Usefull for generating
class averages from a list of annotated regions. Use a spreadsheet app to graph the
results.
Options:
-t PointData directories, full path, comma delimited. These should contain chromosome
specific xxx.bar.zip files.
-b Bed file (chr, start, stop, text, score, strand(+/-/.), full path, containing
regions to stack. Must be all the same size.
-p Peak shift, average distance between + and - strand peaks. Will be used to shift
the PointData by 1/2 the peak shift, defaults to 0.
-u Strand usage, defaults to 0 (combine), 1 (use only same strand), 2 (opposite
strand), or 3 (ignore).
this option to select particular stranded data to aggregate.
-r Replace scores with 1.
-f Pad start and stop of each bed region xxx bps, defaults to 0.
-d Delog2 scores. Do it if your data is in log2 space.
-v Convert each region scores to % of total.
-n Divide scores by the number of regions.
-k Divide each regions score by this value.
-l Divide each regions score by the total number of observations.
-s Scale all regions to a particular size. Defaults to max region size.
-a Average region scores instead of summing.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/AggregatePlotter -t
/Data/PolIIRep1/,/Data/PolIIRep2/ -b /Anno/tssSites.bed -p 73 -u 1
-l
**************************************************************************************
**************************************************************************************
** Aggregate QC Stats2: April 2022 **
**************************************************************************************
Parses and aggregates alignment quality statistics from log and json files produced by
the TNRunner2 DnaAlignQC and SampleConcordance workflows.
Required Options:
-j Directory containing TNRunner2 jobs.
-s Directory for saving the AggQC results.
Options:
-a Alignment log file match, defaults to '.+_AlignHg38.log'
-d Mark duplicates log file match, defaults to '.+_Markdup.log'
-r Read coverage json file match, defaults to '.+_UniObRC.json.gz'
-c Sample concordance json file match, defaults to '.+_SampleConcordance.json.gz'
-i Clinical info json file match, defaults to '.+_AvatarInfo.json.gz'
-n Normal DNA file match, defaults to '.+NormalDNA.+'
-t Tumor DNA file match, defaults to '.+TumorDNA.+'
Example: java -Xmx1G -jar pathToUSeq/Apps/AggregateQCStats2 -j TNJobs/ -s QCStats/
**************************************************************************************
**************************************************************************************
** Alignment End Trimmer: Dec 2017 **
**************************************************************************************
This application can be used to trim alignments according to the density of mismatches.
Each base of the alignment is compared to the reference sequence from the start of the
alignment to the end. If the bases match, the score is increased by -m. If the bases
don't match, the score is decreased by -n. The alignment position with the highest
score is used as the new alignment end point. The cigar string, alignment position,
mpos and flags are all updated to reflect trimming.
Notes:
1) Insertions, deletions and skips are currently not counted as matches or mismatches
Required:
-i Path to the orignal alignment, sam/bam/sam.gz OK.
-r Path to the reference sequence, gzipped OK.
-o Name of the trimmed alignment output. Output is bam and bai.
Optional:
-m Score of match. Default 1
-n Score of mismatch. Default 2
-v Verbose output. This will write out detailed information for every trimmed read.
It is suggested to use this option only on small test files.
-l Min length. If the trimmed length is less than this value, the read is switched
to unaligned. Default 10bp
-e Turn on RNA Editing mode. A>G (forward reads) and T>C (reverse reads) are considered
matches.
-s Turn on mismatch scoring mode. Reads with more than -x mismatches are dropped. If
RNA Editing mode is on, A>G (forward reads) and T>C (reverse reads) are considered
matches.
-x Max number of mismatches allowed in max scoring mode. Default 0
Examples:
1) java -Xmx4G -jar /path/to/AlignmentEndTrimmer -i 1000X1.bam -o 100X1.trim.bam
-r /path/to/hg19.fasta
2) java -Xmx4G -jar /path/to/AlignmentEndTrimmer -i 1000X1.bam -o 100X1.trim.bam
-r /path/to/hg19.fasta -m 0.5 -n 3
3) java -Xmx4G -jar /path/to/AlignmentEndTrimmer -i 1000X1.test.bam
-o 100X1.test.trim.bam -r /path/to/hg19.fasta -v
**************************************************************************************
**************************************************************************************
** Allelic Expression Comparator: Oct 2014 **
**************************************************************************************
Looks for changes in allelic expression between two conditions. First run the
AllelicExpressionDetector on each condition. Only snps with minimum # samples and
where in one of the conditions it also passes FDR and log2Rto thresholds.
Required Options:
-d Directory containing nameSnp.obj files to compare from the AllelicExpressionDetector.
-s Save directory.
Default Options:
-f Minimum -10Log10(FDR) for individual condition allelic expression, default 13
-l Minimum abs(log2Ratio) for individual condition allelic expression, default 1
-m Minimum samples in each condition to compare Snp count data, defaults to 2.
-r Full path to R. Defaults to '/usr/bin/R'
Example: java -Xmx4G -jar pathTo/USeq/Apps/AllelicExpressionComparator -s EyeAEC/
-d EyeAED
**************************************************************************************
**************************************************************************************
** Allelic Expression Detector: Sept 2016 **
**************************************************************************************
Application for identifying allelic expression based on a table of snps and bam
alignments that have been filtered for alignment bias. See the ReferenceMutator and
SamComparator apps. Uses DESeq2 to identify differential expression between alleles.
Required Options:
-n Sample names to process, comma delimited, no spaces.
-b Directory containing coordinate sorted bam and index files named according to their
sample name.
-d SNP data file containing all sample snp calls.
-e Results directory.
-s SNP map bed file from the ReferenceMutator app.
-t Tabix gz indexed bed file of exons where the name column is the gene name, see
ExportExons and https://github.com/samtools/htslib
Default Options:
-g Minimum GenCall score, defaults to 0.25
-q Minimum alignment base quality at snp, defaults to 20
-m Minimum alignment read coverage, defaults to 4
-p Minimum number replicas with heterozygous snp to score, defaults to 3
-r Full path to R (version 3+) loaded with DESeq2, see http://www.bioconductor.org
Type 'library(DESeq2) in R to see if it is installed. Defaults to '/usr/bin/R'
Example: java -Xmx4G -jar pathTo/USeq/Apps/AllelicExpressionDetector -b Bam/RPENormal/
-n D002-14,D005-14,D006-14,D009-14 -d GenotypingResults.txt.gz -s SNPMap_Ref2Alt_Int.txt
-r RPENormal -t ~/Anno/b37EnsGenes7Sept2016_Exons.bed.gz
**************************************************************************************
**************************************************************************************
** Allelic Expression Merger : Sept 2016 **
**************************************************************************************
App to merge two GeneiASE tables from the AlleleicExpressionDetector and the
AllelicExpressionRNASeqWriter. Where geneName coor duplicates are found, writes out
the first's record to the merged file.
Required Arguments:
-f First GeneiASE table (gene snp.id alt.dp ref.dp)
-s Second GeneiASE table (gene snp.id alt.dp ref.dp)
-m Merged output table results.
Example: java -Xmx4G -jar pathTo/USeq/Apps/AllelicExpressionMerger -f snpTable.txt
-s rnaSeqTable.txt -m mergedGeneiASETable.txt
**************************************************************************************
**************************************************************************************
** Allelic Expression RNASeq Writer : Sept 2016 **
**************************************************************************************
Application for parsing count data for downstream Allele Specific Gene Expression
detection, e.g. GeneiASE. Avoids snvs with vars within the read length, skips INDELs.
Required Arguments:
-b Bam file with associated index from an RNASeq experiment after filtering for
allelic alignment bias.
-v Vcf file containing snvs to use in extracting alignment counts from the bam. These
will be filtered using the args below before saving.
-t Tabix gz indexed bed file of exons where the name column is the gene name, see
ExportExons and https://github.com/samtools/htslib
-o Output file.
Default Arguments:
-l Read length, defaults to 50
-c Minimum alignment depth for quality bases, defaults to 10
-q Minimum base quality, defaults to 20
-a Minimum Alt alignment depth, defaults to 2
-r Minimum Ref alignment depth, defaults to 2
-m Minimum allele frequency, defaults to 0.05
-x Maximum allele frequency, defaults to 0.95
-p Don't print header
Example: java -Xmx4G -jar pathTo/USeq/Apps/AllelicExpressionRNASeqWriter -b proc.bam
-v lofreq.vcf.gz -t ~/Anno/b37EnsGenes7Sept2016_Exons.bed.gz -o forGeneiASE.txt.gz
**************************************************************************************
**************************************************************************************
** Allelic Methylation Detector: March 2014 **
**************************************************************************************
AMD identifies regions displaying allelic methylation, e.g. ~50% average mCG
methylation yet individual read pairs show a bimodal fraction distribution of either
fully methylated or unmethylated.
Options:
-s Save directory.
-f Fasta file directory.
-t BAM file directory containing one or more xxx.bam file with their associated xxx.bai
index. The BAM files should be sorted by coordinate and have passed Picard
validation.
-a Minimum number alignments per region, defaults to 15.
-e Minimum number Cs in each alignment, defaults to 6
-m Minimum region fraction methylation, defaults to 0.4
-x Maximum region fraction methylation, defaults to 0.6
-r Full path to R, defaults to /usr/bin/R
-c Converted CG context PointData directories, full path, comma delimited. These
should contain stranded chromosome specific xxx_-/+_.bar.zip files. One
can also provide a single directory that contains multiple PointData
directories. Use the ParsePointDataContexts on the output of the
NovoalignBisulfiteParser to select CG contexts.
-n Non-converted PointData directories, ditto.
