IMPACT-Pipeline: MSK-IMPACT assay data analysis framework to process data on LSF/SGE¶

Author:	Ronak H Shah, Donavan Cheng, Ahmet Zehir, Aijazuddin Syed, Raghu Chandramohan, Meera Prasad et.al
Contact:	rons.shah@gmail.com
Source code:	http://github.com/rhshah/IMPACT-Pipeline
License:	Apache License 2.0

IMPACT-Pipeline is a Perl/Python/R command-line software toolkit to process data, call somatic aberrations (SNV,INDELS,SV,CNV) generated by NGS based MSK-IMPACT assay. It is designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina.

Contents:

Installation¶

git clone --recursive https://github.com/rhshah/IMPACT-Pipeline.git

You can also download the release file in zip, tar.gz format and do:

wget https://github.com/rhshah/IMPACT-Pipeline/archive/1.0.1.tar.gz
tar xzvf 1.0.1.tar.gz

Requirements¶

Please see template.conf file in the configuration folder.

perl:	v5.20.2
python:	v2.7.8
R:	v3.1.2

Purpose and Tools Used¶

Somatic SNV calling:
Trimming:	Trimmomatic
Alignment:	BWA v0.7.5a
	MuTect v1.1.4
Somatic INDEL calling:
	SomaticIndelDetector in GATK v2.3-9
Somatic INDEL calling:
	PINDEL v0.2.5a7
Indel Realignment:
	ABRA v0.92
Mark Duplicates and Various Statistics:
	Picard Tools v1.96
Base Quality Recalibration and Find Covered Intervals:
	GATK v3.3.0
Genotyping Position:
	SAMTOOLS v0.1.19
Somatic Structural Variant Framework:
	IMPACT-SV v1.0.1

Inside the config file¶

There are three sections:

Section 1	Section 2	Section 3
Locations	Parameters	Versions

All of this section start with > sign.

Inside each of the section here are the things that need to be set:

Locations¶

GATK_SomaticIndel:
ZCAT:	Location of the `zcat` program on linux
TMPDIR:	Set the temporary directory for all tools please set somthing other then `/tmp`
JAVA_1_6:	Set JAVA version 1.6
JAVA_1_7:	Set JAVA version 1.7
	Path to GATK somatic indel detector (GATK version 2.3-9)
GATK:	Path to GATK (GATK version 3.3.0)
Reference:	Path to fasta referece file to be used (GRCh37)
Refseq:	Path to refgene file to be used
PICARD:	Path to picard tools (Picard version 1.19)
Mutect:	Path to MuTect (MuTect version 1.1.4)
BaitInterval:	Bait file to be used for analysis. Please make the interval file based on Picard’s HSmetrics tool format.
TargetInterval:	Target file to be used for analysis. Please make the interval file based on Picard’s HSmetrics tool format.
ABRA:	Path to ABRA (ABRA version 0.92)
TargetRegionLIST:
	Target File in GATK’s List format.
PINDELBIN:	Path to pindel installation (Pindel version 0.2.5a7)
SVpipeline:	Path to structural variant framework (only required if running the structural variant detection framework)
CAT:	Location of `cat` program on Linux
PYTHON:	Location of `python` program on Linux (Python version 2.7.8)
TrimGalore:	Path to trimgalore tool
PERL:	Location of `perl` program on Linux (Perl version 5.20.2)
BWA:	Path to bwa tool
GeneInterval:	Gene interval file
GeneIntervalAnn:
	Gene interval annotated file
GeneCoord:	Path to Gene Coordinate file
TilingInterval:	= Path to tiling intervals
TilingIntervalAnn:
	Path to tiling intervals - annotated for cytoband, for copy number
FingerPrintInterval:
	Path to FingerPrint Interval file
dbSNP:	Path to db snp vcf file
COSMIC:	Path to cosmic vcf (version 0.68)
Mills_1000G_Indels:
	Path to Mills 1000G Indels
dbSNP_bitset:	Path to dbsnp bitset file
AnnotateAssessFilterVariants:
	Path to Annotate Assess and Filter variants script
LoessNormalization:
	Path ot Loess Normalization for copynumber
GCBiasFile:	Path to GCbias file for copy number
HistNormDir:	Path to Historiacal Normal dir for Copy number
BestCopyNumber:	Path to Copy number script
NormVsNormCopyNumber:
	Path to Normal vs. Normal Copy number script
StdNormalLoess_TM:
	Standard Normals for copy number analysis - FFPE for tumor samples#
StdNormalLoess_NVN:
	Standard Normals for copy number normal vs normal analysis
AllMetrics:	Path to all metrics R script
SAMTOOLS:	Path to samtools
BEDTOOLS:	Path to bedtools
GenotypeAllele:	Path to Genotype allele script
CosmicHotspotVcf:
	Path to cosmic hotspot vcf
Annovar:	Path to Annovar script
Annovar_db:	Path to Annovar DB
Canonical_refFlat_file:
	Path to canonical reflat file
IGVtools:	Path to IGV tools
TranslationFolder:
	Path to translation folder
HotSpot_mutations:
	Path to hotspot mutations for 2 tiered filtering
clinicalExons:	ListOfClinicalExon
Validated_Exons:
	File with List Of Clinically Validated Exons
Tumor_supressor_list:
	Path to list of tumor supressor genes
Canonical_Exon_Interval_table_with_aa:
	Path to exon interval table
Canonical_Exon_Interval_list:
	Path to canonical exon interval table for DoC
NormalVariantsVCF:
	Path to compiled variants found in mixed normals
QSUB:	Path to qsub for SGE
BSUB:	Path to bsub for LSF
RHOME:	Path to R bin directory
RLIBS:	Path to R library directory
RSYNC:	Path to `rsyn` on system
BarcodeKey:	Path to barcode key file
AdaptorKey:	Path to adaptor key file
StandardNormalsDirectory:
	Directory where the standard normals are stored

Parameters¶

Set the parameters to different file/folders/values required by the IMPACT pipeline

