# HG changeset patch
# User lparsons
# Date 1436279987 14400
# Node ID 30612763595927dcedf982a63fedafd6cfc25ff9
planemo upload for repository https://github.com/lparsons/galaxy_tools/tree/master/tools/rseqc commit aeb25d807817746dd6957f30ce2070662cc10e91
diff -r 000000000000 -r 306127635959 README.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.txt Tue Jul 07 10:39:47 2015 -0400
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+== RSeQC Galaxy Wrapper ==
+
+This is a Galaxy wrapper for the RSeQC RNA-Seq QC package.
+
+** Installation **
+
+Installation from a tool shed provides the necessary tool dependencies, R, numpy, and RSeQC.
+
+Otherwise, make sure that R and the RSeQC scripts are in the path and run under the Galaxy environment.
+Move the xml files to a subdirectory of your tools directory and add lines in tool_conf.xml to point to them.
+Restart the Galaxy server.
+
+Requires Python 2.7
+
+** Attribution **
+
+The RSeQC package and associated documentation can be found at: http://rseqc.sourceforge.net/
+
+The galaxy wrapper code was written by
+ Nilesh Kavthekar, School of Engineering and Applied Sciences, University of Pennsylvania, Class of 2016
+Modified by
+ Lance Parsons, Lewis-Sigler Institute for Integrative Genomics, Princeton University,
+ Bjorn Gruning, University of Freiburg, bjoern.gruening@gmail.com
+The development of the wrapper code is housed on BitBucket at: https://bitbucket.org/lance_parsons/rseqc_galaxy_wrapper
diff -r 000000000000 -r 306127635959 RPKM_count.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/RPKM_count.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ calculates raw count and RPKM values for transcript at exon, intron, and mRNA level
+
+
+ rseqc_macros.xml
+
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diff -r 000000000000 -r 306127635959 RPKM_saturation.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/RPKM_saturation.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ calculates raw count and RPKM values for transcript at exon, intron, and mRNA level
+
+
+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 bam2wig.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/bam2wig.xml Tue Jul 07 10:39:47 2015 -0400
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+
+
+ converts all types of RNA-seq data from .bam to .wig
+
+
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+ rseqc_macros.xml
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+ &1
+ ]]>
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+ strand_type['strand_specific'] == 'none'
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+ strand_type['strand_specific'] != 'none'
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+ strand_type['strand_specific'] != 'none'
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diff -r 000000000000 -r 306127635959 bam_stat.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/bam_stat.xml Tue Jul 07 10:39:47 2015 -0400
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+
+
+ reads mapping statistics for a provided BAM or SAM file.
+
+
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+ rseqc_macros.xml
+
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+ $output
+ ]]>
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diff -r 000000000000 -r 306127635959 clipping_profile.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/clipping_profile.xml Tue Jul 07 10:39:47 2015 -0400
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+
+
+ estimates clipping profile of RNA-seq reads from BAM or SAM file
+
+
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+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 geneBody_coverage.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/geneBody_coverage.xml Tue Jul 07 10:39:47 2015 -0400
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+
+
+ Read coverage over gene body.
+
+
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+ rseqc_macros.xml
+
+
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+ input_list.txt &&
+ #for $i, $additional_input in enumerate($additionalinputs):
+ #set $index = $i+2
+ #set $safename = ''.join(c in '_0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ' and c or '_' for c in $additional_input.file.display_name)
+ #set $fname = 'd' + str($index) + '_' + str($safename) + ".bam"
+ ln -s '$additional_input.file' '${fname}' &&
+ ln -s '$additional_input.file.metadata.bam_index' '${fname}.bai' &&
+ echo '${fname}' >> input_list.txt &&
+ #end for
+ geneBody_coverage.py -i input_list.txt -r $refgene --minimum_length $minimum_length -o output
+ ]]>
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+ len(additionalinputs) >= 2
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+ `_
+
+ .. image:: http://rseqc.sourceforge.net/_images/geneBody_workflow.png
+ :width: 800 px
+ :scale: 80 %
+
+
+Inputs
+++++++++++++++
+
+Input BAM/SAM file
+ Alignment file in BAM/SAM format.
+
+Reference gene model
+ Gene Model in BED format.
+
+Minimum mRNA length
+ Minimum mRNA length (bp). mRNA that are shorter than this value will be skipped (default is 100).
+
+Outputs
+++++++++++++++
+Text
+ Table that includes the data used to generate the plots
+
+R Script
+ R script file that reads the data and generates the plot
+
+PDF
+ The final plot, in PDF format
+
+Example plots:
+ .. image:: http://rseqc.sourceforge.net/_images/Aug_26.geneBodyCoverage.curves.png
+ :height: 600 px
+ :width: 600 px
+ :scale: 80 %
+
+ .. image:: http://rseqc.sourceforge.net/_images/Aug_26.geneBodyCoverage.heatMap.png
+ :height: 600 px
+ :width: 600 px
+ :scale: 80 %
+
+-----
+
+About RSeQC
++++++++++++
+
+The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation.
+
+The RSeQC package is licensed under the GNU GPL v3 license.
+
+.. image:: http://rseqc.sourceforge.net/_static/logo.png
+
+.. _RSeQC: http://rseqc.sourceforge.net/
+]]>
+
+
+
+
+
diff -r 000000000000 -r 306127635959 geneBody_coverage2.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/geneBody_coverage2.xml Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,95 @@
+
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+ Read coverage over gene body
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+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 infer_experiment.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/infer_experiment.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ speculates how RNA-seq were configured
+
+
+ rseqc_macros.xml
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+ $output
+ ]]>
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diff -r 000000000000 -r 306127635959 inner_distance.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/inner_distance.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ calculate the inner distance (or insert size) between two paired RNA reads
+
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+ rseqc_macros.xml
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+ > Read2_end due to spliced mapping of read1)
+ - third column indicates how paired reads were mapped: PE_within_same_exon, PE_within_diff_exon,PE_reads_overlap
+2. output..inner_distance_freq.txt:
+ - inner distance starts
+ - inner distance ends
+ - number of read pairs
+ - note the first 2 columns are left side half open interval
+3. output.inner_distance_plot.r: R script to generate histogram
+4. output.inner_distance_plot.pdf: histogram plot
+
+.. image:: http://rseqc.sourceforge.net/_images/inner_distance.png
+ :height: 600 px
+ :width: 600 px
+ :scale: 80 %
+
+
+-----
+
+About RSeQC
++++++++++++
+
+The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation.
+
+The RSeQC package is licensed under the GNU GPL v3 license.
+
+.. image:: http://rseqc.sourceforge.net/_static/logo.png
+
+.. _RSeQC: http://rseqc.sourceforge.net/
+
+]]>
+
+
+
+
+
diff -r 000000000000 -r 306127635959 junction_annotation.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/junction_annotation.xml Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,119 @@
+
+ compares detected splice junctions to reference gene model
+
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+ rseqc_macros.xml
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+ = 100 splicing events.