-b Provide a bed file (chr, start, stop,...), full path, to scan a list of regions
instead of the genome. See, http://genome.ucsc.edu/FAQ/FAQformat#format1
Example: java -Xmx4G -jar pathTo/USeq/Apps/AllelicMethylationDetector -s AMD
-f Fastas/ -t Bams/ -c PointData/Con -n PointData/NonCon
**************************************************************************************
**************************************************************************************
** Annotate Bed With Genes Nov 2022 **
**************************************************************************************
Takes a bed like file and a UCSC gene table, intersects them and adds a new column to
the file with the gene names that intersect the gene exons or regions.
Parameters:
-u UCSC RefFlat or RefSeq gene table file, full path. See,
http://genome.ucsc.edu/cgi-bin/hgTables, (geneName name2(optional) chrom strand
txStart txEnd cdsStart cdsEnd exonCount exonStarts exonEnds).
-b Bed like file of regions to intersect with genes, gz/zip OK
-i Indexes defining chr start stop columns, defaults to 0,1,2 for bed format.
-r Gzipped results file.
-p Bp padding to expand the bed regions when intersecting with genes.
-g Intersect gene regions with bed, not gene exons.
-f Include gene feature in annotations (5pUTR->3pUTR->All Exons->Intron)
Example: java -Xmx2G -jar pathTo/USeq/Apps/AnnotateBedWithGenes -p 100 -g -i 1,2,3
-b targetRegions.bed -r targetsWithGenes.txt.gz -u hg19EnsGenes.ucsc.gz -f
**************************************************************************************
**************************************************************************************
** Annotated Bed 2 UCSC RefFlat June 2015 **
**************************************************************************************
Run AnnotateBedWithGenes using the addGeneFeature option then use this tool to convert
the annotated bed to genes for those with a gene name. To convert to GTF, use the UCSC
genePredToGtf tool.
Parameters:
-b Annotated bed file to convert
-m Minimum number exons for conversion, defaults to 1
-a Required anno type, defaults to none
Example: java -Xmx2G -jar pathTo/USeq/Apps/AnnotatedBed2UCSCRefFlat -b anno.bed.gz
-a 3pUTR -m 2
**************************************************************************************
**************************************************************************************
** Annotated Vcf Filter: Feb 2023 **
**************************************************************************************
Filters vcf records that have T_DP, N_DP, T_AF, and N_AF INFO field attributes.
Options:
-v Full path file or directory containing xxx.vcf(.gz/.zip OK) file(s).
-s Directory to save the parsed files.
-t Minimum tumor allele frequency (AF), defaults to 0.
-n Maximum normal AF, defaults to 1.
-u Minimum tumor alignment depth, defaults to 0.
-a Minimum tumor alt count, defaults to 0.
-o Minimum normal alignment depth, defaults to 0.
-d Minimum T-N AF difference, defaults to 0.
-r Minimum T/N AF ratio, defaults to 0.
-q Minimum QUAL score, defaults to 0.
-f Comma delimited list of FILTER keys to fail records.
Example: java -jar pathToUSeq/Apps/TNScopeVCFParser -v ToParse/ -n 0.5 -u 100
-o 20 -d 0.05 -r 2 -a 3 -t 50 -p -s FilteredVcfFiles/ -f
multi_event_alt_allele_in_normal,str_contraction
**************************************************************************************
**************************************************************************************
** Annotated Vcf Parser March 2023 **
**************************************************************************************
Splits VCF files that have been annotated with SnpEff, ExAC, and clinvar, plus the
VCFBkz, VCFCallFrequency, and VCFSpliceScanner USeq apps into passing and failing
records. Use the -r option to inspect the effect of the various filters on each
record. Use the VCFRegionFilter app to restrict variants to particular regions.
A summary spreadsheet is exported with select information and excel hyperlinks for
rapid inspection. Must have AF (or T_AF) and DP in the INFO field for Alt filtering.
Options:
-v File path or directory containing xxx.vcf(.gz/.zip OK) file(s) to filter.
-s Directory for saving the results.
-f Perform a candidate somatic variant processing. Setting the following overrides
the defaults.
-d Minimum DP alignment depth
-m Minimum AF allele frequency
-x Maximum AF allele frequency
-w Minimum Alt observations
-q Maximum CF prior call frequency
-k Include non protein_coding transcripts in ANN, defaults to excluding
-j Ignore the max AF filter for ACMG incidental germline gene variants.
-p Maximum population allele frequency, only applies if present.
-z Minimum BKZ when present.
-b Maximum fraction of BKAF samples with allele frequency >= VCF AF*0.9, only applies
if present.
-g Splice junction types to scan.
-n Minimum difference in splice junction scores, only applies if present.
-a Comma delimited list of SnpEff ANN impact categories to select for.
-l Further restrict ANN impacts to one of these effects: frameshift_variant,
stop_gained, stop_lost, or start_lost.
-c Comma delimited list of CLINSIG terms to select for.
-t For CLINSIG=Conflicting_interpretations_of_pathogenicity, minimum fraction
pathogenicity, defaults to 0
-u For CLINSIG=drug_response, only pass it if associated with these genes. Comma
delimited, no spaces. Defaults to all.
-e Comma delimited list of CLINSIG terms to select against.
-C CLINVAR file date for spreadsheet output.
-R Remove variants containing a 'MULTIALLELIC' INFO key. Typically from vt decompose.
-i Comma delimited list of VCF ID keys to select for. If the VCF ID contains one or
more, the record is passed regardless of other filters. The match is not exact.
-o Only require, if set or present, SnpEff ANN or CLINSIG or Splice to be true to pass.
Defaults to require that all set pass.
-T Txt file containing a list of transcripts to keep, one per line, to use in filtering
the summary spreadsheet output.
-r Verbose per record output.
-y Path to a config txt file for setting the above.
Example: java -jar pathToUSeq/Apps/AnnotatedVcfParser -v VCFFiles/ -s Parsed/
-d 74 -m 0.05 -x 0.75 -j -p 0.02 -b 0.1 -z 3 -g D5S,D3S,G5S,G3S -n 4.5 -a
HIGH,MODERATE -e Benign,Likely_benign -c Pathogenic,Likely_pathogenic,
Conflicting_interpretations_of_pathogenicity -t 0.51 -u RYR1 -T
~/Ref/ACMGTranscripts.txt -C 2021-04-04 -w 3 -R
**************************************************************************************
**************************************************************************************
** Avatar Assembler : June 2022 **
**************************************************************************************
Tool for assembling fastq avatar datasets based on the results of several sql queries.
See https://ri-confluence.hci.utah.edu/x/KwBFAg Login on hci-clingen1.hci.utah.edu
Options:
-i Info
-d Diagnosis
-g Gender
-o Origin
-e Prep
-p Path to Exp dir w/o Year, e.g. /Repository/PersonData/
-y Year dirs to examine for fastq linking, defaults to 2017,2018,2019,2020,2021,2022
-j Job dir to place linked fastq
-f Only keep patients with a diagnosis containing this String, defaults to all.
-l Create Fastq links for all patient datasets, defaults to just those with both a
Tumor and Normal exome.
-r Patient stats output file.
Example: java -jar -Xmx2G ~/USeqApps/AvatarAssembler -p /Repository/PersonData/
-r avatarAssembler.log.gz -i sampleInfo.txt -d sampleDiagnosis.txt -g
sampleGender.txt -s sampleOrigin.txt -e samplePrep.txt -f HEM
> avatarAssemblerProblemSamples.txt
**************************************************************************************
**************************************************************************************
** Avatar Data Wrangler : March 2023 **
**************************************************************************************
Tool for assembling directories for TNRunner based on files provided by M2Gen via
download from DNAnexus. Handles patient datasets from different exome capture
platforms with multiple tumor and normal samples.
Each TNRunner Job will be named as AvatarID-NormalExomeID-TumorExomeID-TumorRNASeqID.
When missing they will be labeled NA. Uses the SubjectMatchMaker to fetch or make
molecular data patient ids shared with the other clinically processed datasets.
Required Parameters:
-r Path to a folder containing the following resource files. Each must end with the
given extensions:
xxx_ClinicalMolLinkage_V4.csv - DNAnexus
xxx_WES_QC_Metrics.csv - DNAnexus
xxx_WesCramList.txt - DNAnexus
xxx_RNACramList.txt - DNAnexus
xxx_AWSRepoList.txt - AWS
(optional) xxx_PatientPHI.csv - RISR's PatientIdCoverter
-p Print an example script to pull these resource files.
-j Job dir to build out the patient dir structure.
-t Directory to place temp files with PHI for Subject ID matching
-s Directory containing the SubjectMatchMaker 'currentRegistry_' file
-d Path to save a bash script for downloading the sequence read data.
Example: java -jar ~/USeqApps/AvatarDataWrangler -r Resources/ -j AJobs -t SMM_PHI
-s ~/PHI/SmmRegistry/ -d dxDownloadCmds.sh
**************************************************************************************
**************************************************************************************
** Avatar Project Assembler : June 2023 **
**************************************************************************************
Tool for assembling directories for TNRunner based on files provided by M2Gen.
Handles patient datasets from different exome capture platforms with multiple tumor
samples. Convert each M2Gen xxx.csv to tab delimited using Excel's save as option.
Each TNRunner Job will be named as
PatientID-Platform-NormalExomeID-TumorExomeID-TumorRNASeqID, when missing they will be
labeled NA. Fastq files are linked, not moved, so delete the Jobs dir to start over.
