\ y\<\-c\(0\.000000\,1\.000000\) -\>\ plot\(x\,\ y\,type\=\"n\"\,main\=\"\"\,xlab\=\"\"\,ylab\=\"\"\,xlim\=c\(\-3\.000000\,3\.000000\)\,ylim\=c\(0\.000000\,1\.000000\)\,axes\=FALSE\,xaxt\=\"s\"\,yaxt\=\"s\"\) -\>\ x\<\-c\(0\.000000\,4\.761905\,9\.523810\,14\.285714\,19\.047619\,23\.809524\,28\.571429\,33\.333333\,38\.095238\,42\.857143\,47\.619048\,52\.380952\,57\.142857\,61\.904762\,66\.666667\,71\.428571\,76\.190476\,80\.952381\,85\.714286\,90\.476190\,95\.238095\,100\.000000\) -\>\ y\<\-c\(0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,0\.000000\,20000\.000000\,0\.000000\) -\>\ plot\(x\,\ y\,type\=\"l\"\,main\=\"Average\ Intron\ Profile -\+\ \(685\ \<\=\ length\ \<\ 2653\ bp\)\"\,xlab\=\"Relative\ Location\ \(\%\)\"\,ylab\=\"Average\ Profile\"\,col\=c\(\"\#C8524D\"\)\,ylim\=c\(0\.000000\,24000\.000000\)\,xaxt\=\"s\"\,yaxt\=\"s\"\,lwd\=2\) -\>\ dev\.off\(\) -null\ device\ -\ \ \ \ \ \ \ \ \ \ 1\ -\>\ -INFO\ \ \@\ .*\ \#\.\.\.\ cong\!\ See\ ceas\.pdf\ for\ the\ graphical\ results\ of\ CEAS\!\
--- a/ceas-example/test-data/ceas_out3.xls Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,133 +0,0 @@ -# RefSeq: RefSeq ID -# chr: chromosome of a RefSeq gene -# txStart: 5' end of a RefSeq gene -# txEnd: 3' end site of a RefSeq gene -# strand: strand of a RefSeq gene -# dist u TSS: Distance to the nearest ChIP region's center upstream of transcription start site (bp) -# dist d TSS: Distance to the nearest ChIP region's center downstream of transcription start site (bp) -# dist u TTS: Distance to the nearest ChIP region's center upstream of transcription end site (bp) -# dist d TTS: Distance to the nearest ChIP region's center downstream of transcription end (bp) -# 3000bp u TSS: Occupancy rate of ChIP region in 3000bp upstream of transcription start site (0.0 - 1.0) -# 3000bp d TSS: Occupancy rate of ChIP region in 3000bp downstream of transcription start site (0.0 - 1.0) -# 1/3 gene: Occupancy rate of ChIP region in 1/3 gene (0.0 - 1.0) -# 2/3 gene: Occupancy rate of ChIP region in 2/3 gene (0.0 - 1.0) -# 3/3 gene: Occupancy rate of ChIP region in 3/3 gene (0.0 - 1.0) -# 3000bp d TTS: Occupancy rate of ChIP region in 3000bp downstream of transcriptino end (0.0 - 1.0) -# exons: Occupancy rate of ChIP regions in exons (0.0-1.0) -# Note that txStart and txEnd indicate 5' and 3' ends of genes whereas TSS and TTS transcription start and end sites in consideration of strand. -#name chr txStart txEnd strand dist u TSS dist d TSS dist u TTS dist d TTS 3000bp u TSS 