+* splice junction: multiple splicing events spanning the same intron can be consolidated into one splicing junction.
+
+All detected junctions can be grouped to 3 exclusive categories:
+
+1. Annotated: The junction is part of the gene model. Both splice sites, 5' splice site
+ (5'SS) and 3'splice site (3'SS) can be annotated by reference gene model.
+2. complete_novel: Complete new junction. Neither of the two splice sites cannot be annotated by gene model
+3. partial_novel: One of the splice site (5'SS or 3'SS) is new, while the other splice site is annotated (known)
+
+Inputs
+++++++++++++++
+
+Input BAM/SAM file
+ Alignment file in BAM/SAM format.
+
+Reference gene model
+ Gene model in BED format.
+
+Minimum intron length (default=50)
+ Minimum intron length (bp).
+
+
+Output
+++++++++++++++
+
+1. output.junc.anno.junction.xls:
+ - chrom ID
+ - start position of junction (coordinate is 0 based)
+ - end position of junction (coordinate is 1 based)
+ - number of splice events supporting this junction
+ - 'annotated', 'complete_novel' or 'partial_novel'.
+2. output.anno.junction_plot.r: R script to generate pie chart
+3. output.splice_junction.pdf: plot of splice junctions
+4. output.splice_events.pdf: plot of splice events
+
+.. image:: http://rseqc.sourceforge.net/_images/junction.png
+ :height: 400 px
+ :width: 850 px
+ :scale: 80 %
+
+-----
+
+About RSeQC
++++++++++++
+
+The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation.
+
+The RSeQC package is licensed under the GNU GPL v3 license.
+
+.. image:: http://rseqc.sourceforge.net/_static/logo.png
+
+.. _RSeQC: http://rseqc.sourceforge.net/
+]]>
+
+
+
+
+
diff -r 000000000000 -r 306127635959 junction_saturation.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/junction_saturation.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ detects splice junctions from each subset and compares them to reference gene model
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+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 read_GC.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/read_GC.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ determines GC% and read count
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+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 read_NVC.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/read_NVC.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ to check the nucleotide composition bias
+
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+ rseqc_macros.xml
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+ read_NVC.py
+ --input-file $input
+ --out-prefix output
+ $nx
+ --mapq $mapq
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diff -r 000000000000 -r 306127635959 read_distribution.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/read_distribution.xml Tue Jul 07 10:39:47 2015 -0400
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+
+ calculates how mapped reads were distributed over genome feature
+
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+ rseqc_macros.xml
+
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+ $output
+ ]]>
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+ UTR exons > Introns > Intergenic regions, for example, if a read was mapped to
+both CDS exon and intron, it will be assigned to CDS exons.
+
+* "Total Reads": This does NOT include those QC fail,duplicate and non-primary hit reads
+* "Total Tags": reads spliced once will be counted as 2 tags, reads spliced twice will be counted as 3 tags, etc. And because of this, "Total Tags" >= "Total Reads"
+* "Total Assigned Tags": number of tags that can be unambiguously assigned the 10 groups (see below table).
+* Tags assigned to "TSS_up_1kb" were also assigned to "TSS_up_5kb" and "TSS_up_10kb", tags assigned to "TSS_up_5kb" were also assigned to "TSS_up_10kb". Therefore, "Total Assigned Tags" = CDS_Exons + 5'UTR_Exons + 3'UTR_Exons + Introns + TSS_up_10kb + TES_down_10kb.
+* When assign tags to genome features, each tag is represented by its middle point.
+
+RSeQC cannot assign those reads that:
+
+* hit to intergenic regions that beyond region starting from TSS upstream 10Kb to TES downstream 10Kb.
+* hit to regions covered by both 5'UTR and 3' UTR. This is possible when two head-to-tail transcripts are overlapped in UTR regions.
+* hit to regions covered by both TSS upstream 10Kb and TES downstream 10Kb.
+
+
+Inputs
+++++++++++++++
+
+Input BAM/SAM file
+ Alignment file in BAM/SAM format.
+
+Reference gene model
+ Gene model in BED format.
+
+Sample Output
+++++++++++++++
+
+Output:
+
+=============== ============ =========== ===========
+Group Total_bases Tag_count Tags/Kb
+=============== ============ =========== ===========
+CDS_Exons 33302033 20002271 600.63
+5'UTR_Exons 21717577 4408991 203.01
+3'UTR_Exons 15347845 3643326 237.38
+Introns 1132597354 6325392 5.58
+TSS_up_1kb 17957047 215331 11.99
+TSS_up_5kb 81621382 392296 4.81
+TSS_up_10kb 149730983 769231 5.14
+TES_down_1kb 18298543 266161 14.55
+TES_down_5kb 78900674 729997 9.25
+TES_down_10kb 140361190 896882 6.39
+=============== ============ =========== ===========
+
+-----
+
+About RSeQC
++++++++++++
+
+The RSeQC_ package provides a number of useful modules that can comprehensively evaluate high throughput sequence data especially RNA-seq data. "Basic modules" quickly inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while "RNA-seq specific modules" investigate sequencing saturation status of both splicing junction detection and expression estimation, mapped reads clipping profile, mapped reads distribution, coverage uniformity over gene body, reproducibility, strand specificity and splice junction annotation.
+
+The RSeQC package is licensed under the GNU GPL v3 license.
+
+.. image:: http://rseqc.sourceforge.net/_static/logo.png
+
+.. _RSeQC: http://rseqc.sourceforge.net/
+]]>
+
+
+
+
+
diff -r 000000000000 -r 306127635959 read_duplication.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/read_duplication.xml Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,92 @@
+
+ determines reads duplication rate with sequence-based and mapping-based strategies
+
+
+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 read_quality.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/read_quality.xml Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,106 @@
+
+ determines Phred quality score
+
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+ rseqc_macros.xml
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diff -r 000000000000 -r 306127635959 rseqc_macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/rseqc_macros.xml Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,42 @@
+
+
+ R
+ numpy
+ rseqc
+
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+
+ @article{wang_rseqc:_2012,
+ title = {{RSeQC}: quality control of {RNA}-seq experiments},
+ volume = {28},
+ issn = {1367-4803, 1460-2059},
+ shorttitle = {{RSeQC}},
+ url = {http://bioinformatics.oxfordjournals.org/content/28/16/2184},
+ doi = {10.1093/bioinformatics/bts356},
+ abstract = {Motivation: RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed.
+ Results: We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments.