Required Parameters:
-l Sample linkage file containing at least 4 tab delimited columns named
'ORIENAvatarKey' 'Tumor/Germline' 'WES' 'RNASeq', see
xxx_ClinicalMolLinkage_V4.txt
-g Patient gender file with just two tab delimited columns for the ORIENAvatarKey and
Sex, see xxx_PatientMaster_V4.txt
-p Sample exome platform file with just two tab delimited columns, the sample IDs that
match those in the 'WES' linkage file and the platform type NIM or IDT, see
xxx_WES_Resource_table.txt
-f Directory containing all of the paired fastq and M2Gen cram files for the T/N exomes
and T RNASeq, these should begin with the sample IDs that match those in the 'WES'
and 'RNASeq' columns in the linkage file.
-j Job dir to build out the patient dir structure and linked fastqs. Existing files
won't be overwritten.
-v Verbose debugging output.
Example: java -jar ~/USeqApps/AvatarProjectAssembler -l linkage.txt -g gender.txt -p
platform.txt -f ~/UroProj/Fastq -j ~/UroProj/TNRunnerJobs/ -v
**************************************************************************************
**************************************************************************************
** Avatar Comparator : Sept 2022 **
**************************************************************************************
Tool for identifying AVATAR datasets that are ready for analysis or need attention.
Options:
-j Patient job directory
-v Verbose output
Example: java -jar -Xmx2G ~/USeqApps/AvatarComparator -j AJobs/
**************************************************************************************
**************************************************************************************
** Bam Context Inspector: Oct 2016 **
**************************************************************************************
Application for scanning the surrounding context of a set of regions for non reference
bps. Use to flag variants with adjacent potentially confounding changes.
Required Options:
-b Sorted bam alignment file with associated index.
-r Bed file of regions to split into pass (no non refs) or fail (with a non ref bp).
-f Path to the reference fasta with and xxx.fai index
Default Options:
-p Bp of flanking bases to scan for non ref bases, defaults to 25
-c Minimum alignment coverage of a base before scanning, defaults to 6
-n Minimum non reference base count, defaults to 2
-q Minimum base quality, defaults to 13
-m Minimum alignment mapping quality, defaults to 13
-a Maximum non ref allele frequency, defaults to 0.03
-x Maximum number non ref bps in flanks, defaults to 1
-i Don't fail regions with an indel in the flanks
Example: java -Xmx4G -jar pathTo/USeq/Apps/BamContextInspector -b Bam/rPENormal.bam
-r rPENormal_calls.bed -r Ref/human_g1k_v37_decoy.fasta -q 20 -m 20 -a 2
**************************************************************************************
**************************************************************************************
** Bam Hg19 to B37 Converter: Aug 2016 **
**************************************************************************************
Cuts off the chr from each reference chromosome, converts chrM to MT, and swaps out
the header to convert hg19 alignments to b37 alignments.
Options:
-b Bam files to covert to b37, a directory with such or a single file.
-e A bam file with a good b37 header to add to the converted hg19 alignments.
Example: java -Xmx1500M -jar pathToUSeq/Apps/BamHg19B37Converter -b . -e ~/b37.bam
**************************************************************************************
**************************************************************************************
** Bam Blaster : May 2020 **
**************************************************************************************
Injects SNVs and INDELs from a vcf file into bam alignments. These and their mates are
extracted as fastq for realignment. For SNVs, only alignment bases that match the
reference and have a CIGAR of M are modified. Not all alignments can be modified.
Secondary/supplemental/not proper are skipped. One var per alignment. Variants within
read length distance of prior are ignored and saved to file for iterative processing.
Be sure to normalize and decompose your vcf file (e.g.https://github.com/atks/vt).
INDELs first base must be reference. Use the ExactBamMixer or BamMixer to add
realignments (e.g. 10%) with the unmodified.bams (e.g. 90%). Use the VCFVariantMaker
to generate random vcf variants or pull a VCF from Clinvar/ Cosmic.
Required:
-b Path to a coordinate sorted bam file with index.
-v Path to a trimmed, normalized, decomposed vcf variant file, zip/gz OK.
-r Full path to a directory to save the results.
-s Max size INDEL, defaults to 50
-d Min alignment depth, defaults to 25
-m Min distance between variants, defaults to 150
-i Verbose debugging output
Example: java -Xmx10G -jar pathTo/USeq/Apps/BamBlaster -b ~/BMData/na12878.bam
-r ~/BMData/BB0 -v ~/BMData/clinvar.pathogenic.SnvIndel.vcf.gz
**************************************************************************************
**************************************************************************************
** Bam Concordance: July 2021 **
**************************************************************************************
BC calculates sample level concordance based on uncommon homozygous SNVs found in bam
files. Samples from the same person will show high similarity (>0.9). Run BC on
related sample bams (e.g tumor & normal exomes, tumor RNASeq) plus an unrelated bam
for comparison. Mismatches passing filters are written to file. BC also generates a
variety of AF histograms for checking gender and sample contamination. Although
threaded, BC runs slowly with more that a few bams. Use the USeq ClusterMultiSampleVCF
app to check large batches of vcfs to identify the likely mismatched sample pairs.
WARNING: Mpileup does not check that the chr order is the same across samples and the
fasta reference, be certain they are or mpileup will produce incorrect counts. Use
Picard's ReorderSam app if in doubt.
Note re FFPE derived RNASeq data: A fair bit of systematic error is found in these
datasets. As such, the RNA-> DNA contrasts are low. Yet the DNA->RNA are > 0.9
Options:
-r Path to a bed file of regions to interrogate.
-s Path to the samtools executable.
-f Path to an indexed reference fasta file.
-b Path to a directory containing indexed bam and cram files.
-c Path to a tabix indexed bed file of common dbSNPs. Download 00-common_all.vcf.gz
from ftp://ftp.ncbi.nih.gov/snp/organisms/, grep for 'G5;' containing lines,
run VCF2Bed, bgzip and tabix it with https://github.com/samtools/htslib,
defaults to no exclusion from calcs.
-g Path to a file containing a line with the word 'gender' followed by the word 'male',
'female', 'm', or 'f' surrounded by whitespace, case insensitive, gz/zip OK.
This is compared to the gender estimate.
-d Minimum read depth, defaults to 25.
-a Minimum allele frequency to count as a homozygous variant, defaults to 0.95
-m Minimum allele frequency to count a homozygous match, defaults to 0.9
-q Minimum base quality, defaults to 20.
-u Minimum mapping quality, defaults to 20.
-n Minimum fraction similarity to pass sample set, defaults to 0.85
-x Maximum log2Rto score for calling a sample female, defaults to 1.5
-y Minimum log2Rto score for calling a sample male, defaults to 2.5
-e Sample name to ignore in scoring similarity and gender, defaults to 'RNA'
-j Write gzipped summary stats in json format to this file.
-t Number of threads to use. If not set, determines this based on the number of
threads and memory available to the JVM so set the -Xmx value to the max.
Example: java -Xmx100G -jar pathTo/USeq/Apps/BamConcordance -r ~/exomeTargets.bed
-s ~/ST/1.12/bin/samtools -b ~/Patient7Crams -d 10 -a 0.9 -m 0.8 -f
~/B37/human_g1k_v37.fasta -c ~/B37/b38ComSnps.bed.gz -j bc.json.gz -g
gender.json.gz
**************************************************************************************
**************************************************************************************
** Bam Mixer : April 2018 **
**************************************************************************************
Combines bam alignment files in different fractions to simulate multiple variant
frequencies. Run BamBlaster first.
Required:
-r Path to a directory to save the results
-u Path to the xxx_unmodified.bam from your BamBlaster run
-f Path to the xxx_filtered.bam from your BamBlaster run
-p Path to your realigned paired end bam
-s Path to your realigned single end bam
Optional:
-m Fractions to mix in the variant alignments, comma delimited, no spaces, defaults to
0.025,0.05,0.1,0.2
-v Verbose output.
Example: java -Xmx10G -jar pathTo/USeq/Apps/BamMixer -r ~/TumorSim/
-u ~/bb_unmodified.bam -f ~/bb_filtered.bam -p ~/bb_paired.bam -s ~/bb_single.bam
**************************************************************************************
**************************************************************************************
** Bam Pileup: Nov 2022 **
**************************************************************************************
BP extracts pileup information for each bam file over a list of regions. This includes
the # A,C,G,T,N,Del,Ins,FailingBQ bps for each bam. Provide the max memory available
to the JVM. The header of the results file contains the order of the bams. To
approximate IGV pileup info, set -q 0 -m 0 -i. If many alignment files are to be
processed, run this app on each file individually and then merge them with the
BamPileupMerger app. Secondary, supplementary, duplicate and failing vendorQC
alignments are skipped.
Required Options:
-b Path to a coordinate sorted bam/cram file with index or directory containing such.
-r Bed file of regions to extract pileup information. MUST BE NON OVERLAPPING. Run
the USeq MergeRegions app if unsure. xxx.bed.gz/.zip OK
-f Path to the reference fasta with xxx.fai index used for alignment.
-s Path to a gzip file to save the pileup information, must end in xxx.gz
-t Path to a directory containing the bgzip and tabix executables to compress and index
the bp file, see htslib.org
Default Options:
-q Minimum base quality, defaults to 10
-m Minimum alignment mapping quality, defaults to 13
-i Include counts from overlapping paired reads, defaults to excluding them.
-p Number processors to use, defaults to all, reduce if out of memory errors occur.
-x Max length of region chunk, defaults to 1000, set smaller if out of memory errors
occur.
-c Maximum read coverage stats calculated, defaults to 1000.
-a Alignment chroms and reference don't start with chr yet bed file does.
Example: java -Xmx100G -jar pathTo/USeq/Apps/BamPileup -b CramFiles/ -r target.bed
-f Ref/human_g1k_v37_decoy.fasta -s 15350X.bp.gz -t ~/BioApps/HTSlib/bin/
**************************************************************************************
**************************************************************************************
** Bam Pileup Meger: Sept 2021 **
**************************************************************************************
BPM merges BamPileup files, compresses, and indexes them.