StdNormalForMutationCalling:
	Path to standard normal to be used for mutation calling
ListOfFiles:	File of Files(FOF) for different steps for the pipeline (only required when the process dont start from merging fastq)
Process:	Which process to run the pipeline on ( can be 1,2,3,4,5,6,7 independently or continuous combination in ascending order )
FastqSource:	Where are the fastq file from (can be `GCL` or `DMP`)
MAPQ:	Mapping Quality Threshold (Used by DMP-IMPACT:0.2)0
BASQ:	Base Quality Threshold (Used by DMP-IMPACT:0.2)
MergeDinucleotide:
	Flag to Merge di-nucleotide mutation(can be 1(True) or 2(False))
MoveFiles:	Flag to Move file in folders (can be 1(True) or 2(False))
DeleteIntermediateFiles:
	Flag ti Delete Intermediate Files (can be 1(True) or 2(False))
TNfreqRatio_MutectStdFilter:
	TN freq Ratio for mutect std filter (Used by DMP-IMPACT:5)
TNfreqRatio_SomIndelStdFilter:
	TN freq Ratio for SID std filter (Used by DMP-IMPACT:5)
VF_threshold_hotspot:
	Variant Frequency threshold for SNV hotspot (Used by DMP-IMPACT:0.01)
AD_SomIndelSTDFilter:
	Allele Depth Threshold for SID standard filter (Used by DMP-IMPACT:5)
DP_SomIndelSTDFilter:
	Total Depth Threshold for SID standard filter (Used by DMP-IMPACT:0)
VF_SomIndelSTDilter:
	Variant Frequency Threshold for SID standard filter (Used by DMP-IMPACT:0.01)
AD_MutectSTDFilter:
	Allele Depth Threshold for Mutect standard filter (Used by DMP-IMPACT:5)
DP_MutectSTDFilter:
	Total Depth Threshold for Mutect standard filter (Used by DMP-IMPACT:0)
VF_MutectSTDFilter:
	Variant Frequency Threshold for Mutect standard filter (Used by DMP-IMPACT:0.01)
TNfreqRatio_AnnotationFilter:
	Tumor to Normal frequency ratio therehold for Annotation (Used by DMP-IMPACT:5)
PON_AD_Threshold:
	Panel of Normal Allele Depth Threshold (Used by DMP-IMPACT:3)
PON_TPVF_Threshold:
	Panel of Normal TPVF Threshold (Used by DMP-IMPACT:10)
Pindel_Min_Indel_Len:
	Minimum Length of INDEL called by PINDEL(Used by DMP-IMPACT:25)
Pindel_Max_Indel_Len:
	Maximum Length of INDEL called by PINDEL (Used by DMP-IMPACT:2000)
MAFthreshold_AnnotationFilter:
	Maf threshold for Annotation (Used by DMP-IMPACT:0.01)
minimumDPforSNV:
	Minimum Total Depth for Novel SNVs (Used by DMP-IMPACT:20)
minimumADforSNV:
	Minimum Allele Depth for Novel SNVs (Used by DMP-IMPACT:10)
minimumVFforSNV:
	Minimum Variant Frequency for Novel SNVs (Used by DMP-IMPACT:0.05)
minimumDPforSNVhs:
	Minimum Total Depth for Hotspot SNVs (Used by DMP-IMPACT:20)
minimumADforSNVhs:
	Minimum Allele Depth for Hotspot SNVs (Used by DMP-IMPACT:8)
minimumVFforSNVhs:
	Minimum Variant Frequency for Hotspot SNVs (Used by DMP-IMPACT:0.02)
minimumDPforINDEL:
	Minimum Total Depth for Novel INDELs (Used by DMP-IMPACT:20)
minimumADforINDEL:
	Minimum Allele Depth for Novel INDELs (Used by DMP-IMPACT:10)
minimumVFforINDEL:
	Minimum Variant Frequency for Novel INDELs (Used by DMP-IMPACT:0.05)
minimumDPforINDELhs:
	Minimum Total Depth for Hotspot INDELs (Used by DMP-IMPACT:20)
minimumADforINDELhs:
	Minimum Allele Depth for Hotspot INDELs (Used by DMP-IMPACT:8)
minimumVFforINDELhs:
	Minimum Variant Frequnecy for Hotspot INDELs (Used by DMP-IMPACT:0.02)
occurrencePercent:
	Minimum Percentage For Occurrence In Other Normals (Used by DMP-IMPACT:0.2)
Coverage_threshold_darwin_report:
	Coverage threshold for darwin reports(good coverage vs bad coverage) (Used by DMP-IMPACT:100)
QUEUE_NAME:	Name of the queue on the SGE or LSF
CLUSTER:	Flag for what cluster to be used (can `SGE` or `LSF`)
runABRA:	Flag to whether use ABRA or GATK indel realignment(can be 1(True) or 2(False))

Versions¶

Inside the version there are version that are being used for each tool. This is just for consistency in reports.

Description for title_file.txt¶

Headers for this tab-delimited file should be exactly with this names:

Barcode:	Has to start with bc and end with any number [for example: bc01 or bc101 should match the adaptor & barcode file mentioned in configuration file
Pool:	Can be any string joined by ``-`` and not ``_`` and all entries should be from same pool
Sample_ID:	Can be any string joined by ``-`` and not ``_``
Collab_ID:	Can be any string or `-`

Extracted_DNA_Yield:
Patient_ID:	Can be any string joined by ``-`` and not ``_``
Class:	Can be Tumor or Normal.
Sample_type:	Can be any string or `-`
Input_ng:	Can be any float or `-`
Library_yield:	Can be float or `-`
Pool_input:	Can be float or `-`
Bait_version:	Can be any string or `-`
Gender:	Can be any Male/Female or `-`
PatientName:	Can be any string or `-`
MAccession:	Can be any string or `-`
	Can be a float or `-`

For analysis to start the outputDirectory will be required to have this file with title_file.txt as the name or this file needs to be present in the configuration file with either title_file.txt as then name or Pool_title.txt as the name where Pool is the string used above for that category.

Description for SampleSheet.csv¶

This is a comma separated file is created by the illumina sequencer and it is used to merge the fastq files.

Headers for this tab-delimited file should be exactly with this names:

FCID:	Flowcell ID (required)
Lane:	Lane Number, this is used to merge the fastq files across lanes (required)
SampleID:	Sample ID, this is used to merge the files (required)
SampleRef:	Sample Reference is from [example:HUMAN]
Index:	Index used to sequence the sample (require)
Description:	Description of the samples
Control:	Can be any string or `-`
Recipe:	Can be any string or `-`
Operator:	Can be any string or `-`
SampleProject:	Can be any string or `-`

For analysis to start the outputDirectory will be required to have this file with SampleSheet.csv as the name or this file needs to be present in the configuration file with SampleSheet.csv as the name.