+ Availability and implementation: RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
+ Contact: WL1\{at\}bcm.edu
+ Supplementary Information: Supplementary data are available at Bioinformatics online.},
+ language = {en},
+ number = {16},
+ urldate = {2015-06-30},
+ journal = {Bioinformatics},
+ author = {Wang, Liguo and Wang, Shengqin and Li, Wei},
+ month = aug,
+ year = {2012},
+ pmid = {22743226},
+ pages = {2184--2185},
+ }
+
+
+
+
diff -r 000000000000 -r 306127635959 test-data/bamstats.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/bamstats.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,23 @@
+Load BAM file ... Done
+
+#==================================================
+#All numbers are READ count
+#==================================================
+
+Total records: 40
+
+QC failed: 0
+Optical/PCR duplicate: 0
+Non primary hits 0
+Unmapped reads: 0
+mapq < mapq_cut (non-unique): 0
+
+mapq >= mapq_cut (unique): 40
+Read-1: 20
+Read-2: 20
+Reads map to '+': 20
+Reads map to '-': 20
+Non-splice reads: 36
+Splice reads: 4
+Reads mapped in proper pairs: 39
+Proper-paired reads map to different chrom:0
diff -r 000000000000 -r 306127635959 test-data/hg19.chrom.sizes
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hg19.chrom.sizes Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,93 @@
+chr1 249250621
+chr2 243199373
+chr3 198022430
+chr4 191154276
+chr5 180915260
+chr6 171115067
+chr7 159138663
+chrX 155270560
+chr8 146364022
+chr9 141213431
+chr10 135534747
+chr11 135006516
+chr12 133851895
+chr13 115169878
+chr14 107349540
+chr15 102531392
+chr16 90354753
+chr17 81195210
+chr18 78077248
+chr20 63025520
+chrY 59373566
+chr19 59128983
+chr22 51304566
+chr21 48129895
+chr6_ssto_hap7 4928567
+chr6_mcf_hap5 4833398
+chr6_cox_hap2 4795371
+chr6_mann_hap4 4683263
+chr6_apd_hap1 4622290
+chr6_qbl_hap6 4611984
+chr6_dbb_hap3 4610396
+chr17_ctg5_hap1 1680828
+chr4_ctg9_hap1 590426
+chr1_gl000192_random 547496
+chrUn_gl000225 211173
+chr4_gl000194_random 191469
+chr4_gl000193_random 189789
+chr9_gl000200_random 187035
+chrUn_gl000222 186861
+chrUn_gl000212 186858
+chr7_gl000195_random 182896
+chrUn_gl000223 180455
+chrUn_gl000224 179693
+chrUn_gl000219 179198
+chr17_gl000205_random 174588
+chrUn_gl000215 172545
+chrUn_gl000216 172294
+chrUn_gl000217 172149
+chr9_gl000199_random 169874
+chrUn_gl000211 166566
+chrUn_gl000213 164239
+chrUn_gl000220 161802
+chrUn_gl000218 161147
+chr19_gl000209_random 159169
+chrUn_gl000221 155397
+chrUn_gl000214 137718
+chrUn_gl000228 129120
+chrUn_gl000227 128374
+chr1_gl000191_random 106433
+chr19_gl000208_random 92689
+chr9_gl000198_random 90085
+chr17_gl000204_random 81310
+chrUn_gl000233 45941
+chrUn_gl000237 45867
+chrUn_gl000230 43691
+chrUn_gl000242 43523
+chrUn_gl000243 43341
+chrUn_gl000241 42152
+chrUn_gl000236 41934
+chrUn_gl000240 41933
+chr17_gl000206_random 41001
+chrUn_gl000232 40652
+chrUn_gl000234 40531
+chr11_gl000202_random 40103
+chrUn_gl000238 39939
+chrUn_gl000244 39929
+chrUn_gl000248 39786
+chr8_gl000196_random 38914
+chrUn_gl000249 38502
+chrUn_gl000246 38154
+chr17_gl000203_random 37498
+chr8_gl000197_random 37175
+chrUn_gl000245 36651
+chrUn_gl000247 36422
+chr9_gl000201_random 36148
+chrUn_gl000235 34474
+chrUn_gl000239 33824
+chr21_gl000210_random 27682
+chrUn_gl000231 27386
+chrUn_gl000229 19913
+chrM 16571
+chrUn_gl000226 15008
+chr18_gl000207_random 4262
diff -r 000000000000 -r 306127635959 test-data/hg19_RefSeq_chr1_1-100000.bed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hg19_RefSeq_chr1_1-100000.bed Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,7 @@
+chr1 11873 14409 NR_046018 0 + 14409 14409 0 3 354,109,1189, 0,739,1347,
+chr1 14361 29370 NR_024540 0 - 29370 29370 0 11 468,69,152,159,198,136,137,147,99,154,50, 0,608,1434,2245,2496,2871,3244,3553,3906,10376,14959,
+chr1 17368 17436 NR_106918 0 - 17436 17436 0 1 68, 0,
+chr1 17368 17436 NR_107062 0 - 17436 17436 0 1 68, 0,
+chr1 34610 36081 NR_026818 0 - 36081 36081 0 3 564,205,361, 0,666,1110,
+chr1 34610 36081 NR_026820 0 - 36081 36081 0 3 564,205,361, 0,666,1110,
+chr1 69090 70008 NM_001005484 0 + 69090 70008 0 1 918, 0,
diff -r 000000000000 -r 306127635959 test-data/output.DupRate_plot.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.DupRate_plot.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,14 @@
+pdf('output.DupRate_plot.pdf')
+par(mar=c(5,4,4,5),las=0)
+seq_occ=c(1)
+seq_uniqRead=c(40)
+pos_occ=c(1)
+pos_uniqRead=c(40)
+plot(pos_occ,log10(pos_uniqRead),ylab='Number of Reads (log10)',xlab='Frequency',pch=4,cex=0.8,col='blue',xlim=c(1,500),yaxt='n')
+points(seq_occ,log10(seq_uniqRead),pch=20,cex=0.8,col='red')
+ym=floor(max(log10(pos_uniqRead)))
+legend(300,ym,legend=c('Sequence-base','Mapping-base'),col=c('blue','red'),pch=c(4,20))
+axis(side=2,at=0:ym,labels=0:ym)
+axis(side=4,at=c(log10(pos_uniqRead[1]),log10(pos_uniqRead[2]),log10(pos_uniqRead[3]),log10(pos_uniqRead[4])), labels=c(round(pos_uniqRead[1]*100/sum(pos_uniqRead)),round(pos_uniqRead[2]*100/sum(pos_uniqRead)),round(pos_uniqRead[3]*100/sum(pos_uniqRead)),round(pos_uniqRead[4]*100/sum(pos_uniqRead))))
+mtext(4, text = "Reads %", line = 2)
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.GC.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.GC.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,19 @@
+GC% read_count
+60.78 3
+41.18 3
+47.06 5
+56.86 7
+29.41 1
+27.45 2
+37.25 2
+78.43 1
+58.82 1
+50.98 3
+49.02 2
+62.75 1
+68.63 1
+54.90 1
+52.94 3
+35.29 1
+43.14 2
+39.22 1
diff -r 000000000000 -r 306127635959 test-data/output.GC_plot.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.GC_plot.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,4 @@
+pdf("output.GC_plot.pdf")
+gc=rep(c(60.78,41.18,47.06,56.86,29.41,27.45,37.25,78.43,58.82,50.98,49.02,62.75,68.63,54.90,52.94,35.29,43.14,39.22),times=c(3,3,5,7,1,2,2,1,1,3,2,1,1,1,3,1,2,1))
+hist(gc,probability=T,breaks=100,xlab="GC content (%)",ylab="Density of Reads",border="blue",main="")