Options:
-d Path to a directory of single sample bgzipped BamPileup files with the tbi indexes.
-p Path to save the output pileup file, must end in gz.
-t Path to the directory containing the compiled bgzip and tabix executables. See
https://www.htslib.org
-s Skip header check, use with caution!
Example: java -Xmx100G -jar pathTo/USeq/Apps/BamPileupMerger -d BPFiles/
-t ~/BioApps/HTSlib/1.10.2/bin -p abl.bp.txt.gz
**************************************************************************************
**************************************************************************************
** Bar2Gr: Nov 2006 **
**************************************************************************************
Converts xxx.bar to text xxx.gr files.
-f The full path directory/file text for your xxx.bar file(s).
Example: java -Xmx1500M -jar pathTo/T2/Apps/Bar2Gr -f /affy/BarFiles/
**************************************************************************************
**************************************************************************************
** Bar 2 USeq: Mar 2011 **
**************************************************************************************
Recurses through directories and sub directories of xxx.bar(.zip/.gz OK) files
converting them to xxx.useq files (http://useq.sourceforge.net/useqArchiveFormat.html).
Required Options:
-f Full path directory containing bar files or directories of bar files.
Default Options:
-i Index size for slicing split chromosome data (e.g. # rows per file),
defaults to 10000.
-r For graphs, select a style, defaults to 0
0 Bar
1 Stairstep
2 HeatMap
3 Line
-h Color, hexadecimal (e.g. #6633FF), enclose in quotations
-d Description, enclose in quotations
-g Reset genome version, defaults to that indicated by the bar files.
-e Delete original folders, use with caution.
-m Replace bar files with new xxx.useq file in bar file directory, use with caution.
Example: java -Xmx4G -jar pathTo/USeq/Apps/Bar2USeq -f
/AnalysisResults/ -i 5000 -h '#6633FF' -g D_rerio_Jul_2010
-d 'Final processed chIP-Seq results for Bcd and Hunchback, 30M reads'
**************************************************************************************
**************************************************************************************
** Bed2Bar: June 2010 **
**************************************************************************************
Bed2Bar builds stair step graphs from bed files for display in IGB. Strands are merged
and text information removed. Will also generate a merged bed file thresholding the
graph at that level.
-f Full path file or directory containing xxx.bed(.zip/.gz OK) files
-v Genome version (eg H_sapiens_Mar_2006), get from UCSC Browser,
http://genome.ucsc.edu/FAQ/FAQreleases.
-s Sum bed scores for overlapping regions, defaults to assigning the highest score.
-t Threshold, defaults to 0.
-g Maximum gap, defaults to 0.
Example: java -Xmx4G pathTo/Apps/Bed2Bar -f /affy/res/zeste.bed.gz -v
M_musculus_Jul_2007 -g 1000 -s -t 100
**************************************************************************************
**************************************************************************************
** BedTabix: Dec 2019 **
**************************************************************************************
Converts bed files to a SAMTools compressed bed tabix format. Recursive.
Required Options:
-v Full path file or directory containing xxx.bed(.gz/.zip OK) file(s). Recursive!
-t Full path tabix directory containing the compiled bgzip and tabix executables. See
http://sourceforge.net/projects/samtools/files/tabix/
-f Force overwriting of existing indexed bed files, defaults to skipping.
-d Do not delete non gzipped bed files after successful indexing, defaults to deleting.
-e Only print error messages.
Example: java -jar pathToUSeq/Apps/BedTabix -v /VarScan2/BEDFiles/
-t /Samtools/Tabix/tabix-0.2.6/
**************************************************************************************
**************************************************************************************
** Bed 2 UCSC RefFlat June 2015 **
**************************************************************************************
Takes a bed file and a UCSC gene table, intersects them and assigns each bed region
to a gene, then builds a new gene table using the bed region coordinates. Note, each
bed region must intersect only one gene. Modify the input gene table
(MergeUCSCGeneTable and manually trim) based on the errors. Lastly, all bed regions
must be assigned to genes.
Parameters:
-u UCSC RefFlat or RefSeq gene table file, full path. See,
http://genome.ucsc.edu/cgi-bin/hgTables, (geneName name2(optional) chrom strand
txStart txEnd cdsStart cdsEnd exonCount exonStarts exonEnds).
-b Bed file of regions to intersect with the gene table.
-t Don't remove UTRs if present, from the gene table.
-r Results file.
Example: java -Xmx2G -jar pathTo/USeq/Apps/Bed2UCSCRefFlat -u refSeqJun2015.ucsc
-b targetRegionsFat.bed -r targetRegionsFat.ucsc
**************************************************************************************
**************************************************************************************
** Bed Region Splitter : Nov 2022 **
**************************************************************************************
Regions exceeding the chunk size are split into multiple parts, each the minimum or
larger. The split regions can also be padded up to a provided length without
overlapping adjacent regions.
Required:
-d Path to a file or directory containing just bed files to split and or pad.
Optional:
-c Minimum BP chunk size, defaults to 2000.
-p Pad the split regions up to the pad size without intersecting adjacent split
regions. Note the input bed files must not have overlapping regions for this to
work. If in doubt run the MergeRegions app first.
Example: java -Xmx4G -jar pathTo/USeq/Apps/BedRegionSplitter -d ToSplit/ -c 150 -p 200
**************************************************************************************
**************************************************************************************
** BisSeq: June 2016 **
**************************************************************************************
Takes two condition (treatment and control) PointData from converted and non-converted
C bisulfite sequencing data parsed using the NovoalignBisulfiteParser and scores
regions for differential methylation using either a fisher exact or chi-square test
for changes in methylation. A Benjamini & Hockberg correction is applied to convert
the pvalues to FDRs. Data is only collected on bases that meet the minimum
read coverage threshold in both datasets. The fraction differential methylation
statistic is calculated by taking the pseudomedian of all of the log2 paired base level
fraction methylations in a given window. Overlapping windows that meet both the
FDR and pseLog2Ratio thresholds are merged when generating enriched and reduced
regions. BisSeq generates several tracks for browsing and lists of differentially
methlated regions. To examine only mCG contexts, first filter your PointData using the
ParsePointDataContexts app.
Options:
-s Save directory, full path.
-c Treatment converted PointData directories, full path, comma delimited. These should
contain stranded chromosome specific xxx_-/+_.bar.zip files fro the NBP app.
One can also provide a single directory that contains multiple PointData
directories.
-C Control converted PointData directories, ditto.
-n Treatment non-converted PointData directories, ditto.
-N Control non-coverted PointData directories, ditto.
-a Scramble control data.
Default Options:
-d Minimum per base read coverage, defaults to 5.
-w Window size, defaults to 250.
-m Minimum number methy C observations in window, defaults to 5.
-f FDR threshold, defaults to 30 (-10Log10(0.01)).
-l Log2Ratio threshold, defaults to 1.585 (3x).
-r Full path to R, defaults to '/usr/bin/R'
-g Don't print graph files.
Example: java -Xmx10G -jar pathTo/USeq/Apps/BisSeq -c /Sperm/Converted -n
/Sperm/NonConverted -C /Egg/Converted -N /Egg/NonConverted -s /Res/BisSeq
-w 500 -m 10 -l 2 -f 50
**************************************************************************************
**************************************************************************************
** Bis Seq Aggregate Plotter: October 2012 **
**************************************************************************************
BSAP merges bisulfite data over equally sized regions to generate data for class
average agreggate plots of fraction methylation. A smoothing window is also applied.
Data for unstranded, sense, and antisense are produced.
Options:
-c Converted PointData directories, full path, comma delimited. These should
contain stranded chromosome specific xxx_-/+_.bar.zip files. One
can also provide a single directory that contains multiple PointData
directories. See the NovoalignBisulfiteParser app.
-n Non-converted PointData directories, ditto.
-b Bed file (tab delim: chr start stop name score strand(+/-/.)), full path.
-i Don't invert - stranded regions, defaults to inverting.
-s Scale all regions to a particular size. Defaults to scaling to max region size.
-m Calculate individual base fractions and then take a mean, ignoring zeros, over
the window, instead of summing the obs in the window and taking the fraction.
-o Minimum number of observations before scoring base fraction methylation, defaults
to 8.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/BisSeqAgregatePlotter -c
/NBP/Con -n /NBP/NonCon -b /Anno/tssSites.bed -m
**************************************************************************************
**************************************************************************************
** BisSeqErrorAdder: June 2012 **
**************************************************************************************
Takes PointData from converted and non-converted C bisulfite sequencing data parsed
using the NovoalignBisulfiteParser and simulates a worse non-coversion rate by
randomly picking converted observations and making them non-converted. This is
accomplished by first measuring the non-conversion rate in the test chromosome (e.g.
chrLambda), calculating the fraction of converted C's need to flip to non-converted
to reach the target fraction non-converted and then using this flip fraction
to modify the other chromosome data.
Options:
-s Save directory, full path.
-c Converted PointData directories, full path, comma delimited. These should
contain stranded chromosome specific xxx_-/+_.bar.zip files. One
can also provide a single directory that contains multiple PointData
directories.
-n Non-converted PointData directories, ditto.
-f Target fraction non-converted for test chromosome, this cannot be less than the
current fraction.
-t Test chromosome, defaults to chrLambda* .
Example: java -Xmx12G -jar pathTo/USeq/Apps/BisSeqErrorAdder -c /Data/Sperm/Converted
-n /Data/Sperm/NonConverted -f 0.02
**************************************************************************************
**************************************************************************************
** BisStat: June 2018 **
**************************************************************************************
Takes PointData from converted and non-converted C bisulfite sequencing data parsed
using the NovoalignBisulfiteParser and generates several xxCxx context statistics and
graphs (bp and window level fraction converted Cs) for visualization in IGB.