Description for adaptor file in the configuration file¶

The adaptor file is the tab-delimited file with two columns:

Barcode Key to which the adaptor belongs which should always start with bc
Adaptor sequence itself

There is no header in this file.

For Example:

bc01 GATCGGAAGAGCACACGTCTGAACTCCAGTCACAACGTGATATCTCGTATGCCGTCTTCTGCTTG

Description for barcode file in the configuration file¶

The barcode file is the tab-delimited file with two columns:

Barcode Sequece
Barcode Number that sequence represent.

There is a header in this file.

For Example:

Sequence	TruSeqBarcode
AACGTGAT	bc01

Usage¶

Quick Usage¶

RunIlluminaProcess.pl [options]

–config | -c S Path to configration file(required)

—svConfig | -sc S Path to structural variant configration file(optional)

–symLinkFlag | -sf I Flag for Keeping or removing the symolic links(1:Remove;2:Keep)(default:2)

—dataDirectory | -d S Path where all the files to be processed are located (required)

–outputDirectory | -o S Path where all the output files will be written (required)

Assuming you have setup the configuration file properly and you have SampleSheet.csv and title_file.txt in the dataDirectory you can run:

nohup perl RunIlluminaProcess.pl -c configuration.txt -sc configuration_sc.txt -d /path/to/fastq/files -o /path/to/output/directory

Detailed Usage¶

The behaviour of the prgram depends on the inputs in the configuration file:

In the configuration file the Process variable in section >Parameters tells pipeline following:

What does each number represent¶

Process	Things Pipeline will do
1	Merge Fastq
2	Trimming, Mapping & sorting of SAM file giving you a BAM file
3	Mark Duplicates, Indel Realignment, Base Quality Recalibration
4	Metrics Calculation, QC Report Genaration and launching IMPACT-SV if given -sc flag specified
5	Variant Calling
6	Variant Filtering and Genotyping
7	Variant Annotation and Variant Filtering

Using different Process to run Pipeline¶

To run the complete pipeline. Set the following in the configuration file:

Process: 1,2,3,4,5,6,7
To run from Process 1. Set the following in the configuration file:

ListOfFilesListOfFiles:

fastq.list #where fastq.list contains all the fastq files to be proces, this needs to be an even number as it automatically pairs them.

Process: 2,3,4,5,6,7
To run from the Process 3 to 7. Set the following in the configuration file:

ListOfFiles: SortedBam.list (where SortedBam.list contains all the sorted bam files from Process 2 to be processed)

Process: 3,4,5,6,7
To run from the Process 4 to 7. Set the following in the configuration file:

ListOfFiles: RecalibratedBam.list (where Recalibrated.list contains all the recalibrated bam files from Process 3 to be processed)

Process: 4,5,6,7

To run from the Process 5 to 7. Set the following in the configuration file:

ListOfFiles: RecalibratedBam.list (where Recalibrated.list contains all the recalibrated bam files from Process 3 to be processed)

Process: 5,6,7

To run from the Process 6 to 7. Set the following in the configuration file:

ListOfFiles: RecalibratedBam.list (where Recalibrated.list contains all the recalibrated bam files from Process 3 to be processed)

Process: 6,7

To run from the Process 7. Set the following in the configuration file:

ListOfFiles: RecalibratedBam.list (where Recalibrated.list contains all the recalibrated bam files from Process 3 to be processed)

Process: 7

If you want to run each Process separetly that is also possible but you need to make sure that files from previous procss are present in the outputDirectory

Shell Script to run pipeline¶

There is also a helper shell script (Run_Pipeline_Example.sh) in the bin directory which will help to run the framework. Which looks like this:

##Run_Pipeline_Example.sh
#author:Ronak H Shah
#v1.0.1
##Path where the fastq are stored
export DATADIR=<Path for Data Directory>
##Path where the output shoud be written
export OUTDIR=<Path To Output Directory>
##Path to the IMPACT-Pipeline script
export PipelineScript=<Path to IMPACT-Pipeline Script>
##Path to Perl installation
export Perl=<Path to Perl>
##Project associated with the Run
export ProjectName=<ProjectName>
##Path to working directory where you will write the LSF/SGE outputs
export WorkingDir=<Path to write sge/lsf files>
##Path to configfile for running main IMPACT pipeline
export CONFIGFILE=<Path To Pipeline Configuration File>
##Path to structural variants pipeline configuration file
export SV_ConfigFile=<Path to SV detection configuration file>

##Run both IMPACT-Pipeline & SV Process on LSF
echo bsub -q sol -cwd ${WorkingDir} -J ${ProjectName} -e${ProjectName}.stderr -o ${ProjectName}.stdout -We 24:00 -R "rusage[mem=2]" -M 4 \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -sc {$SV_ConfigFile} -d ${DATADIR} -o ${OUTDIR}\"
bsub -q sol -cwd ${WorkingDir} -J ${PoolName} -e${ProjectName}.stderr -o ${ProjectName}.stdout -We 24:00 -R "rusage[mem=2]" -M 4 \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -sc {$SV_ConfigFile} -d ${DATADIR} -o ${OUTDIR}\"
##Run IMPACT-Pipeline on LSF
echo bsub -q sol -cwd ${WorkingDir} -J ${ProjectName} -e${ProjectName}.stderr -o ${ProjectName}.stdout -We 24:00 -R "rusage[mem=2]" -M 4 \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -d ${DATADIR} -o ${OUTDIR}\"
bsub -q sol -cwd ${WorkingDir} -J ${PoolName} -e${ProjectName}.stderr -o ${ProjectName}.stdout -We 24:00 -R "rusage[mem=2]" -M 4 \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -d ${DATADIR} -o ${OUTDIR}\"