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.NVC.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.NVC.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,52 @@
+Position A C G T N X
+0 5 7 18 10 0 0
+1 6 7 15 8 4 0
+2 5 9 18 5 3 0
+3 11 9 14 4 2 0
+4 5 9 12 14 0 0
+5 4 11 19 6 0 0
+6 11 7 12 10 0 0
+7 9 8 12 9 2 0
+8 12 9 11 8 0 0
+9 8 9 8 10 5 0
+10 9 8 9 14 0 0
+11 9 6 11 14 0 0
+12 14 8 12 6 0 0
+13 10 6 9 15 0 0
+14 9 9 7 15 0 0
+15 10 10 9 9 2 0
+16 8 4 6 14 8 0
+17 9 9 10 9 3 0
+18 7 5 11 12 5 0
+19 12 8 4 10 6 0
+20 10 6 9 15 0 0
+21 9 9 15 7 0 0
+22 14 6 11 9 0 0
+23 13 11 11 5 0 0
+24 12 8 7 10 3 0
+25 9 13 4 8 6 0
+26 11 16 7 6 0 0
+27 11 8 13 8 0 0
+28 13 6 9 12 0 0
+29 9 9 12 10 0 0
+30 8 6 15 11 0 0
+31 7 9 11 13 0 0
+32 7 8 14 11 0 0
+33 11 11 10 8 0 0
+34 6 12 13 9 0 0
+35 8 17 11 4 0 0
+36 9 8 7 16 0 0
+37 11 9 12 8 0 0
+38 8 9 10 13 0 0
+39 8 12 11 9 0 0
+40 12 9 10 9 0 0
+41 9 13 11 7 0 0
+42 10 12 9 9 0 0
+43 7 13 11 9 0 0
+44 10 12 6 12 0 0
+45 10 10 9 11 0 0
+46 7 10 10 13 0 0
+47 9 9 12 10 0 0
+48 10 6 14 10 0 0
+49 8 10 13 9 0 0
+50 7 8 9 16 0 0
diff -r 000000000000 -r 306127635959 test-data/output.NVC_plot.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.NVC_plot.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,17 @@
+position=c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50)
+A_count=c(5,6,5,11,5,4,11,9,12,8,9,9,14,10,9,10,8,9,7,12,10,9,14,13,12,9,11,11,13,9,8,7,7,11,6,8,9,11,8,8,12,9,10,7,10,10,7,9,10,8,7)
+C_count=c(7,7,9,9,9,11,7,8,9,9,8,6,8,6,9,10,4,9,5,8,6,9,6,11,8,13,16,8,6,9,6,9,8,11,12,17,8,9,9,12,9,13,12,13,12,10,10,9,6,10,8)
+G_count=c(18,15,18,14,12,19,12,12,11,8,9,11,12,9,7,9,6,10,11,4,9,15,11,11,7,4,7,13,9,12,15,11,14,10,13,11,7,12,10,11,10,11,9,11,6,9,10,12,14,13,9)
+T_count=c(10,8,5,4,14,6,10,9,8,10,14,14,6,15,15,9,14,9,12,10,15,7,9,5,10,8,6,8,12,10,11,13,11,8,9,4,16,8,13,9,9,7,9,9,12,11,13,10,10,9,16)
+N_count=c(0,4,3,2,0,0,0,2,0,5,0,0,0,0,0,2,8,3,5,6,0,0,0,0,3,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
+X_count=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
+total= A_count + C_count + G_count + T_count
+ym=max(A_count/total,C_count/total,G_count/total,T_count/total) + 0.05
+yn=min(A_count/total,C_count/total,G_count/total,T_count/total)
+pdf("output.NVC_plot.pdf")
+plot(position,A_count/total,type="o",pch=20,ylim=c(yn,ym),col="dark green",xlab="Position of Read",ylab="Nucleotide Frequency")
+lines(position,T_count/total,type="o",pch=20,col="red")
+lines(position,G_count/total,type="o",pch=20,col="blue")
+lines(position,C_count/total,type="o",pch=20,col="cyan")
+legend(41,ym,legend=c("A","T","G","C"),col=c("dark green","red","blue","cyan"),lwd=2,pch=20,text.col=c("dark green","red","blue","cyan"))
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.clipping_profile.pdf
Binary file test-data/output.clipping_profile.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output.clipping_profile.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.clipping_profile.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,5 @@
+pdf("output.clipping_profile.pdf")
+read_pos=c(0,1,2,3,4,5,6,7,8,9,44,45,46,47,48,49,50)
+count=c(16,12,11,8,6,5,1,1,1,1,1,2,2,2,3,4,4)
+plot(read_pos,1-(count/40),col="blue",main="clipping profile",xlab="Position of reads",ylab="Mappability",type="b")
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.clipping_profile.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.clipping_profile.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,18 @@
+Position Read_Total Read_clipped
+0 40 16
+1 40 12
+2 40 11
+3 40 8
+4 40 6
+5 40 5
+6 40 1
+7 40 1
+8 40 1
+9 40 1
+44 40 1
+45 40 2
+46 40 2
+47 40 2
+48 40 3
+49 40 4
+50 40 4
diff -r 000000000000 -r 306127635959 test-data/output.eRPKM.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.eRPKM.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,8 @@
+#chr start end name score strand 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
+chr1 17368 17436 NR_106918 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+chr1 17368 17436 NR_107062 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+chr1 34610 36081 NR_026818 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+chr1 69090 70008 NM_001005484 0 + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+chr1 14361 29370 NR_024540 0 - 256950.511332 128475.255666 85650.1704438 128475.255666 102780.204533 85650.1704438 73414.431809 64237.6278329 57100.1136292 51390.1022663 46718.2747875 64237.6278329 59296.2718457 55060.8238568 51390.1022663 48178.2208747 45344.207882 57100.1136292 54094.8444908 51390.1022663
+chr1 34610 36081 NR_026820 0 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+chr1 11873 14409 NR_046018 0 + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30572.0644704 27514.8580233 25013.5072939 22929.0483528 42330.5508051 39306.9400333 36686.4773644 34393.5725292 32370.4212039 45858.0967056 43444.5126684 41272.287035
diff -r 000000000000 -r 306127635959 test-data/output.geneBodyCoverage.curves.pdf
Binary file test-data/output.geneBodyCoverage.curves.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output.geneBodyCoverage.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.geneBodyCoverage.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,8 @@
+d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam <- c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
+
+
+pdf("output.geneBodyCoverage.curves.pdf")
+x=1:100
+icolor = colorRampPalette(c("#7fc97f","#beaed4","#fdc086","#ffff99","#386cb0","#f0027f"))(1)
+plot(x,d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',xlab="Gene body percentile (5'->3')", ylab="Coverage",lwd=0.8,col=icolor[1])
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.geneBodyCoverage.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.geneBodyCoverage.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,2 @@
+Percentile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