BisStat estimates whether a given C is methylated using a binomial distribution where
the expect can be calculated using the fraction of non-converted Cs present in the
lambda data. Binomial p-values are converted to FDRs using the Benjamini & Hochberg
method. This app requires considerable RAM (10-64G).
Options:
-s Save directory, full path.
-c Converted PointData directories, full path, comma delimited. These should
contain stranded chromosome specific xxx_-/+_.bar.zip files. One
can also provide a single directory that contains multiple PointData
directories.
-n Non-converted PointData directories, ditto.
-f Directory containing chrXXX.fasta(/.fa .zip/.gz OK) files for each chromosome.
Default Options:
-p Minimimal FDR for non-converted C's to be counted as methylated, defaults to 20 a
-10Log10(FDR = 0.01) conversion.
-e Expected fraction non-converted Cs due to partial bisulfite conversion and
sequencing error, defaults to 0.005 .
-l Use the unmethylated lambda alignment data to set the expected fraction of
non-converted Cs due to partial conversion and sequencing error. This is
predicated on including a 'chrLambda' fasta sequence while aligning your data.
-o Minimum read coverage to count mC fractions, defaults to 8
-w Window size, defaults to 1000.
-m Minimum number Cs passing read coverage in window to score, defaults to 5.
-r Full path to R, defaults to '/usr/bin/R'
-g Don't merge stranded data, defaults to running a non stranded analysis. Affects CG's.
-a First density quartile fraction methylation threshold, defaults to 0.25
-b Fourth density quartile fraction methylation threshold, defaults to 0.75
Example: java -Xmx12G -jar pathTo/USeq/Apps/BisStat -c /Data/Sperm/Converted -n
/Data/Sperm/NonConverted -s /Data/Sperm/BisSeq -w 5000 -m 10 -f
/Genomes/Hg18/Fastas -o 10
**************************************************************************************
**************************************************************************************
** BisStat Region Maker: Nov 2014 **
**************************************************************************************
Takes serialized window objects from BisStat, thresholds based on the min and max
fraction methylation params and prints regions in bed format meeting the criteria.
May also build regions base on the density of a given fraction methylation quartile.
For example, to identify regions where at least 0.8 of the sequenced Cs are low
methylated (<= 0.25 default settings in BisStat) set -q 1 -m 0.8 . To find regions of
with >= 0.9 of the Cs with high methylation (>= 0.75 default BisStat setting), set
-q 3 -m 0.9 .
Options:
-s SerializedWindowObject directory from BisStat, full path.
-m Minimum fraction.
-x Maximum fraction.
-g Maximum gap, defaults to 0.
-q Merge windows based on their quartile density score, not fraction methylation, by
indicating 1,2,or 3 for 1st, 2nd+3rd, or 4th, respectively.
-r Full path to R, defaults to '/usr/bin/R'
Example: java -Xmx4G -jar pathTo/USeq/Apps/BisStatRegionMaker -m 0.8 -x 1.0 -g 100
-s /Data/BisStat/SerializedWindowObjects
**************************************************************************************
**************************************************************************************
** Calculate Per Cycle Error Rate : July 2015 **
**************************************************************************************
Calculates per cycle snv error rate provided a sorted indexed bam file and a fasta
sequence file. Only checks CIGAR M bases not masked or INDEL bases.
Required Options:
-b Full path to a coordinate sorted bam file (xxx.bam) with its associated (xxx.bai)
index or directory containing such. Multiple files are processed independently.
-f Full path to the single fasta file you wish to use in calculating the error rate.
Default Options:
-s Perform separate first read second read analysis, defaults to merging.
-c Maximum fraction failing cycles, defaults to 0.1
-1 Maximum first read or merged read error rate, defaults to 0.01
-2 Maximum second read error rate, defaults to 0.0175
-o Write coverage statistics to this log file instead of stdout.
-j Write summary stats in json format to this file. Only stats for the first bam file
are saved. Only separate strand analysis permitted.
-m Set minimum mapping quality for inclusion. Default: 0.
-p Require that a read be mapped in a proper pair for inclusion in error rate calculations.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/CalculatePerCycleErrorRate
-b /Data/Bam/ -f /Fastas/chrPhiX_Illumina.fasta.gz
**************************************************************************************
**************************************************************************************
** Caris Data Wrangler : Aug 2022 **
**************************************************************************************
The Caris Data Wrangler downloads complete patient datasets from an AWS bucket, parses
the xml test file for patient info, fetches/makes coreIds using the SubjectMatchMaker
app and assembles TNRunner compatible analysis directories. If indicated, it will
delete AWS objects that were successfully downloaded. A deidentified version of the
xml report is saved. Do look at the log output at the failing patient datasets and
resolve the issues (e.g. multiple xmls, multiple vcf, for the same Caris ID).
Options:
-b S3 URI where Caris files are placed
-j Directory to build patient job folders
-r Directory to place PHI Caris xml reports
-s Directory to place temp files with PHI for Subject ID matching
-c Directory containing the SubjectMatchMaker 'currentRegistry_' file
-t Minimum hours old before downloading, defaults to 12
-p Credentials profile, defaults to 'default'
-d Delete S3 objects after successful download
Example: java -jar pathToUSeq/Apps/CarisDataWrangler -b s3://hci-caris/
-j ~/Scratch/Caris/CJobs/ -r ~/Scratch/Caris/XmlReports_PHI/ -c
~/Scratch/SMM/Registry_PHI -s ~/Scratch/Caris/SMM_Tmp_PHI
**************************************************************************************
**************************************************************************************
** Caris Vcf Comparator: March 2022 **
**************************************************************************************
CVC compares a Caris vcf generated with the CarisXmlVcfParser to a recalled vcf.
Exact recall vars are so noted and removed. Caris vcf with no exact but one
overlapping record can be merged with -k. Be sure to vt normalize each before running.
Recall variants failing FILTER are not saved.
Options:
-c Path to a the Caris xml parsed vcf file, see the CarisXmlVcfParser app.
-r Path to a recalled snv/indel vcf file.
-m Path to named vcf file for saving the results.
Example: java -Xmx2G -jar pathToUSeq/Apps/CarisVcfComparator -f TL-18-03CFD6.vcf
-r /F1/TL-18-03CFD6_recall.vcf.gz -g -c -m /F1/TL-18-93CFD6_merged.vcf.gz -k
**************************************************************************************
**************************************************************************************
** Caris Xml Vcf Parser: Apr 2023 **
**************************************************************************************
This tool parses Caris paired xml and vcf report files to generate: new vcfs where xml
reported genomic alternations are annotated, bed files of copy number changes and gene
fusions as well as a summary spreadsheet of the non NGS test results and report
attributes. Use Vt to normalize the vcf output files.
Options:
-d Path to a directory containing paired xml and vcf files from Caris.
-f Alternatively, a path to a tab delimited file where each line contains the dataset
name, the xml file path, and the vcf file.
-s Path to a directory for saving the results. Only impacting vcf, cnv, fusions are
saved, if none present, no file(s) are created.
-u Path to a Hg38 UCSC RefFlat or RefSeq merged gene file for CNV and gene fusion
coordinate extraction. See: http://www.genome.ucsc.edu/FAQ/FAQformat.html#format9
(refSeqGeneName name2(optional) chr strand txStart txEnd cdsStart cdsEnd exonCount
exonStarts exonEnds). Be sure to merge transcripts with the USeq MergeUCSCGeneTable
app and remove non standard chromosomes.
-i Include PHI in spreadsheet output, defaults to excluding.
-a Include all vcf records in output, defaults to just those with an xml match.
-k Skip datasets that are missing either an xml or vcf report.
Example: java -Xmx2G -jar pathToUSeq/Apps/CarisXmlVcfParser -d CarisReports/
-s ParsedCarisReports/ -u ~/GRCh38/hg38RefSeq25Dec2020_MergedStdChr.ucsc.gz
**************************************************************************************
**************************************************************************************
** Correlate: Nov 2008 **
**************************************************************************************
Calculates all pair-wise Pearson correlation coefficients (r) and if indicated will
perform a hierarchical clustering on the files.
Parameters:
-d The full path directory text containing serialized java float[] files (xxx.celp
see CelProcessor app).
-a Files provided are float[][] files (xxx.cela) and need to be collapsed to float[]
-c Cluster files.
Example: java -Xmx256M -jar pathTo/T2/Apps/Correlate -d /Mango/PCels/ -c -a
**************************************************************************************
**************************************************************************************
** CBio 2 Sgr : Dec 2022 **
**************************************************************************************
Converts the cBioPortal mutation table download tsv file to a center position svg file
for subsequent mutation density plotting. Removes duplicate sample variants.
Options:
-d Directory containing xxx.tsv files for parsing.
-s Skip adding sample names to output.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/CBio2Sgr -d GeneTableFiles
**************************************************************************************
**************************************************************************************
** ChIPSeq: May 2014 **
**************************************************************************************
The ChIPSeq application is a wrapper for processing ChIP-Seq data through a variety of
USeq applications. It:
1) Parses raw alignments (sam, eland, bed, or novoalign) into binary PointData
2) Filters PointData for duplicate alignments
3) Makes relative ReadCoverage tracks from the PointData (reads per million mapped)
4) Runs the PeakShiftFinder to estimate the peak shift and optimal window size
5) Runs the MultipleReplicaScanSeqs to window scan the genome generating enrichment
tracks using DESeq2's negative binomial pvalues and B&H's FDRs
6) Runs the EnrichedRegionMaker to identify likely chIP peaks (FDR < 1%, >2x).