3000bp d TSS 1/3 gene 2/3 gene 3/3 gene 3000bp d TTS exons -NM_001031576 chr26 19281 27136 + NA 4099848 NA 4091993 0 0 0 0 0 0 0 -NM_204615 chr26 57466 61594 - 4057535 NA 4061663 NA 0 0 0 0 0 0 0 -NM_001005431 chr26 65800 76175 - 4042954 NA 4053329 NA 0 0 0 0 0 0 0 -NM_001145491 chr26 91618 92166 - 4026963 NA 4027511 NA 0 0 0 0 0 0 0 -NM_204398 chr26 93069 97423 + NA 4026060 NA 4021706 0 0 0 0 0 0 0 -NM_001305147 chr26 254661 282571 + NA 3864468 NA 3836558 0 0 0 0 0 0 0 -NM_001305148 chr26 254661 282571 + NA 3864468 NA 3836558 0 0 0 0 0 0 0 -NM_001012868 chr26 350397 355252 - 3763877 NA 3768732 NA 0 0 0 0 0 0 0 -NM_001031030 chr26 479573 493561 + NA 3639556 NA 3625568 0 0 0 0 0 0 0 -NM_001305140 chr26 520705 526012 + NA 3598424 NA 3593117 0 0 0 0 0 0 0 -NM_001031029 chr26 537101 565572 + NA 3582028 NA 3553557 0 0 0 0 0 0 0 -NM_205248 chr26 537250 760479 + NA 3581879 NA 3358650 0 0 0 0 0 0 0 -NM_204727 chr26 662969 683066 - 3436063 NA 3456160 NA 0 0 0 0 0 0 0 -NR_105475 chr26 669012 669122 - 3450007 NA 3450117 NA 0 0 0 0 0 0 0 -NM_205449 chr26 785617 794076 + NA 3333512 NA 3325053 0 0 0 0 0 0 0 -NM_204681 chr26 897458 902049 - 3217080 NA 3221671 NA 0 0 0 0 0 0 0 -NM_001278156 chr26 905313 917094 - 3202035 NA 3213816 NA 0 0 0 0 0 0 0 -NM_204184 chr26 960209 964268 - 3154861 NA 3158920 NA 0 0 0 0 0 0 0 -NM_204316 chr26 974223 991244 + NA 3144906 NA 3127885 0 0 0 0 0 0 0 -NM_001031028 chr26 993815 1003847 - 3115282 NA 3125314 NA 0 0 0 0 0 0 0 -NM_001006392 chr26 1024606 1047756 - 3071373 NA 3094523 NA 0 0 0 0 0 0 0 -NM_001039596 chr26 1073550 1080033 - 3039096 NA 3045579 NA 0 0 0 0 0 0 0 -NM_001031027 chr26 1090460 1096137 + NA 3028669 NA 3022992 0 0 0 0 0 0 0 -NM_001031026 chr26 1096566 1104751 - 3014378 NA 3022563 NA 0 0 0 0 0 0 0 -NM_001030913 chr26 1122296 1132596 + NA 2996833 NA 2986533 0 0 0 0 0 0 0 -NM_001171886 chr26 1220031 1222791 - 2896338 NA 2899098 NA 0 0 0 0 0 0 0 -NM_205054 chr26 1229703 1232833 + NA 2889426 NA 2886296 0 0 0 0 0 0 0 -NM_204462 chr26 1234686 1236536 - 2882593 NA 2884443 NA 0 0 0 0 0 0 0 -NM_204463 chr26 1265724 1267989 - 2851140 NA 2853405 NA 0 0 0 0 0 0 0 -NM_001030378 chr26 1289091 1290386 - 2828743 NA 2830038 NA 0 0 0 0 0 0 0 -NM_001195554 chr26 1382510 1388447 + NA 2736619 NA 2730682 0 0 0 0 0 0 0 -NM_001012548 chr26 1406905 1428443 + NA 2712224 NA 2690686 0 0 0 0 0 0 0 -NR_031486 chr26 1442696 1442779 - 2676350 NA 2676433 NA 0 0 0 0 0 0 0 -NR_031487 chr26 1442896 1442979 - 2676150 NA 2676233 NA 0 0 0 0 0 0 0 -NM_205250 chr26 1472137 