+d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
diff -r 000000000000 -r 306127635959 test-data/output.infer_experiment.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.infer_experiment.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,6 @@
+
+
+This is PairEnd Data
+Fraction of reads explained by "1++,1--,2+-,2-+": 1.0000
+Fraction of reads explained by "1+-,1-+,2++,2--": 0.0000
+Fraction of reads explained by other combinations: 0.0000
diff -r 000000000000 -r 306127635959 test-data/output.inner_distance.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.inner_distance.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,20 @@
+seq.11990047 235 sameTranscript=No,dist=genomic
+seq.14614493 31 sameTranscript=Yes,sameExon=Yes,dist=mRNA
+seq.24018133 2 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.10608403 158 sameTranscript=Yes,sameExon=No,dist=mRNA
+seq.10820209 146 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.1537155 33 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.25274725 17 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.26326595 211 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.28833653 55 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.25049090 61 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.23476912 69 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.28059536 225 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.13270875 200 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.2214586 132 sameTranscript=Yes,nonExonic=Yes,dist=genomic
+seq.31061198 -31 readPairOverlap
+seq.13539256 208 sameTranscript=No,dist=genomic
+seq.13835843 -7 sameTranscript=No,dist=genomic
+seq.5556605 88 sameTranscript=No,dist=genomic
+seq.20367385 17 sameTranscript=No,dist=genomic
+seq.17373919 146394 sameTranscript=No,dist=genomic
diff -r 000000000000 -r 306127635959 test-data/output.inner_distance_freq.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.inner_distance_freq.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,100 @@
+-250 -245 0
+-245 -240 0
+-240 -235 0
+-235 -230 0
+-230 -225 0
+-225 -220 0
+-220 -215 0
+-215 -210 0
+-210 -205 0
+-205 -200 0
+-200 -195 0
+-195 -190 0
+-190 -185 0
+-185 -180 0
+-180 -175 0
+-175 -170 0
+-170 -165 0
+-165 -160 0
+-160 -155 0
+-155 -150 0
+-150 -145 0
+-145 -140 0
+-140 -135 0
+-135 -130 0
+-130 -125 0
+-125 -120 0
+-120 -115 0
+-115 -110 0
+-110 -105 0
+-105 -100 0
+-100 -95 0
+-95 -90 0
+-90 -85 0
+-85 -80 0
+-80 -75 0
+-75 -70 0
+-70 -65 0
+-65 -60 0
+-60 -55 0
+-55 -50 0
+-50 -45 0
+-45 -40 0
+-40 -35 0
+-35 -30 1
+-30 -25 0
+-25 -20 0
+-20 -15 0
+-15 -10 0
+-10 -5 1
+-5 0 0
+0 5 1
+5 10 0
+10 15 0
+15 20 2
+20 25 0
+25 30 0
+30 35 2
+35 40 0
+40 45 0
+45 50 0
+50 55 1
+55 60 0
+60 65 1
+65 70 1
+70 75 0
+75 80 0
+80 85 0
+85 90 1
+90 95 0
+95 100 0
+100 105 0
+105 110 0
+110 115 0
+115 120 0
+120 125 0
+125 130 0
+130 135 1
+135 140 0
+140 145 0
+145 150 1
+150 155 0
+155 160 1
+160 165 0
+165 170 0
+170 175 0
+175 180 0
+180 185 0
+185 190 0
+190 195 0
+195 200 1
+200 205 0
+205 210 1
+210 215 1
+215 220 0
+220 225 1
+225 230 0
+230 235 1
+235 240 0
+240 245 0
+245 250 0
diff -r 000000000000 -r 306127635959 test-data/output.inner_distance_plot.pdf
Binary file test-data/output.inner_distance_plot.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output.inner_distance_plot.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.inner_distance_plot.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,11 @@
+pdf('output.inner_distance_plot.pdf')
+fragsize=rep(c(-248,-243,-238,-233,-228,-223,-218,-213,-208,-203,-198,-193,-188,-183,-178,-173,-168,-163,-158,-153,-148,-143,-138,-133,-128,-123,-118,-113,-108,-103,-98,-93,-88,-83,-78,-73,-68,-63,-58,-53,-48,-43,-38,-33,-28,-23,-18,-13,-8,-3,2,7,12,17,22,27,32,37,42,47,52,57,62,67,72,77,82,87,92,97,102,107,112,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,192,197,202,207,212,217,222,227,232,237,242,247),times=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,2,0,0,2,0,0,0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,1,0,1,0,1,0,0,0))
+frag_sd = sd(fragsize)
+frag_mean = mean(fragsize)
+frag_median = median(fragsize)
+write(c("Mean insert size",frag_mean), stdout())
+write(c("Median insert size",frag_median), stdout())
+write(c("Standard deviation",frag_sd), stdout())
+hist(fragsize,probability=T,breaks=100,xlab="mRNA insert size (bp)",main=paste(c("Mean=",frag_mean,";","SD=",frag_sd),collapse=""),border="blue")
+lines(density(fragsize,bw=10),col='red')
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.junction.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.junction.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,4 @@
+chrom intron_st(0-based) intron_end(1-based) read_count annotation
+chr1 17055 17232 1 annotated
+chr1 21768 22000 1 complete_novel
+chr1 12697 13220 1 partial_novel
diff -r 000000000000 -r 306127635959 test-data/output.junctionSaturation_plot.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.junctionSaturation_plot.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,12 @@
+pdf('output.junctionSaturation_plot.pdf')
+x=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100)
+y=c(0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1)
+z=c(0,0,0,0,0,0,1,1,1,1,1,1,1,2,2,2,2,2,2,3)
+w=c(0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,2)
+m=max(0,0,0)
+n=min(0,0,0)
+plot(x,z/1000,xlab='percent of total reads',ylab='Number of splicing junctions (x1000)',type='o',col='blue',ylim=c(n,m))
+points(x,y/1000,type='o',col='red')
+points(x,w/1000,type='o',col='green')
+legend(5,0, legend=c("All junctions","known junctions", "novel junctions"),col=c("blue","red","green"),lwd=1,pch=1)
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.junction_plot.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.junction_plot.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,8 @@
+pdf("output.splice_events.pdf")