Options:
-s Save directory, full path.
-t Treatment alignment file directories, full path, comma delimited, no spaces, one
for each biological replica. These should each contain one or more text
alignment files (gz/zip OK) for a particular replica. Alternatively, provide
one directory that contains multiple alignment file directories.
-c Control alignment file directories, ditto.
-y Type of alignments, either novoalign, sam, bed, or eland (sorted or export).
-v Genome version (e.g. H_sapiens_Feb_2009, M_musculus_Jul_2007), see UCSC FAQ,
http://genome.ucsc.edu/FAQ/FAQreleases.
-r Full path to R, defaults to '/usr/bin/R'. Be sure to install DESeq2, gplots, and
qvalue Bioconductor packages.
Advanced Options:
-m Combine any replicas and run single replica analysis (ScanSeqs), defaults to
using DESeq2.
-a Maximum alignment score. Defaults to 60, smaller numbers are more stringent.
-q Minimum mapping quality score. Defaults to 13, bigger numbers are more stringent.
This is a phred-scaled posterior probability that the mapping position of read
is incorrect. Set to 0 for RNASeq data.
-p Peak shift, defaults to the PeakShiftFinder peak shift or 150bp. Set to 0 for
RNASeq data.
-w Window size, defaults to the PeakShiftFinder peak shift + stnd dev or 250bp.
-i Minimum number reads in window, defaults to 10.
-f Filter bed file (tab delimited: chr start stop) to use in excluding intersecting
windows while making peaks, e.g. satelliteRepeats.bed .
-g Print verbose output from each application.
-e Don't look for reduced regions.
Example: java -Xmx2G -jar pathTo/USeq/Apps/ChIPSeq -y eland -v D_rerio_Dec_2008 -t
/Data/PolIIRep1/,/Data/PolIIRep2/ -c /Data/PolIINRep1/,/Data/PolIINRep2/ -s
/Data/Results/WtVsNull -f /Anno/satelliteRepeats.bed
**************************************************************************************
**************************************************************************************
** Compare Intersecting Regions: Nov 2012 **
**************************************************************************************
Compares test region file(s) against a master set of regions for intersection.
Reports the results as columns relative to the master. Assumes interbase coordinates.
Options:
-m Full path for the master bed file (tab delim: chr start stop ...).
-t Full path to the test bed file to intersect or directory of files.
-g Maximum bp gap allowed for scoring an intersection, defaults to 0 bp. Negative gaps
force overlaps, positive gaps allow non intersecting bases between regions.
Example: java -Xmx4G -jar pathTo/Apps/CompareIntersectingRegions -g 1000
-m /All/mergedRegions.bed.gz -t /IndividualERs/
************************************************************************************
**************************************************************************************
** Compare Intersecting Vcfs : June 2019 **
**************************************************************************************
Compares vcf files by creating a master list of variants and then scores each for the
presense of the same CHROM POS ALT REF in each vcf file.
Options:
-v A directory of vcf files to compare (xxx.vcf(.gz/.zip OK)).
-r Name of a spreadsheed results file, should end in xxx.txt.gz
Example: java -Xmx10G -jar pathTo/USeq/Apps/CompareIntersectingVcfs -v VCFs/
-r comparisonVcf.txt.gz
**************************************************************************************
**************************************************************************************
** Compare Parsed Alignments: Nov 2009 **
**************************************************************************************
Compares two parsed alignments for a common distribution of snps using R's Fisher's
Exact. Run the ParseIntersectingAlignments with the same snp table first.
Options:
-a Full path file name for the first xxx.alleles file.
-b Full path file name for the first xxx.alleles file.
-d Full path directory name for writing temporary files.
-r Full path file name for R, defaults to '/usr/bin/R'
Example: java -Xmx1500M -jar pathToUSeq/Apps/CompareParsedAlignments.
-a /SeqData/lymphSNPs.alleles -b /SeqData/normalSNPs.alleles -b /temp/
**************************************************************************************
**************************************************************************************
** Concatinate Fastas: Oct 2010 **
**************************************************************************************
Concatinates a directory of fasta files into a single sequence seperated by a defined
number of Ns. Outputs the merged fasta as well as bed files for the junctions and
spacers as well as a file to be used to shift UCSC gene table annotations. Use this
app to create artificial chromosomes for poorly assembled genomes.
Options:
-d Full path directory for saving the results.
-f Full path directory containing fasta files to concatinate.
-n Number of Ns to use as a spacer, defaults to 1000.
-c Name to give the concatinate, defaults to chrConcat .
Example: java -Xmx4G -jar pathTo/USeq/Apps/ConcatinateFastas -n 2000 -d
/zv8/MergedNA_Scaffolds -f /zv8/BadFastas/ -c chrNA_Scaffold
**************************************************************************************
**************************************************************************************
** Correct VCF Ends: July 2017 **
**************************************************************************************
Use to correct the END=xxx tags in a Crossmap vcf . Removes any MC tags. Adds chr.
Required Options:
-v Path to the Crossmap vcf file.
-b Path to the VCF2Bed -> Crossmap bed file
-r Path to save the modifed gzipped file.
Ex: java -jar USeq_XXX/Apps/CorrectVCFEnds -v b38.vcf -b b38.bed -r finalB38.vcf.gz
**************************************************************************************
**************************************************************************************
** CorrelatePointData: Aug 2011 **
**************************************************************************************
Calculates a Pearson Correlation Coefficient on the values of PointData found with the
same positions in the two datasets. Do NOT use on stair-step/ heat-map graph data.
Only use on point representation data.
Options:
-f First PointData set. This directory should contain chromosome specific xxx.bar.zip
files, stranded or unstranded.
-s Second PointData set, ditto.
-p Full path file name to use in saving paired scores, defaults to not printing.
Example: java -Xmx4G -jar pathTo/USeq/Apps/CorrelatePointData -f /BaseFracMethyl/X1
-s /BaseFracMethyl/X2
**************************************************************************************
**************************************************************************************
** Bisulfite Convert Fastas: Dec 2008 **
**************************************************************************************
Converts all the c/C's to t/T's in fasta file(s) maintaining case.
Required Parameters:
-f Full path text for the xxx.fasta file or directory containing such.
Example: java -Xmx2000M -jar pathTo/Apps/BisulfiteConvertFastas -f /affy/Fastas/
**************************************************************************************
**************************************************************************************
** Consensus : March 2017 **
**************************************************************************************
Consensus clusters alignments sharing the same unclipped start position and molecular
barcode. It then calls consensus on the clustered alignments outputing fastq for
realignment and unmodified bam records. After running, align the fastq files and merge
the new bams with those in the save directory.
Required arguments:
-b Path to the mate matched bam file created by FastqBarcodeTagger | cutadapt | bwa |
MatchMates. See FBT and MM for details.
Optional Arguments:
-s Path to a directory to save the results, defaults to a derivative of the
bam file.
-t Number concurrent threads to run, defaults to the max available to the jvm / 2.
-c Number of alignments to process in one chunk, defaults to 1,000,000. Adjust for the
availible RAM.
-x Maximum number of alignments to cluster before subsampling, defaults to 20000.
-q Minimum barcode base quality, defaults to 13, anything less is assigned an N.
-n Minimum number of non N barcode bases, defaults to 7, anything less is tossed.
-f Minimum fraction barcode identity for inclusion in a cluster, defaults to 0.875 .
-u Minimum read base quality for inclusion in consensus calling, defaults to 13.
-r Minimum read base fraction identity to call a consensus base, defaults to 0.66 .
Anything less is assigned an N.
Example: java -Xmx100G -jar pathTo/USeq/Apps/Consensus -b MM/passingMM.sorted.bam
**************************************************************************************
**************************************************************************************
** Correlation Maps: Nov 2007 **
**************************************************************************************
CM calculates a correlation score for each window of genes and using permutation, an
empirical p-value. The correlation score is the mean of all pair Spearman ranks for
the gene expression profiles in each window. If a single value is given (unlogged!) for
each gene, a mean of the scores within each window is calculated.
To calculate p-values, X randomized datasets are created by shuffling the expression
profiles between genes, windows are scored and pooled. P-values for each real
score are calculated based on the area under the right side of the randomized score
distribution. In addition to a spread sheet report summary, heat map xxx.bar files
for the p-values and mean correlation are created for visualization in IGB.
Note, this analysis is not stranded. If so desired parse lists appropriately.
Parameters:
-f The full path file text for a tab delimited gene file (text,chr,start,stop,scores)
-o GenomicRegion filter file, full path file text for a tab delimited region file to use in
removing genes from correlation analysis. (chrom, start, stop).
-g Genome version for IGB visualizations (e.g. C_elegans_May_2007).
-w Window size, default is 50000bp. Setting this too small may exclude some regions.
-n Minimum number of genes required in each window, defaults to 3. Setting this too
high will exclude some regions.
-r Number random trials, defaults to 100
Example: java -Xmx256M -jar pathTo/T2/Apps/CorrelationMaps -f /Mango/geneFile.txt
-w 30000 -n 2 -o /Mango/operons.txt
**************************************************************************************
**************************************************************************************
** Defined Region Bis Seq: Dec 2013 **
**************************************************************************************
Takes two condition (treatment and control) PointData from converted and non-converted
C bisulfite sequencing data parsed using the NovoalignBisulfiteParser and scores user
defined regions for differential methylation using either a fisher or chi-square test.
A Benjamini & Hockberg correction is applied to convert the pvalues to FDRs. Data is
only collected on Cs that meet the minimum read coverage threshold in both datasets.