##Run both IMPACT-Pipeline & SV Process on SGE
echo qsub -q test.q -wd ${WorkingDir} -N ${ProjectName} -l hvmem=2G,virtual_free=2G -pe smp 1 -b y \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -sc ${SV_ConfigFile} -d ${DATADIR} -o ${OUTDIR}\"
qsub -q test.q -wd ${WorkingDir} -N ${ProjectName} -l hvmem=2G,virtual_free=2G -pe smp 1 -b y \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -sc ${SV_ConfigFile} -d ${DATADIR} -o ${OUTDIR}\"
##Run both IMPACT-Pipeline on SGE
echo qsub -q test.q -wd ${WorkingDir} -N ${ProjectName} -l hvmem=2G,virtual_free=2G -pe smp 1 -b y \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -d ${DATADIR} -o ${OUTDIR}\"
qsub -q test.q -wd ${WorkingDir} -N ${ProjectName} -l hvmem=2G,virtual_free=2G -pe smp 1 -b y \"${Perl} ${PipelineScript} -c ${CONFIGFILE} -d ${DATADIR} -o ${OUTDIR}\"

Description of sub-scripts¶

Script in the bin folder¶

Compiling QC metrics, Generating QC-Report and running copynumber analysis¶

dmp_compile_qc_metrics.pl [options]

–bamList | -i S File of files having list of all bam files (required)

—titleFile | -t S tab-delimited title file for the samples (required and submit with full path)

–AllMetrics | -am S Path to AllMetrics script (required)

—LoessNorm | -ln S Path to Loess Normalization Script (required)

–BestCN | -cn S Path to Best Copy Number Script (required)

—GCBias | -gcb S Path to GC bias file (required)

–HistNorm | -his S Path to Directory with all historical normal files (required)

—queue | -q S Name of the Sun Grd Engine Queue where the pipeline needs to run (required)

--qsub S Path to qsub executable for SGE(default:None,optional)

—bsub S Path to bsub executable for LSF(default:None,required)

–metricsScript | -ms S Name of the script used to generate .html and .pdf files

—outdir | -o S Path where all the output files will be written (optional) [default:cwd]

Genotyping Variants across multiple samples¶

dmp_genotype_allele.pl [options]

–FilteredMutationVcfFile | -fmv S vcf file describing details about the mutations (required)

—BamFile | -bam S bam file to be used for genotyping (required)

–RefFile | -rf S Path to genome reference file (required)

—samtools | -s S Path to samtools (required)

–bedtools | -b S Path to bedtools (required)

—MinBaseQualit | -mbq I Min. Base Quality Threshold (optional;default:5)

–MinMappingQuality | -mmq I Min. Mapping Quality Threshold (optional;default:5)

—deleteUnwantedFiles | -d I 2=>To delete files 1=> To keep files (default:2,optional)

–outdir | -o S Path where all the output files will be written (optional;default:current working directory)

—outFile | -of S Name of the allele depth output file (optional;default:BamFame-.bam+_mpileup.alleledepth)

–bamId | -bi S Bam Id to be used (optional;default:bamfile name)

—queue | -q S Name of the SGE / LSF Queue where the pipeline needs to run (required)

--qsub S Path to qsub executable for SGE(default:None,optional)

—bsub S Path to bsub executable for LSF(default:None,required)

–mpileUpOutFile | -mof S Name of samtools mpileup output file (optional;default:BamFile-.bam+.mpileup)

—typeOfSample | -tos S Type of Sample (optional;default:Tumor;canbe Tumor or Normal)

Running Indel Realignment using ABRA¶

usage: Run_AbraRealignment.py [options]

Run ABRA Indel Realignment

arguments:

-h, --help

show this help message and exit

-i BamFile.list, –bamList BamFile.list: Full path to the tumor bam files as a fof.

`-p PatientID, --patientId PatientID`
	Id of the Patient for which the bam files are to be realigned
`-v, --verbose`	make lots of noise [default]

-t 5, –threads 5 Number of Threads to be used to run ABRA -d, –mdp Threshold for downsampling depth to run ABRA -k [43 [43 ...]], –kmers [43 [43 ...]]

Number of k-mers to be used to run ABRA; Multiple k-mers are separated by space

-temp /somepath/tmpdir, –temporaryDirectory /somepath/tmpdir: Full Path to temporary directory
-r /somepath/Homo_Sapeins_hg19.fasta, –referenceFile /somepath/Homo_Sapeins_hg19.fasta: Full Path to the reference file with the bwa index.
-a /somepath/ABRA.jar, –abraJar /somepath/ABRA.jar: Full Path to the ABRA jar file.
-tr /somepath/targetRegion.bed, –targetRegion /somepath/targetRegion.bed: Full Path to the target region bed file
-j /somepath/java, –javaPATH /somepath/java: Path to java executable.
-b /somepath/bin, –bwaPATH /somepath/bin: Path to the bin of bwa executable.
-q all.q or clin.q, –queue all.q or clin.q: Name of the SGE queue
-o /somepath/output, –outDir /somepath/output: Full Path to the output dir.
-qsub /somepath/qsub, –qsubPath /somepath/qsub: Full Path to the qsub executables of SGE.
-bsub /somepath/bsub, –bsubPath /somepath/bsub: Full Path to the bsub executables of LSF.

Call Indels > 25bp using Pindel¶

usage: Run_Pindel.py [options]

Run Pindel for Long Indels & MNPS (32bp-350bp)

optional arguments:

-h, --help

show this help message and exit

-i pindel.conf, –pindelConfig pindel.conf: Full path to the pindel configuration
-pId PatientID, –patientId PatientID: Id of the Patient for which the bam files are to be realigned

-v, --verbose

make lots of noise [default]

-t 5, –threads 5 Number of Threads to be used to run Pindel -r /somepath/Homo_Sapeins_hg19.fasta, –referenceFile /somepath/Homo_Sapeins_hg19.fasta

Full Path to the reference file with the bwa index.

-p /somepath/pindel/bin, –pindelDir /somepath/pindel/bin: Full Path to the Pindel executables.
-chr ALL, –chromosomes ALL: Which chr/fragment. Pindel will process reads for one chromosome each time. ChrName must be the same as in reference sequence and in read file.
-q all.q or clin.q, –queue all.q or clin.q: Name of the SGE queue
-o /somepath/output, –outDir /somepath/output: Full Path to the output dir.
-op TumorID, –outPrefix TumorID: Id of the Tumor bam file which will be used as the prefix for Pindel output files
-qsub /somepath/qsub, –qsubPath /somepath/qsub: Full Path to the qsub executables of SGE.
-bsub /somepath/bsub, –bsubPath /somepath/bsub: Full Path to the bsub executables of LSF.