+events=c(25.0,25.0,25.0)
+pie(events,col=c(2,3,4),init.angle=30,angle=c(60,120,150),density=c(70,70,70),main="splicing events",labels=c("partial_novel 25%","complete_novel 25%","known 25%"))
+dev.off()
+pdf("output.splice_junction.pdf")
+junction=c(33.3333333333,33.3333333333,33.3333333333)
+pie(junction,col=c(2,3,4),init.angle=30,angle=c(60,120,150),density=c(70,70,70),main="splicing junctions",labels=c("partial_novel 33%","complete_novel 33%","known 33%"))
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.pos.DupRate.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.pos.DupRate.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,2 @@
+Occurrence UniqReadNumber
+1 40
diff -r 000000000000 -r 306127635959 test-data/output.qual.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.qual.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,62 @@
+pdf('output.qual.boxplot.pdf')
+p0<-rep(c(33,43,45,51,54,58,59,60,61,62,63,64,66,67,69,70,71),times=c(1,2,1,3,1,1,1,1,1,3,1,1,1,2,5,5,10)/1000)
+p1<-rep(c(43,45,51,56,57,58,60,61,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,2,1,1,2,1,3,3,1,8,5,6)/1000)
+p2<-rep(c(43,49,51,53,54,56,58,59,60,61,64,65,66,67,69,70,71),times=c(1,1,1,1,2,1,1,1,2,1,1,2,2,2,7,6,8)/1000)
+p3<-rep(c(33,39,53,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,1,1,2,1,1,1,4,4,1,5,5,6)/1000)
+p4<-rep(c(33,55,58,59,60,61,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,5,1,1,4,1,2,5,2,3,3,9)/1000)
+p5<-rep(c(33,53,58,60,61,62,64,65,67,68,69,70,71),times=c(1,1,1,2,1,4,3,2,2,3,5,4,11)/1000)
+p6<-rep(c(33,40,54,56,58,60,64,66,67,68,69,70,71),times=c(3,2,1,1,1,1,2,2,4,4,4,2,13)/1000)
+p7<-rep(c(41,42,43,49,50,51,57,58,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,2,1,1,1,2,1,1,2,7,1,2,3,12)/1000)
+p8<-rep(c(33,39,53,56,58,59,60,62,63,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,2,4,3,2,3,5,12)/1000)
+p9<-rep(c(33,40,50,52,53,57,58,60,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,3,1,4,1,1,3,2,5,4,10)/1000)
+p10<-rep(c(33,40,53,54,55,58,60,63,64,66,67,68,69,70,71),times=c(2,1,1,2,1,1,2,1,4,1,1,1,7,6,9)/1000)
+p11<-rep(c(45,50,51,52,53,56,57,58,60,62,63,64,65,66,67,68,69,70,71),times=c(1,2,1,1,1,1,1,1,3,2,1,1,1,3,1,1,3,5,10)/1000)
+p12<-rep(c(33,41,52,53,54,58,59,60,64,65,66,67,69,70,71),times=c(3,1,1,2,1,1,1,3,1,2,3,2,5,6,8)/1000)
+p13<-rep(c(33,40,51,53,55,56,58,59,60,63,64,65,66,67,68,69,70,71),times=c(4,1,1,1,1,1,1,1,1,1,3,1,2,2,2,3,5,9)/1000)
+p14<-rep(c(33,39,54,56,57,59,60,61,62,63,65,66,67,68,69,70,71),times=c(4,1,3,1,1,1,1,2,1,1,1,2,2,1,7,2,9)/1000)
+p15<-rep(c(33,39,40,42,45,50,52,53,57,58,59,60,61,64,66,68,69,70,71),times=c(2,2,1,1,1,1,1,1,1,3,1,1,1,3,1,1,4,5,9)/1000)
+p16<-rep(c(33,47,51,52,53,58,59,60,61,64,67,69,70,71),times=c(4,1,1,1,2,3,1,1,2,3,3,7,2,9)/1000)
+p17<-rep(c(33,48,50,51,53,54,55,56,58,60,61,63,64,65,66,67,69,70,71),times=c(1,1,1,1,2,1,1,1,2,2,3,2,2,1,2,1,7,2,7)/1000)
+p18<-rep(c(33,43,48,51,53,58,59,60,61,63,64,66,67,68,69,70,71),times=c(2,1,1,1,2,2,1,2,2,3,1,1,3,1,7,2,8)/1000)
+p19<-rep(c(33,44,47,50,51,52,54,58,59,61,62,64,65,67,69,70,71),times=c(2,1,1,2,1,1,2,1,1,1,1,3,1,1,8,4,9)/1000)
+p20<-rep(c(33,46,47,51,54,56,58,59,61,62,63,64,66,67,69,70,71),times=c(1,1,1,1,2,1,1,2,1,2,1,5,5,3,5,2,6)/1000)
+p21<-rep(c(33,43,54,55,57,58,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,5,2,3,1,3,2,4,5,4,6)/1000)
+p22<-rep(c(33,47,51,53,54,57,58,60,62,63,64,65,66,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,5,1,4,3,5,5,6)/1000)
+p23<-rep(c(33,42,53,54,55,57,58,62,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,1,5,2,2,3,2,9,3,4)/1000)
+p24<-rep(c(33,42,52,54,57,60,61,63,64,65,66,67,69,70,71),times=c(1,1,1,1,1,2,1,1,5,1,6,4,5,4,6)/1000)
+p25<-rep(c(33,53,54,57,61,62,63,64,66,67,68,69,70,71),times=c(1,1,1,2,1,1,1,2,4,5,2,9,5,5)/1000)
+p26<-rep(c(46,53,54,57,58,60,61,62,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,2,5,8,4,1,5,3,5)/1000)
+p27<-rep(c(42,43,48,54,56,57,60,61,62,66,67,68,69,70,71),times=c(1,1,1,2,1,1,4,1,2,1,4,1,5,6,9)/1000)
+p28<-rep(c(51,55,56,57,60,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,2,4,2,2,2,4,10,1,8)/1000)
+p29<-rep(c(49,52,56,57,58,60,63,64,65,66,67,69,70,71),times=c(2,1,1,1,2,1,1,3,1,3,6,8,2,8)/1000)
+p30<-rep(c(45,47,50,57,61,62,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,6,3,2,8,5,8)/1000)
+p31<-rep(c(48,52,53,54,57,58,59,60,61,62,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,4,1,2,3,3,7,3,6)/1000)
+p32<-rep(c(43,47,48,54,56,62,64,66,67,68,69,70,71),times=c(1,1,1,1,2,1,2,1,5,3,10,5,7)/1000)
+p33<-rep(c(52,55,58,60,61,63,64,68,69,70,71),times=c(1,1,2,1,1,1,5,4,11,5,8)/1000)
+p34<-rep(c(42,43,50,56,59,60,63,64,67,68,69,70,71),times=c(1,1,1,1,1,1,1,3,1,4,9,5,11)/1000)
+p35<-rep(c(42,53,57,58,60,64,66,68,69,70,71),times=c(1,1,1,2,1,3,2,2,12,7,8)/1000)
+p36<-rep(c(48,53,56,61,63,64,66,67,69,70,71),times=c(2,1,1,2,1,1,2,6,7,3,14)/1000)
+p37<-rep(c(53,56,60,63,64,66,68,69,70,71),times=c(2,2,1,1,3,2,6,7,8,8)/1000)
+p38<-rep(c(41,48,53,57,59,61,62,63,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,3,3,4,1,4,6,11)/1000)
+p39<-rep(c(38,42,51,53,56,58,61,63,64,65,66,67,68,69,70,71),times=c(1,1,1,1,1,1,1,1,2,1,4,2,2,7,3,11)/1000)
+p40<-rep(c(53,58,61,62,63,64,66,67,68,69,70,71),times=c(1,1,3,1,2,2,1,2,2,9,4,12)/1000)
+p41<-rep(c(48,53,54,57,58,59,60,61,63,64,66,67,68,69,70,71),times=c(1,1,1,1,1,1,2,1,1,1,3,3,1,5,7,9)/1000)
+p42<-rep(c(38,49,54,58,59,64,65,66,67,68,69,70,71),times=c(1,1,3,1,1,3,1,2,1,1,7,7,9)/1000)
+p43<-rep(c(50,51,62,63,64,65,66,67,69,70,71),times=c(2,1,1,1,3,1,2,4,3,8,12)/1000)
+p44<-rep(c(48,54,63,64,65,66,67,68,69,70,71),times=c(1,2,4,1,1,2,2,1,7,8,8)/1000)
+p45<-rep(c(50,57,58,59,60,62,64,67,69,70,71),times=c(1,1,1,1,1,1,1,1,10,8,7)/1000)
+p46<-rep(c(43,48,54,59,60,64,65,66,67,68,69,70,71),times=c(2,1,1,2,1,2,1,1,4,2,1,6,8)/1000)
+p47<-rep(c(49,53,56,61,64,66,67,69,70,71),times=c(1,1,1,1,2,2,2,3,10,7)/1000)
+p48<-rep(c(61,64,66,67,68,69,70,71),times=c(2,1,2,2,2,6,5,7)/1000)