The fraction differential methylation statistic is calculated by taking the
pseudomedian of all of the log2 paired base level fraction methylations in a given
region. To examine particular mC contexts (e.g. mCG), first filter your PointData
using the ParsePointDataContexts app.
Options:
-b A bed file of regions to score (tab delimited: chr start stop ...)
-s Save directory, full path.
-c Treatment converted PointData directories, full path, comma delimited. These should
contain stranded chromosome specific xxx_-/+_.bar.zip files fro the NBP app.
One can also provide a single directory that contains multiple PointData
directories.
-C Control converted PointData directories, ditto.
-n Treatment non-converted PointData directories, ditto.
-N Control non-coverted PointData directories, ditto.
Default Options:
-d Minimum per base read coverage, defaults to 5.
-r Full path to R, defaults to '/usr/bin/R'
Example: java -Xmx10G -jar pathTo/USeq/Apps/DefinedRegionBisStat -c /Sperm/Converted
-n /Sperm/NonConverted -C /Egg/Converted -N /Egg/NonConverted -s /Res/DRBS
-b /Res/CpGIslands.bed
**************************************************************************************
**************************************************************************************
** Defined Region Differential Seq: May 2019 **
**************************************************************************************
DRDS takes sorted bam files, one per replica, minimum one per condition, minimum two
conditions (e.g. treatment and control or a time course/ multiple conditions) and
identifies differentially expressed genes using DESeq2 or SAMTools. DESeq2's rLog
normalized count data is used to heirachically cluster the samples. Differential
splicing is estimated using a chi-square test of independence. When testing only a
few genes or regions, append these onto a full gene table so that DESeq2 can
appropriately estimate the library size and replica variance.
Options:
-s Save directory.
-c Conditions directory containing one directory for each condition with one xxx.bam
file per biological replica and their xxx.bai indexs. 3-4 reps recommended per
condition. The BAM files should be sorted by coordinate using Picard's SortSam.
All spice junction coordinates should be converted to genomic coordinates, see
USeq's SamTranscriptomeParser.
-r Full path to R (version 3+) loaded with DESeq2, samr, and gplots defaults to
'/usr/bin/R' file, see http://www.bioconductor.org . Type 'library(DESeq2);
library(samr); library(gplots)' in R to see if they are installed.
-u UCSC RefFlat or RefSeq gene table file, full path. Tab delimited, see RefSeq Genes
http://genome.ucsc.edu/cgi-bin/hgTables, (uniqueName1 name2(optional) chrom
strand txStart txEnd cdsStart cdsEnd exonCount (commaDelimited)exonStarts
(commaDelimited)exonEnds). Example: ENSG00000183888 C1orf64 chr1 + 16203317
16207889 16203385 16205428 2 16203317,16205000 16203467,16207889 . NOTE:
this table should contain only ONE composite transcript per gene (e.g. use
Ensembl genes NOT transcripts). Use the MergeUCSCGeneTable app to collapse
transcripts. See http://useq.sourceforge.net/usageRNASeq.html for details.
-b (Or) a bed file (chr, start, stop,...), full path, See,
http://genome.ucsc.edu/FAQ/FAQformat#format1
-g Genome Version (ie H_sapiens_Mar_2006), see UCSC Browser,
http://genome.ucsc.edu/FAQ/FAQreleases.
-f Turn off DESeq2 independent filtering.
Advanced Options:
-m Mask overlapping gene annotations, recommended for well annotated genomes.
-x Max per base alignment depth, defaults to 50000. Genes containing such high
density coverage are ignored.
-n Max number alignments per read. Defaults to 1, unique. Assumes 'NH' tags have
been set by processing raw alignments with the SamTranscriptomeProcessor.
-e Minimum number alignments per gene-region per replica, defaults to 10.
-q Minimum alignment mapping quality, defaults to 0, no filtering.
-i Score introns instead of exons.
-p Perform a stranded analysis. Only collect reads from the same strand as the
annotation.
-j Reverse stranded analysis. Only collect reads from the opposite strand of the
annotation. This setting should be used for the Illumina's strand-specific
dUTP protocol.
-k Second read's strand is flipped. Otherwise, assumes this was not done in the
SamTranscriptomeParser.
-t Don't delete temp files (R script, R results, Rout, etc..).
-a Run SAMseq in place of DESeq2. This is only recommended with five or more
replicates per condition.
-v Use these 3 -10Log10(AdjPVal) thresholds, comma delimited, no spaces, defaults
to 10,20,30
-w Use these 3 absolute log2 ratio thresholds, comma delimited, no spaces, defaults
to 0.585,1,1.585
-y Add in non phred AdjPVal columns, defaults to excluding.
Example: java -Xmx4G -jar pathTo/USeq/Apps/DefinedRegionDifferentialSeq -c
/Data/TimeCourse/ESCells/ -s /Data/TimeCourse/DRDS -g H_sapiens_Feb_2009
-u /Anno/mergedHg19EnsemblGenes.ucsc.gz -w 0.322,0.585,1 -y -q 13
**************************************************************************************
**************************************************************************************
** Defined Region RNA Editing: April 2014 **
**************************************************************************************
DRRE scores regions for the pseudomedian of the base fraction edits as well as the
probability that the observations occured by chance using a permutation test based on
the chiSquare goodness of fit statistic.
Options:
-b A bed file of regions to score (tab delimited: chr start stop ...)
-e Edited PointData directory from the RNAEditingPileUpParser.
These should contain stranded chromosome specific xxx_-/+_.bar.zip files. One
can also provide a single directory that contains multiple PointData
directories. These will be merged when scanning.
-r Reference PointData directory from the RNAEditingPileUpParser. Ditto.
-a Minimum base read coverage, defaults to 5.
-t Run a stranded analysis, defaults to non-stranded.
-i Remove base fraction edits that are non zero and represented by just one edited
base.
Example: java -Xmx4G -jar pathTo/USeq/Apps/DefinedRegionRNAEditing -b hg19UTRs.bed
-e /PointData/Edited -r /PointData/Reference
**************************************************************************************
**************************************************************************************
** Defined Region Scan Seqs: March 2011 **
**************************************************************************************
DRSS takes chromosome specific PointData xxx.bar.zip files and extracts scores under
each region to calculate several statistics including a binomial p-value, Storey
q-value FDR, an empirical FDR, a p-value for strand skew, and a chi-square test of
independence between the exon read count distributions between treatment and control
data (a test for alternative splicing). Several measures of read counts are provided
including counts for each strand, a normalized log2 ratio, and RPKMs (# reads per kb
of interrogated region per total million mapped reads). If a gene table is provided,
scores under each exon are summed to give a whole gene summary. It is also recommended
to run a gene table of introns (see the ExportIntronicRegions app) to look for
intronic retention and novel transfrags/ exons. If one provides splice junction bed
files for treatment and control RNA-Seq data, see the NovoalignParser, splice
junctions will be scored for differential expression. This is an additional
calculation unrelated to the chi-square independance test. Lastly, if control
data is not provided, simple region sums are calculated.
Options:
-s Save directory, full path.
-t Treatment PointData directories, full path, comma delimited. These should
contain unshifted stranded chromosome specific xxx_-/+_.bar.zip files. One
can also provide a single directory that contains multiple PointData
directories.
-c Control PointData directories, ditto.
-p Peak shift, average distance between + and - strand peaks for chIP-Seq data, see
PeakShiftFinder. For RNA-Seq set to the smallest expected fragment size. Will
be used to shift the PointData 3' by 1/2 the peak shift.
-r Full path to R loaded with Storey's q-value library, defaults to '/usr/bin/R'
file, see http://genomics.princeton.edu/storeylab/qvalue/
-u UCSC RefFlat or RefSeq gene table file, full path. See,
http://genome.ucsc.edu/cgi-bin/hgTables, (name1 name2(optional) chrom strand
txStart txEnd cdsStart cdsEnd exonCount exonStarts exonEnds)
-b (Or) a bed file (chr, start, stop,...), full path, See,
http://genome.ucsc.edu/FAQ/FAQformat#format1
Advanced Options:
-o Don't remove overlapping exons, defaults to filtering gene annotation for overlaps.
-i Score introns instead of exons.
-f Scan for just enriched regions, defaults to look for both. Only use with chIP-Seq
datasets where the control is input. This turns on the empFDR estimation.
-d Treatment splice junction bed file(s) from the NovoalignParser, comma delimited,
full path.
-e Control splice junction bed file(s), comma delimited, full path.
-m Minimum number of reads in associated gene before scoring splice junctions.
Used in estimating the expected proportion of T and scaling the log2Ratio.
Defaults to 100.
-w Use read score probabilities (assumes scores are > 0 and <= 1), defaults to
assigning 1 to each read score. Experimental.
Example: java -Xmx4G -jar pathTo/USeq/Apps/DefinedRegionScanSeqs -t
/Data/PolIIRep1/,/Data/PolIIRep2/ -c /Data/Input1/,Data/Input2/ -s
/Data/PolIIResults -p 100 -b /Data/selectRegions.bed -f
**************************************************************************************
**************************************************************************************
** DRDS Annotator: January 2014 **
**************************************************************************************
This application annotates DefinedRegionDifferentialSeq xlsx files using Ensembl
biomart tab-delimited annotation files. By default, ensembl biomart output files will
list the Ensembl gene id in the first column and Ensembl transcript id in the second
column. This application assumes these defaults. It will match the gene id in the
first column of the biomart file to the name listed in the 'IGB HyperLink' column
found in the 'Analyzed Genes' tab of the DRDS xlxs output. All biomart columns after
the transcript id column are added to the output file. The data is inserted between
the 'Alt Name' and locus columns in the 'Analyzed Genes' tab.
The biomart output files can have multiple annotation lines for each gene id.