1474003 + NA 2646992 NA 2645126 0 0 0 0 0 0 0 -NR_105486 chr26 1566398 1566508 - 2552621 NA 2552731 NA 0 0 0 0 0 0 0 -NM_001160320 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004709 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160324 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004493 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160323 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160322 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001160321 chr26 1697226 1773801 + NA 2421903 NA 2345328 0 0 0 0 0 0 0 -NM_001004395 chr26 1776431 1800083 + NA 2342698 NA 2319046 0 0 0 0 0 0 0 -NM_001030914 chr26 1811042 1820368 - 2298761 NA 2308087 NA 0 0 0 0 0 0 0 -NM_204506 chr26 1823407 1843085 - 2276044 NA 2295722 NA 0 0 0 0 0 0 0 -NR_031488 chr26 1925941 1926037 - 2193092 NA 2193188 NA 0 0 0 0 0 0 0 -NM_213581 chr26 2070404 2084478 - 2034651 NA 2048725 NA 0 0 0 0 0 0 0 -NR_035298 chr26 2086590 2086691 - 2032438 NA 2032539 NA 0 0 0 0 0 0 0 -NR_105470 chr26 2094750 2094860 - 2024269 NA 2024379 NA 0 0 0 0 0 0 0 -NM_001030915 chr26 2117140 2128322 - 1990807 NA 2001989 NA 0 0 0 0 0 0 0 -NM_001031498 chr26 2175178 2177159 - 1941970 NA 1943951 NA 0 0 0 0 0 0 0 -NM_001008452 chr26 2305308 2315138 + NA 1813821 NA 1803991 0 0 0 0 0 0 0 -NM_001006322 chr26 2315293 2325130 - 1793999 NA 1803836 NA 0 0 0 0 0 0 0 -NM_001004414 chr26 2373245 2375480 - 1743649 NA 1745884 NA 0 0 0 0 0 0 0 -NM_001044644 chr26 2390696 2401255 - 1717874 NA 1728433 NA 0 0 0 0 0 0 0 -NM_001031499 chr26 2425841 2429413 - 1689716 NA 1693288 NA 0 0 0 0 0 0 0 -NM_001033642 chr26 2445710 2453125 + NA 1673419 NA 1666004 0 0 0 0 0 0 0 -NM_001033643 chr26 2469318 2475028 + NA 1649811 NA 1644101 0 0 0 0 0 0 0 -NM_204664 chr26 2498398 2509349 + NA 1620731 NA 1609780 0 0 0 0 0 0 0 -NR_031489 chr26 2511657 2511746 - 1607383 NA 1607472 NA 0 0 0 0 0 0 0 -NR_031490 chr26 2512568 2512648 - 1606481 NA 1606561 NA 0 0 0 0 0 0 0 -NR_105523 chr26 2669792 2669902 - 1449227 NA 1449337 NA 0 0 0 0 0 0 0 -NR_031491 chr26 2896046 2896142 + NA 1223083 NA 1222987 0 0 0 0 0 0 0 -NM_001190924 chr26 2961382 2962268 + NA 1157747 NA 1156861 0 0 0 0 0 0 0 -NM_001007881 chr26 2999189 3002725 + NA 1119940 NA 1116404 0 0 0 0 0 0 0 -NM_204320 chr26 3006741 3011817 - 1107312 NA 1112388 NA 0 0 0 0 0 0 0 -NM_001030916 chr26 3035271 3039335 - 1079794 NA 1083858 NA 0 0 0 0 0 0 0 -NM_204151 chr26 3047964 3050306 - 1068823 NA 1071165 NA 0 0 0 0 0 0 0 -NM_204326 chr26 3124816 3214381 - 904748 NA 994313 NA 0 0 0 0 0 0 0 -NM_204336 chr26 3320769 3332327 + NA 798360 NA 786802 0 0 0 0 0 0 0 -NM_204622 chr26 3339753 3358863 - 760266 NA 779376 NA 0 0 0 0 0 0 0 -NM_205515 chr26 3359016 3368964 + NA 760113 NA 750165 0 0 0 0 0 0 0 -NM_001012843 chr26 3370712 3377196 + NA 748417 NA 741933 0 0 0 0 0 0 0 -NM_001029849 chr26 3377655 3382628 - 736501 NA 741474 NA 0 0 0 0 0 0 0 -NM_001006323 chr26 3439841 3454783 - 664346 NA 679288 NA 0 0 0 0 0 0 0 -NR_035162 chr26 3455233 3455324 + NA 663896 NA 663805 0 0 0 0 0 0 0 -NM_001012697 chr26 3516478 3545774 + NA 602651 NA 573355 0 0 0 0 0 0 0 -NM_001030917 chr26 3590932 3597509 - 521620 NA 528197 NA 0 0 0 0 0 0 0 -NM_001031500 chr26 3597231 3600802 + NA 521898 NA 518327 0 0 0 0 0 0 0 -NM_001040018 chr26 3629575 3631171 + NA 489554 NA 487958 0 0 0 0 0 0 0 -NM_001257295 chr26 3698350 3701362 - 417767 NA 420779 NA 0 0 0 0 0 0 0 -NM_001257296 chr26 3701377 3715857 - 403272 NA 417752 NA 0 0 0 0 0 0 0 -NM_001012549 chr26 3735643 3742472 - 376657 NA 383486 NA 0 0 0 0 0 0 0 -NM_001030918 chr26 3742618 3760175 - 358954 NA 376511 NA 0 0 0 0 0 0 0 -NM_001006324 chr26 3760758 3765368 - 353761 NA 358371 NA 0 0 0 0 0 0 0 -NM_205063 chr26 3809805 3812700 + NA 309324 NA 306429 0 0 0 0 0 0 0 -NM_001293109 chr26 3859074 3879130 - 239999 NA 260055 NA 0 0 0 0 0 0 0 -NM_001293108 chr26 3859074 3882051 - 237078 NA 260055 NA 0 0 0 0 0 0 0 -NR_102328 chr26 3916006 3918143 - 200986 NA 203123 NA 0 0 0 0 0 0 0 -NM_204728 chr26 3920817 3937442 - 181687 NA 198312 NA 0 0 0 0 0 0 0 -NM_001244905 chr26 4104910 4108376 + NA 14219 NA 10753 0 0 0 0 0 0 0 -NM_001293166 chr26 4138324 4142325 - NA 23196 NA 19195 0 0 0 0 0 0 0 -NM_001030919 chr26 4144091 4175943 + 24962 NA 56814 NA 0 0 0 0 0 0 0 -NM_001257297 chr26 4209891 4216177 + 90762 NA 97048 NA 0 0 0 0 0 0 0 -NM_001257298 chr26 4218028 4238067 + 98899 NA 118938 NA 0 0 0 0 0 0 0 -NM_205490 chr26 4375371 4380959 - NA 261830 NA 256242 0 0 0 0 0 0 0 -NM_001305129 chr26 4391940 4397490 + 272811 NA 278361 NA 0 0 0 0 0 0 0 -NM_001271612 chr26 4433568 4438784 + 314439 NA 319655 NA 0 0 0 0 0 0 0 -NM_001030920 chr26 4498991 4730550 + 379862 NA 611421 NA 0 0 0 0 0 0 0 -NM_001030921 chr26 4541748 4544997 - NA 425868 NA 422619 0 0 0 0 0 0 0 -NM_001006325 chr26 4548211 4559974 + 429082 NA 440845 NA 0 0 0 0 0 0 0 -NM_001037832 chr26 4571684 4576072 - NA 456943 NA 452555 0 0 0 0 0 0 0 -NM_001080870 chr26 4578266 4580646 - NA 461517 NA 459137 0 0 0 0 0 0 0 -NM_001080868 chr26 4578266 4580647 - NA 461518 NA 459137 0 0 0 0 0 0 0 -NM_001030922 chr26 