+p49<-rep(c(33,56,60,64,66,68,69,70,71),times=c(1,1,2,1,1,2,2,5,10)/1000)
+p50<-rep(c(33,66,67,68,69,70,71),times=c(1,1,1,2,4,5,7)/1000)
+boxplot(p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13,p14,p15,p16,p17,p18,p19,p20,p21,p22,p23,p24,p25,p26,p27,p28,p29,p30,p31,p32,p33,p34,p35,p36,p37,p38,p39,p40,p41,p42,p43,p44,p45,p46,p47,p48,p49,p50,xlab="Position of Read(5'->3')",ylab="Phred Quality Score",outline=F)
+dev.off()
+
+
+pdf('output.qual.heatmap.pdf')
+qual=c(1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,3,0,0,1,0,0,0,1,1,1,1,3,1,1,0,1,2,0,5,5,10,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,2,1,1,2,1,3,3,1,8,5,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,2,0,1,0,1,1,2,1,0,0,1,2,2,2,0,7,6,8,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,1,2,1,1,1,4,4,1,5,5,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,5,1,1,0,4,1,2,5,2,3,3,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,4,0,3,2,0,2,3,5,4,11,3,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,2,0,2,4,4,4,2,13,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,2,1,0,0,0,0,0,1,1,0,0,0,2,0,1,1,2,7,1,2,3,12,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,1,2,0,1,2,0,0,4,3,2,3,5,12,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,3,0,1,0,0,0,4,1,1,3,2,5,4,10,2,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,1,0,2,0,0,1,4,0,1,1,1,7,6,9,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,1,1,0,0,1,1,1,0,3,0,2,1,1,1,3,1,1,3,5,10,3,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,1,0,0,0,1,1,3,0,0,0,1,2,3,2,0,5,6,8,4,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,1,1,0,0,1,3,1,2,2,2,3,5,9,4,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,1,1,2,1,1,0,1,2,2,1,7,2,9,2,0,0,0,0,0,2,1,0,1,0,0,1,0,0,0,0,1,0,1,1,0,0,0,1,3,1,1,1,0,0,3,0,1,0,1,4,5,9,4,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,2,0,0,0,0,3,1,1,2,0,0,3,0,0,3,0,7,2,9,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,2,1,1,1,0,2,0,2,3,0,2,2,1,2,1,0,7,2,7,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,2,0,0,0,0,2,1,2,2,0,3,1,0,1,3,1,7,2,8,2,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,1,1,0,2,0,0,0,1,1,0,1,1,0,3,1,0,1,0,8,4,9,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,2,0,1,0,1,2,0,1,2,1,5,0,5,3,0,5,2,6,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,5,0,0,0,2,0,3,1,3,2,4,5,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1,1,0,1,0,2,1,5,1,4,0,3,5,5,6,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,2,0,0,0,1,1,5,2,2,3,2,9,3,4,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,2,1,0,1,5,1,6,4,0,5,4,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,2,0,0,0,1,1,1,2,0,4,5,2,9,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,1,0,1,1,2,0,5,0,8,4,1,5,3,5,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,2,0,1,1,0,0,4,1,2,0,0,0,1,4,1,5,6,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,2,0,4,2,2,2,4,10,1,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,1,1,2,0,1,0,0,1,3,1,3,6,0,8,2,8,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,0,2,0,6,3,2,8,5,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,1,1,1,2,1,0,4,1,2,3,3,7,3,6,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,1,0,2,0,0,0,0,0,1,0,2,0,1,5,3,10,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,2,0,1,1,0,1,5,0,0,0,4,11,5,8,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,1,3,0,0,1,4,9,5,11,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,2,0,1,0,0,0,3,0,2,0,2,12,7,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,1,0,0,0,0,2,0,1,1,0,2,6,0,7,3,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,1,0,0,1,3,0,2,0,6,7,8,8,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,1,1,1,3,0,3,4,1,4,6,11,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,1,0,1,2,1,4,2,2,7,3,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,3,1,2,2,0,1,2,2,9,4,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,1,2,1,0,1,1,0,3,3,1,5,7,9,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,1,0,0,0,0,3,1,2,1,1,7,7,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,1,3,1,2,4,0,3,8,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,1,2,2,1,7,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,1,0,0,1,0,10,8,7,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,0,0,0,2,1,1,4,2,1,6,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,2,0,2,2,0,3,10,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,2,2,2,6,5,7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,1,0,1,0,2,2,5,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,2,4,5,7)
+mat=matrix(qual,ncol=51,byrow=F)
+Lab.palette <- colorRampPalette(c("blue", "orange", "red3","red2","red1","red"), space = "rgb",interpolate=c('spline'))
+heatmap(mat,Rowv=NA,Colv=NA,xlab="Position of Read",ylab="Phred Quality Score",labRow=seq(from=33,to=71),col = Lab.palette(256),scale="none" )
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.rawCount.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.rawCount.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,8 @@
+#chr start end name score strand 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
+chr1 17368 17436 NR_106918 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+chr1 17368 17436 NR_107062 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+chr1 34610 36081 NR_026818 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+chr1 69090 70008 NM_001005484 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+chr1 14361 29370 NR_024540 0 - 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4
+chr1 34610 36081 NR_026820 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+chr1 11873 14409 NR_046018 0 + 0 0 0 0 0 0 0 0 1 1 1 1 2 2 2 2 2 3 3 3
diff -r 000000000000 -r 306127635959 test-data/output.read_distribution.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.read_distribution.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,16 @@
+Total Reads 40
+Total Tags 44
+Total Assigned Tags 38