Script in the support-scripts folder¶

Calculate intervals from bam file that have some minimum coverage¶

usage: Run_FindCoveredInterval.py [options]

This will run find covered interval program from GATK.

optional arguments:

-h, --help

show this help message and exit

-i BamFile.list, –bamList BamFile.list: Full path to the tumor bam files as a fof.
-of OutFilePrefix, –outFilePrefix OutFilePrefix: Output Covered Interval File Prefix for the bam files.

-v, --verbose

make lots of noise [default]

-t 5, –threads 5 Number of Threads to be used to run: FindCoveredIntervals
-dp 20, –totaldepth 20: Total depth threshold
-mbq 20, –minbasequality 20: Threshold for minimum base quality for Running Find Covered Interval
-mmq 20, –minmappingquality 20: Threshold for minimum mapping quality for Running Find Covered Interval
-r /somepath/Homo_Sapeins_hg19.fasta, –referenceFile /somepath/Homo_Sapeins_hg19.fasta: Full Path to the reference file with the bwa index.
-g /somepath/GenomeAnalysisTK.jar, –gatkJar /somepath/GenomeAnalysisTK.jar: Full Path to the GATK jar file.
-j /somepath/java, –javaPATH /somepath/java: Path to java executable.
-q all.q or clin.q, –queue all.q or clin.q: Name of the SGE queue
-o /somepath/output, –outDir /somepath/output: Full Path to the output dir.
-qsub /somepath/qsub, –qsubPath /somepath/qsub: Full Path to the qsub executables of SGE.
-bsub /somepath/bsub, –bsubPath /somepath/bsub: Full Path to the bsub executables of LSF.

Annotating variants in merged variant file¶

dmp_annotate_variants.pl [options]

–SomaticMutIndelFile | -si S File containing mutations (required and submit with full path,Ex:/SomePath/Some_SomaticMutIndel.txt)

—ConfigurationFile | -c S Configuration file that contains the locations for the programs and the databases (required and submit with full path)

–titleFile | -t S tab-delimited title file for the samples (required and submit with full path)

—outdir | -o S Path where all the output files will be written (optional;default:cwd)

–exonCoverageFile | -ec S Path where the all exon coverage file is located (full path]

—geneCoverageFile | -gc S Path where the gene coverage file is located (full path)]

—deleteUnwantedFiles | -d I 2=>To delete files 1=> To keep files (default:2,optional)

Filter variants after annotation¶

dmp_filter_genotyped_variants.pl [options]

–input | -i S File containing mutations with genotype information (required)

—hotspots | -h S File containing the list of hotspots (required)

–clinicalExons | -ce S File containing the list of clinical exons (required)

—titleFile | -t S Title file (required)

–minimumDPforSNVs | -dp_snv I Minumum accepted DP for novel SNVs (default: 20)

—minimumADforSNVs | -ad_snv I Minimum accepted AD for novel SNVs (default: 10)

–minimumVFforSNVs | -vf_snv F Minimum accepted VF for novel SNVs (default: 0.05)

—minimumDPforSNVhotspot | -dp_snvHS I Minumum accepted DP for Hotspot SNVs (default: 20)

–minimumADforSNVhotspot | -ad_snvHS I Minimum accepted AD for Hotspot SNVs (default: 8)

—minimumVFforSNVhotspot | -vf_snvHS F Minimum accepted VF for Hotspot SNVs (default: 0.02)

–minimumDPforINDELs | -dp_indel I Minumum accepted DP for novel INDELs (default: 20)

—minimumADforINDELs | -ad_indel I Minimum accepted AD for novel INDELs (default: 10)

–minimumVFforINDELs | -vf_indel I Minimum accepted VF for novel INDELs (default: 0.05)

—minimumDPforINDELhotspot | -dp_indelHS F Minumum accepted DP for Hotspot INDELs (default: 20)

–minimumADforINDELhotspot | -ad_indelHS I Minimum accepted AD for Hotspot INDELs (default: 8)

—minimumVFforINDELhotspot | -vf_indelHS F Minimum accepted VF for Hotspot INDELs (default: 0.02)

–minimumOccurrencePercent | -occurrence S Minimum accepted value of occurrence in other normals, in percent (default: 20)

—TNfreqRatioThreshold | -tn_ratio S Minimum value for VFt/VFn value (default: 5)

–MAFthreshold | -mt F Minimum accepted MAF values for unmatched variant calls (default : 0.01)

Filter indels from SomaticIndelDetector before genotyping¶

dmp_filter_indel.pl [options]

–IndelTxtFile|t S tab-delimted Indel file describing details about the mutations (required)

—IndelVcfFile|v S VCF format Indel file describing details about the mutations (required)

–sampleName|s S Name of the sample (required)

—totaldepth|dp I Tumor total depth threshold for Somatic Indel Detector(default:0,optional)

–alleledepth|ad I Tumor Allele depth threshold for Somatic Indel Detector(default:3,optional)

—variantfreq|vf F Tumor variant frequency threshold for Somatic Indel Detector(default:0.01,optional)

–TNratio|tnr I Tumor-Normal variant frequency ratio threshold for Somatic Indel Detector(default:5,optional)

—outdir|o S Path where all the output files will be written (optional) default:current working directory

Filter snv from MuTect before genotyping¶

dmp_filter_mutect.pl [options]

–MutationTxtFile|t S tab-delimted Mutect file describing details about the mutations (required)

–MutationVcfFile|v S VCF format Mutect file describing details about the mutations (required)

—sampleName|s S Name of the sample (required)

–totaldepth|dp I Tumor total depth threshold for Mutect(default:0,optional).

—alleledepth|ad I Tumor Allele depth threshold for Mutect(default:3,optional).

–variantfreq|vf F Tumor variant frequency threshold for Mutect(default:0.01,optional).

—TNratio|tnr I Tumor-Normal variant frequency ratio threshold for Mutect(default:5,optional).

–outdir|o S Path where all the output files will be written (optional) default:current working directory

Filter indels from PINDEL before genotyping¶

usage: dmp_filter_pindel.py [options]

Filter Indels from the output of pindel

optional arguments:

`-h, --help`	show this help message and exit
`-v, --verbose`	make lots of noise [default]

-i SomeID.vcf, -inputVcf SomeID.vcf: Input vcf freebayes file which needs to be filtered
-tsn SomeName, –tsampleName SomeName: Name of the tumor Sample
-dp 0, –totaldepth 0: Tumor total depth threshold
-ad 3, –alleledepth 3: Tumor allele depth threshold

-tnr 5, –tnRatio 5 Tumor-Normal variant frequency ratio threshold -vf 0.01, –variantfrequency 0.01

Tumor variant frequency threshold

-o /somepath/output, –outDir /somepath/output: Full Path to the output dir.
-min 25, –min_var_len 25: Minimum length of the Indels
-max 500, –max_var_len 500: Max length of the Indels

File Description¶

Here is small description for each of the important output files.