4730394 4744364 - NA 625235 NA 611265 0 0 0 0 0 0 0 -NM_204877 chr26 4751619 4755464 - NA 636335 NA 632490 0 0 0 0 0 0 0 -NM_001302134 chr26 4791084 4792014 + 671955 NA 672885 NA 0 0 0 0 0 0 0 -NM_001006327 chr26 4828534 4833077 - NA 713948 NA 709405 0 0 0 0 0 0 0 -NM_001008453 chr26 4838545 4850970 - NA 731841 NA 719416 0 0 0 0 0 0 0 -NM_001030923 chr26 4876559 4884910 + 757430 NA 765781 NA 0 0 0 0 0 0 0 -NM_204429 chr26 4897094 4901738 - NA 782609 NA 777965 0 0 0 0 0 0 0 -NM_204967 chr26 4946735 4952662 - NA 833533 NA 827606 0 0 0 0 0 0 0 -NM_204473 chr26 4990765 4993729 + 871636 NA 874600 NA 0 0 0 0 0 0 0 -NR_105623 chr26 5087926 5087980 - NA 968851 NA 968797 0 0 0 0 0 0 0
--- a/ceas-example/test-data/galGal3.len Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,57 +0,0 @@ -chr1 200994015 -chr2 154873767 -chr3 113657789 -chr4 94230402 -chrZ 74602320 -chrUn_random 63870806 -chr5 62238931 -chr7 38384769 -chr6 37400442 -chr8 30671729 -chr9 25554352 -chr10 22556432 -chr11 21928095 -chr12 20536687 -chr13 18911934 -chr14 15819469 -chr20 13986235 -chr15 12968165 -chr17 11182526 -chr18 10925261 -chr19 9939723 -chr21 6959642 -chr24 6400109 -chr23 6042217 -chr26 5102438 -chr27 4841970 -chr28 4512026 -chr22 3936574 -chr25 2031799 -chrE22C19W28_E50C23 895237 -chrW_random 729481 -chrE64_random 557643 -chr16 432983 -chr8_random 420759 -chrZ_random 346234 -chrW 259642 -chr16_random 246252 -chr1_random 222095 -chrE22C19W28_E50C23_random 191099 -chr4_random 180706 -chr22_random 156216 -chr2_random 142837 -chr28_random 105415 -chr7_random 104000 -chr25_random 80372 -chr20_random 75095 -chr11_random 72667 -chrE64 49846 -chr13_random 35775 -chr6_random 34212 -chr5_random 27907 -chrM 16775 -chr10_random 13679 -chr18_random 11891 -chr12_random 7060 -chr17_random 2911 -chr32 1028
--- a/ceas-example/tool-data/ceas.loc.sample Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,17 +0,0 @@ -#This is a sample file distributed with Galaxy that is used by the -#ceas tool. The ceas.loc file has this format (white space -#characters are TAB characters): -# -#<unique_build> <dbkey> <Description> <PathToGdbFile> -# -#For example: -# -#hg18_illumina_pe hg18 Human (hg18) /home/galaxy/genomes/ceaslib/GeneTable/hg18 -#mm9_generic mm9 Mouse (mm9) /home/galaxy/genomes/ceaslib/GeneTable/mm9 -#...etc... -# -# The GDB files are sqlite databases containing the RefSeq genes for -# the organism in question -# -#This file should be placed in galaxy's tool-data directory when the -#ceas tool is installed.