+=====================================================================
+Group Total_bases Tag_count Tags/Kb
+CDS_Exons 918 0 0.00
+5'UTR_Exons 1652 3 1.81
+3'UTR_Exons 2967 4 1.35
+Introns 14397 27 1.88
+TSS_up_1kb 4000 0 0.00
+TSS_up_5kb 20000 4 0.20
+TSS_up_10kb 35240 4 0.11
+TES_down_1kb 2000 0 0.00
+TES_down_5kb 12512 0 0.00
+TES_down_10kb 22752 0 0.00
+=====================================================================
diff -r 000000000000 -r 306127635959 test-data/output.saturation.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.saturation.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,87 @@
+pdf('output.saturation.pdf')
+par(mfrow=c(2,2))
+name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95)
+S5=c()
+S10=c()
+S15=c()
+S20=c()
+S25=c()
+S30=c()
+S35=c()
+S40=c()
+S45=c()
+S50=c()
+S55=c()
+S60=c()
+S65=c()
+S70=c()
+S75=c()
+S80=c()
+S85=c()
+S90=c()
+S95=c()
+boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q1',xlab='Resampling percentage')
+name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95)
+S5=c(1.0)
+S10=c(1.0)
+S15=c(1.0)
+S20=c(1.0)
+S25=c(1.0)
+S30=c(1.0)
+S35=c(1.0)
+S40=c(1.0)
+S45=c(0.259259259259)
+S50=c(0.333333333334)
+S55=c(0.39393939394)
+S60=c(0.444444444444)
+S65=c(0.0256410256403)
+S70=c(0.0476190476199)
+S75=c(0.111111111112)
+S80=c(0.166666666666)
+S85=c(0.21568627451)
+S90=c(0.111111111112)
+S95=c(0.0526315789469)
+boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q2',xlab='Resampling percentage')
+name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95)
+S5=c()
+S10=c()
+S15=c()
+S20=c()
+S25=c()
+S30=c()
+S35=c()
+S40=c()
+S45=c()
+S50=c()
+S55=c()
+S60=c()
+S65=c()
+S70=c()
+S75=c()
+S80=c()
+S85=c()
+S90=c()
+S95=c()
+boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q3',xlab='Resampling percentage')
+name=c(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95)
+S5=c(4.00000000001)
+S10=c(1.5)
+S15=c(0.666666666666)
+S20=c(1.5)
+S25=c(1.00000000001)
+S30=c(0.666666666666)
+S35=c(0.428571428571)
+S40=c(0.25)
+S45=c(0.111111111111)
+S50=c(0.0)
+S55=c(0.09090909091)
+S60=c(0.25)
+S65=c(0.153846153846)
+S70=c(0.0714285714295)
+S75=c(0.0)
+S80=c(0.0624999999991)
+S85=c(0.117647058824)
+S90=c(0.111111111111)
+S95=c(0.0526315789465)
+boxplot(100*S5,100*S10,100*S15,100*S20,100*S25,100*S30,100*S35,100*S40,100*S45,100*S50,100*S55,100*S60,100*S65,100*S70,100*S75,100*S80,100*S85,100*S90,100*S95,names=name,outline=F,ylab='Percent Relative Error',main='Q4',xlab='Resampling percentage')
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output.seq.DupRate.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.seq.DupRate.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,2 @@
+Occurrence UniqReadNumber
+1 40
diff -r 000000000000 -r 306127635959 test-data/output.splice_events.pdf
Binary file test-data/output.splice_events.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output.splice_junction.pdf
Binary file test-data/output.splice_junction.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output2.geneBodyCoverage.curves.pdf
Binary file test-data/output2.geneBodyCoverage.curves.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output2.geneBodyCoverage.heatMap.pdf
Binary file test-data/output2.geneBodyCoverage.heatMap.pdf has changed
diff -r 000000000000 -r 306127635959 test-data/output2.geneBodyCoverage.r
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output2.geneBodyCoverage.r Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,8 @@
+d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam <- c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
+
+
+pdf("output.geneBodyCoverage.curves.pdf")
+x=1:100
+icolor = colorRampPalette(c("#7fc97f","#beaed4","#fdc086","#ffff99","#386cb0","#f0027f"))(1)
+plot(x,d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam,type='l',xlab="Gene body percentile (5'->3')", ylab="Coverage",lwd=0.8,col=icolor[1])
+dev.off()
diff -r 000000000000 -r 306127635959 test-data/output2.geneBodyCoverage.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output2.geneBodyCoverage.txt Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,2 @@
+Percentile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
+d1_pairend_strandspecific_51mer_hg19_chr1_1_100000_bam 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
diff -r 000000000000 -r 306127635959 test-data/output_read_count.xls
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output_read_count.xls Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,47 @@
+#chrom st end accession score gene_strand tag_count RPKM
+chr1 12227 12612 NR_046018_intron_1 0 + 0 0.000
+chr1 12721 13220 NR_046018_intron_2 0 + 0 0.000
+chr1 11873 12227 NR_046018_exon_1 0 + 0 0.000
+chr1 12612 12721 NR_046018_exon_2 0 + 1 208507.089
+chr1 13220 14409 NR_046018_exon_3 0 + 2 38229.222
+chr1 11873 14409 NR_046018_mRNA 0 + 3 41272.287
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diff -r 000000000000 -r 306127635959 test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bam
Binary file test-data/pairend_strandspecific_51mer_hg19_chr1_1-100000.bam has changed
diff -r 000000000000 -r 306127635959 test-data/testwig.Forward.wig
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/testwig.Forward.wig Tue Jul 07 10:39:47 2015 -0400
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diff -r 000000000000 -r 306127635959 test-data/testwig.Reverse.wig
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/testwig.Reverse.wig Tue Jul 07 10:39:47 2015 -0400
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diff -r 000000000000 -r 306127635959 test-data/testwig.wig
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/testwig.wig Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,1788 @@
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diff -r 000000000000 -r 306127635959 tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml Tue Jul 07 10:39:47 2015 -0400
@@ -0,0 +1,12 @@
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