QC Metrics Files¶

Proj_*_All_Metrics.pdf¶

PDF file with graphical representation of all calculated quality and performance metrics for all samples in project. Numerical values are contained in the text files below.

Proj_*_ALL_basequalities.txt¶

Illumina base quality score by cycle (following GATK base quality recalibration), displayed for each sample.

Proj_*_ALL_Canonical_exoncoverage.txt¶

Mean coverage for each target interval corresponding to exons in canonical transcripts, displayed for each sample. Coverage is computed for reads with mapping quality > 20.

Proj_*_ALL_exoncoverage.txt¶

Mean coverage for each target interval corresponding to all protein-coding exons in all transcripts (including flanking splice sites), displayed for each sample. Coverage is computed for reads with mapping quality > 20.

Proj_*_ALL_exonnomapqcoverage.txt¶

Mean coverage for each target interval corresponding to all protein-coding exons in all transcripts (including flanking splice sites), displayed for each sample. Coverage is computed for reads regardless of mapping quality (i.e., including reads mapping to multiple locations).

Proj_*_ALL_FPavgHom.txt¶

Average minor allele fraction across all tiling SNPs that are homozygous in the given sample. This is an indication of the degree of contamination from unrelated DNA. (The IMPACT and HemePACT panels contain >1,000 “tiling SNPs” where both alleles are common in the population.)

Proj_*_ALL_FPCResultsUnMatch.txt¶

Pairs of samples from different individuals with concordant fingerprint genotypes. (The number represents the fraction of tiling SNPs homozygous in one sample which are homozygous for the alternate allele in the other sample.)

Proj_*_ALL_FPCResultsUnMismatch.txt¶

Pairs of samples from the same individual with discordant fingerprint genotypes. (The number represents the fraction of tiling SNPs homozygous in one sample which are homozygous for the alternate allele in the other sample.)

Proj_*_ALL_FPCsummary.txt¶

Matrix of all pairwise concordance values based on fingerprint genotypes. (The number represents the fraction of tiling SNPs homozygous in one sample which are homozygous for the alternate allele in the other sample.)

Proj_*_ALL_FPhet.txt¶

Fraction of all tiling SNPs that are heterozygous in the given sample. Samples with >0.50 heterozygous SNPs may be contaminated with unrelated DNA. (The IMPACT and HemePACT panels contain >1,000 “tiling SNPs” where both alleles are common in the population.)

Proj_*_ALL_FPsummary.txt¶

For each tiling SNP in each sample: the observed allele counts of each base, the inferred genotype, and the minor allele fraction. (The IMPACT and HemePACT panels contain >1,000 “tiling SNPs” where both alleles are common in the population.)

Proj_*_ALL_gcbias.txt¶

For each sample, the average coverage for target intervals in different bins of GC content (from 25-30% to 80-85%).

Proj_*_ALL_genecoverage.txt¶

Mean coverage for each target gene (across all target exons), displayed for each sample. Coverage is computed for reads with mapping quality > 20.

Proj_*_ALL_genotypehotspotnormals.txt¶

Sites of known somatic mutation hotspots (COSMIC) with variants detected in a normal sample. Allele counts are also shown for the matched tumor sample as well as any other tumor where the corresponding variant was detected.

Proj_*_ALL_HSmetrics.txt¶

Quality and performance metrics for hybrid selection, calculated by Picard.

Proj_*_ALL_insertsizemetrics.txt¶

Numerical values for histogram of library insert size distribution, displayed for each sample.

Proj_*_ALL_intervalnomapqcoverage_loess.txt¶

Mean coverage for each target interval, displayed for each sample. Coverage is computed for reads regardless of mapping quality (i.e., including reads mapping to multiple locations). Coverage is then normalized according to a Loess normalization to adjust for biases in GC content of target intervals.

Proj_*_ALL_intervalnomapqcoverage.txt¶

Mean coverage for each target interval, displayed for each sample. Coverage is computed for reads regardless of mapping quality (i.e., including reads mapping to multiple locations).

Proj_*_ALL_orgbasequalities.txt¶

Illumina base quality score by cycle (prior to GATK base quality recalibration), displayed for each sample.

Copy Number Files¶

Proj_*_ALL_copynumber.seg¶

Segmented copy number (following %GC-based loess normalization and CBS-based segmentation) for all samples. This file can be loaded in IGV.

Proj_*_copynumber_segclusp.genes.txt¶

Gene-level copy number (following %GC-based loess normalization and CBS-based segmentation) for all samples. Each gene is assigned the copy number ratio (fold-change) for the segment on which it falls. If a gene spans multiple segments, it is assigned the average copy number ratio. The normal sample selected for copy number normalization is shown for each sample. Both fold-change and a p-value (indicative of the signal-to-noise ratio) are given.

Proj_*_copynumber_segclusp.intragenic.txt¶

List of genes with putative intragenic copy number deletions. Individual targets (exons) are listed down the right-most column. The copy number ratios for the exons in each gene are grouped into two clusters. (Cluster membership is labeled as “1” or “2”.) Genes where exons with lower copy number are consecutive are candidates for intragenic deletions, though this analysis tends to overcall events.

Proj_*_copynumber_segclusp.pdf¶

Plots of copy number profiles for all tumors. Each data point represents a target interval (blue = exon; red = tiling SNP). Each tumor is normalized against a diploid normal, either from the same project or from a historical panel of normals; the specific normal used for normalization is shown. Listed below each plot are the 20 highest-level amplifications (left) and deletions (right), though not all are significant. Significantly altered genes (fold-change > 2 and p-value < 0.05) are marked with an asterisk.

Proj_*_copynumber_segclusp.probes.txt¶

Target-level copy number (following %GC-based loess normalization) for all samples. The normal sample selected for copy number normalization is shown for each sample. “fc” = fold change; “lr” = log ratio.