--- a/ceas-example/tool_data_table_conf.xml.sample Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,6 +0,0 @@ -<tables> - <table name="ceas_annotations" comment_char="#"> - <columns>value, dbkey, name, path</columns> - <file path="tool-data/ceas.loc" /> - </table> -</tables> \ No newline at end of file
--- a/ceas-example/tool_data_table_conf.xml.test Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,6 +0,0 @@ -<tables> - <table name="ceas_annotations" comment_char="#"> - <columns>value, dbkey, name, path</columns> - <file path="${__HERE__}/test-data/ceas.loc" /> - </table> -</tables> \ No newline at end of file
--- a/ceas-example/tool_dependencies.xml Sun Nov 20 22:51:03 2016 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,87 +0,0 @@ -<?xml version="1.0"?> -<tool_dependency> - <!-- R 3.1.2 from main/test toolshed --> - <package name="R" version="3.1.2"> - <repository changeset_revision="1ca39eb16186" name="package_r_3_1_2" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> - </package> - <!-- bx_python from main/test toolshed --> - <package name="bx-python" version="0.7.1"> - <repository changeset_revision="6a811020ff2f" name="package_bx_python_0_7" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> - </package> - <!-- Python mysqldb package --> - <package name="python_mysqldb" version="1.2.5"> - <install version="1.0"> - <actions> - <action type="download_by_url">https://pypi.python.org/packages/source/M/MySQL-python/MySQL-python-1.2.5.zip</action> - <action type="make_directory">$INSTALL_DIR/lib/python</action> - <action type="shell_command"> - export PYTHONPATH=$PYTHONPATH:$INSTALL_DIR/lib/python && - python setup.py install --install-lib $INSTALL_DIR/lib/python --install-scripts $INSTALL_DIR/bin - </action> - <action type="set_environment"> - <environment_variable action="prepend_to" name="PYTHONPATH">$INSTALL_DIR/lib/python</environment_variable> - <environment_variable action="prepend_to" name="PATH">$INSTALL_DIR/bin</environment_variable> - </action> - </actions> - </install> - <readme>Installs Python module MySQLdb 1.2.5</readme> - </package> - <!-- cistrome_ceas - Installs the version of CEAS package found in the Cistrome - distribution: - https://bitbucket.org/cistrome/cistrome-applications-harvard/overview - --> - <package name="cistrome_ceas" version="1.0.2.d8c0751"> - <install version="1.0"> - <actions> - <action type="shell_command"> - hg clone https://bitbucket.org/cistrome/cistrome-applications-harvard cistrome_ceas - </action> - <action type="shell_command"> - hg update d8c0751 - </action> - <action type="make_directory">$INSTALL_DIR/lib/python</action> - <action type="shell_command"> - cd published-packages/CEAS/ - export PYTHONPATH=$PYTHONPATH:$INSTALL_DIR/lib/python && - python setup.py install --install-lib $INSTALL_DIR/lib/python --install-scripts $INSTALL_DIR/bin - </action> - <action type="set_environment"> - <environment_variable action="prepend_to" name="PYTHONPATH">$INSTALL_DIR/lib/python</environment_variable> - <environment_variable action="prepend_to" name="PATH">$INSTALL_DIR/bin</environment_variable> - </action> - </actions> - </install> - <readme>Installs version 1.0.2 of CEAS from cistrome (commit id d8c0751, - datestamp 20140929), which includes ceasBW (a version of ceas which can - handle bigWig file input from MACS2. - - Cistrome code is at - https://bitbucket.org/cistrome/cistrome-applications-harvard/overview - - The CEAS code is under the published-packages/CEAS/ subdirectory - - Cistrome data files and documentation can be found at - http://liulab.dfci.harvard.edu/CEAS/index.html - </readme> - </package> - <!-- fetchChromSize from UCSC tools --> - <package name="ucsc_fetchChromSizes" version="1.0"> - <install version="1.0"> - <actions> - <action type="download_binary"> - <url_template architecture="x86_64" os="linux">http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/fetchChromSizes</url_template> - </action> - <action type="chmod"> - <file mode="755">$INSTALL_DIR/fetchChromSizes</file> - </action> - <action type="set_environment"> - <environment_variable action="prepend_to" name="PATH">$INSTALL_DIR</environment_variable> - </action> - </actions> - </install> - <readme>Installs the binary executable for the fetchChromSizes utility - from UCSC tools - </readme> - </package> -</tool_dependency>