Proj_*_discrete_CNA.txt¶

Matrix of significant copy number alterations by gene (rows are genes; columns are tumors). 2 = amplification; 0 = neutral; -2 = deletion.

Proj_*_loessnorm.pdf¶

Plots showing best loess normalization fit to adjust for coverage bias related to %GC content of target intervals.

Structural Variant Files¶

Proj_*_AllAnnotatedSVs.txt¶

Annotated output from DELLY rearrangement detection algorithm. Genomic coordinates and gene annotations are provided for both breakpoints. The distance between breakpoints (SV_LENGTH) and orientation of fragments that are joined (Conection_Type) are shown. Also provided are the number of supporting paired reads and split reads and the inferred derived sequence at the breakpoint (when possible). This analysis tends to overcall events—as the vast majority of rearrangements are false positives, manual review and further filtering are necessary. Events listed (in Site2Description) as “Protein fusion: in frame” should be prioritized.

Proj_*_AllAnnotatedSVs.xlsx¶

Same information as Proj_*_AllAnnotatedCVs.txt (above) in Excel file format.

Per Sample Files¶

s__Proj__copynumber.seg¶

Segmented copy number for each individual sample. Each segment is listed with the genomic coordinates, number of target intervals, and copy number log ratio.

s__Proj__mrg_cl_aln_srt_MD_IR_BR.canonical.exon.covg.sample_interval_summary¶

Coverage statistics (total, mean, quartiles) for each exon of a canonical transcript in each individual sample. Coverage is calculated for reads with mapping quality > 20.

s__Proj__mrg_cl_aln_srt_MD_IR_BR.gene_nomapq.covg.sample_interval_summary¶

Coverage statistics (total, mean, quartiles) for each exon of any target transcript in each individual sample. Coverage is computed for reads regardless of mapping quality (i.e., including reads mapping to multiple locations).

s__Proj__mrg_cl_aln_srt_MD_IR_BR.gene.covg.sample_interval_summary¶

Coverage statistics (total, mean, quartiles) for each exon of any target transcript in each individual sample. Coverage is calculated for reads with mapping quality > 20.

s__Proj__mrg_cl_aln_srt_MD_IR_BR.target.covg¶

Coverage statistics (mean covearage and %GC content) for all target intervals (coding exons and tiling SNPs) in each individual sample. Coverage is calculated for reads with mapping quality > 20.

s__Proj__mrg_cl_aln_srt_MD_IR_BR.tiling_nomapq.covg.sample_interval_summary¶

Coverage statistics (mean covearage and %GC content) for just tiling intervals (tiling SNPs and fingerprint SNPs) in each individual sample. Coverage is computed for reads regardless of mapping quality (i.e., including reads mapping to multiple locations).

s__Proj__mrg_cl_aln_srt_MD_IR_BR.tiling.covg.sample_interval_summary¶

Coverage statistics (mean covearage and %GC content) for just tiling intervals (tiling SNPs and fingerprint SNPs) in each individual sample. Coverage is calculated for reads with mapping quality > 20.

Sample Info File¶

Proj_*_title_file.txt¶

Sample IDs (provided by CMO and Investigators) and limited metadata (including sample type, input DNA amount, library yield, and capture platform) for all samples in the project.

Variant Files¶

annotated_exonic_variants.txt¶

Mutations and indels (and genomic annotations and allele counts) called in all tumors in the project. The normal sample used for mutation calling is displayed in the second column (usually either the matched normal or a pool of unmatched normal). Presence in dbSNP, COSMIC, and the 1000 genomes project (depicted as the minor allele fraction in the population) is shown. For tumors called against an unmatched pool of normal, variants present in >0.01 of the 1000 genomes project (and not in COSMIC) are automatically filtered out. Allele counts across a panel of historical normal are shown to indicate potential systematic artifacts. The rightmost columns display the stats for every variant in every sample, including a panel of historical normals (usually labeled with a capital M or S). DP = depth of coverage at the variant site; RD = counts of reference allele; AD = counts of alternate (mutant) allele; VF = variant frequency.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotated.txt¶

Intermediate variants file displaying all mutations and indels called prior to any filtering. Stats are included for every variant in every sample in the project (as described above).

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedExonic.txt¶

Intermediate variants files displaying all non-silent mutations and indels called in exonic (plus splice site) regions of target genes.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedExonic.Dropped.txt¶

Intermediate variants files displaying all non-silent mutations and indels called in exonic (plus splice site) regions of target genes. The file labeled “Dropped” includes candidate mutations that were subsequently rejected based on empirical filters (e.g., present in historical normal or insufficient coverage, read support, or variant frequency).

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedExonic.Filtered.txt¶

Intermediate variants files displaying all non-silent mutations and indels called in exonic (plus splice site) regions of target genes. The file labeled “Filtered” includes candidate mutations that survived all filters.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedSilent.txt¶

Intermediate variants files displaying all silent mutations called in exonic (plus splice site) regions of target genes.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedSilent.Dropped.txt¶

Intermediate variants files displaying all silent mutations called in exonic (plus splice site) regions of target genes. The file labeled “Dropped” includes candidate mutations that were subsequently rejected based on empirical filters (e.g., present in historical normal or insufficient coverage, read support, or variant frequency).

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedSilent.Filtered.txt¶

Intermediate variants files displaying all silent mutations called in exonic (plus splice site) regions of target genes. The file labeled “Filtered” includes candidate mutations that survived all filters.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedNonPanelExonic.txt¶

Intermediate variants files displaying all non-silent mutations and indels called in exonic (plus splice site) regions of off-target genes.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedNonPanelExonic.Dropped.txt¶

Intermediate variants files displaying all non-silent mutations and indels called in exonic (plus splice site) regions of off-target genes. The file labeled “Dropped” includes candidate mutations that were subsequently rejected based on empirical filters (e.g., present in historical normal or insufficient coverage, read support, or variant frequency).

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedNonPanelExonic.Filtered.txt¶

Intermediate variants files displaying all non-silent mutations and indels called in exonic (plus splice site) regions of off-target genes. The file labeled “Filtered” includes candidate mutations that survived all filters.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedNonPanelSilent.txt¶

Intermediate variants files displaying all silent mutations and indels called in off-target regions, including introns, intergenic regions, and off-target genes.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedNonPanelSilent.Dropped.txt¶

Intermediate variants files displaying all silent mutations and indels called in off-target regions, including introns, intergenic regions, and off-target genes. The file labeled “Dropped” includes candidate mutations that were subsequently rejected based on empirical filters (e.g., present in historical normal or insufficient coverage, read support, or variant frequency).

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedNonPanelSilent.Filtered.txt¶

Intermediate variants files displaying all silent mutations and indels called in off-target regions, including introns, intergenic regions, and off-target genes. The file labeled “Filtered” includes candidate mutations that survived all filters.

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotated_All_Filtered.txt¶

Aggregation of all mutations and indels called (on-target and off-target) that survived all empirical filters (in the “Filtered” files above).

Proj_*_AllSomaticMutIndel_withAlleleDepth.txt¶

Candidate mutations called in all samples, prior to genomic annotation.

Proj_*_AllSomaticMutIndel_withAlleleDepth_mergedDNP.txt¶

Candidate mutations called, prior to genomic annotation, with adjacent SNVs with similar coverage and allele frequencies merged into dinucleotide substitutions.

Citation¶

If you use this software in a publication, please cite our paper

Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology.

ListOfFilesListOfFiles:
	fastq.list #where fastq.list contains all the fastq files to be proces, this needs to be an even number as it automatically pairs them.
Process:	2,3,4,5,6,7

ListOfFiles:	SortedBam.list (where SortedBam.list contains all the sorted bam files from Process 2 to be processed)
Process:	3,4,5,6,7

ListOfFiles:	RecalibratedBam.list (where Recalibrated.list contains all the recalibrated bam files from Process 3 to be processed)
Process:	4,5,6,7

IMPACT-Pipeline: MSK-IMPACT assay data analysis framework to process data on LSF/SGE¶

Installation¶

Requirements¶

Purpose and Tools Used¶

Inside the config file¶

Locations¶

Parameters¶

Versions¶

Description for title_file.txt¶

Description for SampleSheet.csv¶

Description for adaptor file in the configuration file¶

Description for barcode file in the configuration file¶

Usage¶

Quick Usage¶

Detailed Usage¶

What does each number represent¶

Using different Process to run Pipeline¶

Shell Script to run pipeline¶

Description of sub-scripts¶

Script in the bin folder¶

Compiling QC metrics, Generating QC-Report and running copynumber analysis¶

Genotyping Variants across multiple samples¶

Running Indel Realignment using ABRA¶

Call Indels > 25bp using Pindel¶

Script in the support-scripts folder¶

Calculate intervals from bam file that have some minimum coverage¶

Annotating variants in merged variant file¶

Filter variants after annotation¶

Filter indels from SomaticIndelDetector before genotyping¶

Filter snv from MuTect before genotyping¶

Filter indels from PINDEL before genotyping¶

File Description¶

QC Metrics Files¶

Proj_*_All_Metrics.pdf¶

Proj_*_ALL_basequalities.txt¶

Proj_*_ALL_Canonical_exoncoverage.txt¶

Proj_*_ALL_exoncoverage.txt¶

Proj_*_ALL_exonnomapqcoverage.txt¶

Proj_*_ALL_FPavgHom.txt¶

Proj_*_ALL_FPCResultsUnMatch.txt¶

Proj_*_ALL_FPCResultsUnMismatch.txt¶

Proj_*_ALL_FPCsummary.txt¶

Proj_*_ALL_FPhet.txt¶

Proj_*_ALL_FPsummary.txt¶

Proj_*_ALL_gcbias.txt¶

Proj_*_ALL_genecoverage.txt¶

Proj_*_ALL_genotypehotspotnormals.txt¶

Proj_*_ALL_HSmetrics.txt¶

Proj_*_ALL_insertsizemetrics.txt¶

Proj_*_ALL_intervalnomapqcoverage_loess.txt¶

Proj_*_ALL_intervalnomapqcoverage.txt¶

Proj_*_ALL_orgbasequalities.txt¶

Copy Number Files¶

Proj_*_ALL_copynumber.seg¶

Proj_*_copynumber_segclusp.genes.txt¶

Proj_*_copynumber_segclusp.intragenic.txt¶

Proj_*_copynumber_segclusp.pdf¶

Proj_*_copynumber_segclusp.probes.txt¶

Proj_*_discrete_CNA.txt¶

Proj_*_loessnorm.pdf¶

Structural Variant Files¶

Proj_*_AllAnnotatedSVs.txt¶

Proj_*_AllAnnotatedSVs.xlsx¶

Per Sample Files¶

s_*_Proj_*_copynumber.seg¶

s_*_Proj_*_mrg_cl_aln_srt_MD_IR_BR.canonical.exon.covg.sample_interval_summary¶

s_*_Proj_*_mrg_cl_aln_srt_MD_IR_BR.gene_nomapq.covg.sample_interval_summary¶

s_*_Proj_*_mrg_cl_aln_srt_MD_IR_BR.gene.covg.sample_interval_summary¶

s_*_Proj_*_mrg_cl_aln_srt_MD_IR_BR.target.covg¶

s_*_Proj_*_mrg_cl_aln_srt_MD_IR_BR.tiling_nomapq.covg.sample_interval_summary¶

s_*_Proj_*_mrg_cl_aln_srt_MD_IR_BR.tiling.covg.sample_interval_summary¶

Sample Info File¶

Proj_*_title_file.txt¶

Variant Files¶

annotated_exonic_variants.txt¶

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotated.txt¶

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedExonic.txt¶

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedExonic.Dropped.txt¶

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedExonic.Filtered.txt¶

Proj_*_AllSomaticMutIndel_withAlleleDepth_annovarAnnotatedSilent.txt¶

s__Proj__copynumber.seg¶

s__Proj__mrg_cl_aln_srt_MD_IR_BR.canonical.exon.covg.sample_interval_summary¶

s__Proj__mrg_cl_aln_srt_MD_IR_BR.gene_nomapq.covg.sample_interval_summary¶

s__Proj__mrg_cl_aln_srt_MD_IR_BR.gene.covg.sample_interval_summary¶

s__Proj__mrg_cl_aln_srt_MD_IR_BR.target.covg¶

s__Proj__mrg_cl_aln_srt_MD_IR_BR.tiling_nomapq.covg.sample_interval_summary¶

s__Proj__mrg_cl_aln_srt_MD_IR_BR.tiling.covg.sample_interval_summary¶