Currently, this app uses the first annotation line encountered.
Required Arguments:
-i Input file. Path to DRDS xlsx output file you wish to annotate
-a Annotation file. Path to biomart annotation file.
-o Annotated output file. Path to the annotated output file
Example: java -Xmx4G -jar pathTo/USeq/Apps/DRDSAnnotator -i geneStats.xlsx
-a mm10.biomart.txt -o geneStats.ann.xlsx
**************************************************************************************
**************************************************************************************
** Enriched Region Maker: July 2013 **
**************************************************************************************
ERM combines windows from ScanSeqs xxx.swi files into larger enriched or reduced
regions based on one or more scores. For each score index, you must provide a minimal
score. Adjacent windows that exceed the minimum score(s) are merged and the best
window scores applied to the region. If treatment and control PointData are provided,
the best 25bp peak within each region will be identified and each ER rescored. To
select for ERs with a 1% FDR and 2x enrichment above control, follow the example
assuming score indexes 1,2,4 correspond to QValFDR, EmpFDR, and
Log2Ratio. Note, if you are performing a static analysis comparing chIP vs chIP,
don't set thresholds on the EmpFDR, this was disabled and all of the values are zero.
To print descriptions of the score indexes, complete the command line and skip the
-i option. Lastly, FDRs and p-values are represented in USeq in a transformed state,
as -10Log10(FDR/p-val) where 13 = 5%, 20 = 1%, etc. To select for regions with an FDR of
less than 1% you would set a threshold of 20 for the QValFDR and, if running a static
analysis, the EmpFDR.
Options:
-f Full path file name for the serialized xxx.swi file from ScanSeqs, if a
directory is specified, all xxx.swi files will be processed.
-s Minimal score(s) one for each score idex, comma delimited, no spaces.
-i Score index(s) one for each minimum score.
Advanced Options:
-n Make a given number of ERs, one or more, comma delimited, no spaces. Uses score
index 0.
-m Multiply scores by -1 to make reduced regions instead of enriched regions.
-r Remove windows that intersect a list of regions. Enter a full path tab delimited
regions file text (chr start stop) Coordinates are assumed to be zero based and
stop inclusive. Useful for excluding regions from ER generation.
-b BP buffer to subtract and add to start and stops of regions used in filtering
intersecting windows, defaults to 0.
-e Exclude entire ERs that intersect the -r regions, defaults to removing windows.
This is more exclusive and will not simply punch holes in ERs but throw out
The entire ER.
-g Max gap, defaults to the size of the window used in ScanSeqs.
-t Provide treatment PointData directories, full path, comma delimited to ID the peak
center in each ER. These should contain the same unshifted stranded chromosome
specific xxx_-/+_.bar.zip files used in ScanSeqs.
-c Control PointData directories, ditto.
-p Full path to R, defaults to '/usr/bin/R', required for rescanning ERs.
-w Sub window size, defaults to 25bp.
Example: java -Xmx500M -jar pathTo/USeq/Apps/EnrichedRegionMaker -f /solexa/zeste.swi
-i 1,2,4 -s 20,20,1 -w 50
**************************************************************************************
**************************************************************************************
** Estimate Error Rates: May 2023 **
**************************************************************************************
EER scans an mpileup file looking for short windows of adjacent bps (default 7) where
1) each base exceeds a minimum read depth of high quality bases (>100)
2) shows little evidence of indels (<0.1), and
3) the fraction of poor quality bps isn't excessive (<0.5).
The non reference snv observations are then tabulated for the center base in each
window, if low (<0.1), they are assumed error and saved. For indel error calculations,
each bp is scored as above sans the indel filter and window requirement. Insertions
are counted once regardless of the size, where as deletions are counted for every base
affected. Run this app on samples where real snvs and indels are expected to have an
allele frequency of > ~0.5 , e.g. normal or pure single clone somatic.
Required Options:
-m Path to a normal sample mpileup file (gz/zip OK), 'samtools mpileup -B -q 20 -d
1000000 -f $fastaIndex -l $bedFile *.bam | gzip > mpileup.gz' Multiple samples
in the file are merged.
Default Options:
-b Minimum base quality, default 20
-r Minimum good base coverage, default 100
-i Maximum INDEL allele freq for snv counting, default 0.1
-n Maximum non reference allelic freq, default 0.1
-p Maximum failing base allele freq, default 0.5
-f Number flanking bp to define scorable region, default 3
-s Comma delimited list (zero is 1st sample, no spaces) of sample indexes to merge,
defaults to all.
-c File path to save a count table of parsed observations, defaults to none.
Example: java -Xmx4G -jar pathToUSeq/Apps/EstimateErrorRates -m normExo.mpileup.gz
-r 200 -i 0.15 -f 2 -s 0,3,4 -c countTable.txt
**************************************************************************************
**************************************************************************************
** Exact Bam Mixer : May 2020 **
**************************************************************************************
Combines bam alignment files in different fractions to simulate multiple variant
frequencies. Run BamBlaster first. Threaded, so provide almost all the memory available
to java. The ExactBamMixer attempts to create bam files containing variants will very
similar AFs. The BamMixer produces more of a spread of AFs. Ignore Picard and log4j
NOTE and ERROR output. These are just warnings from Picard that can't be silenced.
Required:
-r Path to a directory to save the results
-u Path to the xxx_unmodified.bam from your BamBlaster run
-f Path to the xxx_filtered.bam from your BamBlaster run
-i Path to your realigned bam containing injected variants, merge the single and
paired end alignment files with MergeSams USeq app.
-v Path to the vcf file containing variants used to modify the BamBlaster alignments
Optional:
-m Fractions to mix in the variant alignments, comma delimited, no spaces, defaults to
0.025,0.05,0.1,0.2
-t Number of threads to use, defaults to all
-a Minimum number alt read pairs to include an injected variant in a particular mixed
bam, defaults to 2
-n Name to prepend onto the read group, defaults to EBM
Example: java -Xmx100G -jar pathTo/USeq/Apps/ExactBamMixer -r ~/TumorSim/ -v snv.vcf
-u bb_unmodified.bam -f bb_filtered.bam -i bb_mergedReAlign.bam -n Snv
**************************************************************************************
**************************************************************************************
** Export Exons Nov 2014 **
**************************************************************************************
EE takes a UCSC Gene table and prints the exons to a bed file.
Parameters:
-g Full path file text for the UCSC Gene table.
-a Expand the size of each exon by X bp, defaults to 0
-u Remove UTRs if present, defaults to including
-n Append exon numbers to the gene name field. This makes the bed file compatible
with DRDS
-f Export just 5' UTRs
Example: java -Xmx1000M -jar pathTo/T2/Apps/ExportExons -g /user/Jib/ucscPombe.txt
-a 50
**************************************************************************************
**************************************************************************************
** Export Intergenic Regions May 2007 **
**************************************************************************************
EIR takes a gff file and uses it to mask a boolean array. Parts of the boolean array
that are not masked are returned and represent integenic sequences. Be sure to put in
a gff line at the stop of each chromosome noting the last base so you caputure the last
intergenic region. (eg chr1 GeneDB lastBase 3600000 3600001 . + . lastBase). Base
coordinates are assumed to be stop inclusive, not interbase.
Parameters:
-g Full path file text for a gff file or directory containing such.
-t Base pairs to trim from the ends of each intergenic region, defaults to 0.
-m Minimum acceptable intergenic size, those smaller will be tossed, defaults to 60bp
-s Subtract one from the start and stop coordinates.
Example: java -Xmx1000M -jar pathTo/T2/Apps/ExportIntergenicRegions -s -m 100 -g
/user/Jib/GffFiles/Pombe/sanger.gff
**************************************************************************************
**************************************************************************************
** Export Intronic Regions June 2007 **
**************************************************************************************
EIR takes a UCSC Gene table and fetches the most conservative/ smallest intronic
regions. Base coordinates are assumed to be stop inclusive, not interbase.
Parameters:
-g Full path file text for the UCSC Gene table.
-m Minimum acceptable intron size, those smaller will be tossed, defaults to 60bp
-s Subtract one from the stop coordinates of your UCSC table to convert from interbase.
Example: java -Xmx1000M -jar pathTo/T2/Apps/ExportIntronicRegions -s -m 100 -g
/user/Jib/ucscPombe.txt
**************************************************************************************
**************************************************************************************
** Export Trimmed Genes May 2012 **
**************************************************************************************
EE takes a UCSC Gene table and clips each gene back to the first intron closed by a
coding sequence exon. Thus these include all of the 5'UTRs. Genes with no introns are
removed.
Parameters:
-g Full path file text for the UCSC Gene table.
-u Print just UTRs, defaults to UTRs plus 1st CDS intron with flanking exon.
-i Print just 1st CDS intron with flanking exons.
Example: java -Xmx1000M -jar pathTo/T2/Apps/ExportTrimmedGenes -u -g
/user/Jib/ucscPombe.txt
**************************************************************************************
**************************************************************************************
** Fastq Barcode Tagger: Jan 2020 **
**************************************************************************************
Takes 2 or 3 fastq files (paired end reads and possibly a third containing unique
molecular barcodes/ indexes), appends the barcode and quality to the fastq header, and
writes out the modified records. For IDT inline 2 fastq UMI data sets, the barcode is
parsed from the beginning of each fastq. Be sure to clip 5Ns from the 3' end when
adapter trimming.
Options:
-f First fastq file, .gz/.zip OK.
-s Second fastq file, .gz/.zip OK.
-b Barcode fastq file, .gz/.zip OK, or set -e
-e Parse barcodes from the first 3bp of each read and combine the two 3mers into a
6mer barcode. 5bp are trimmed from the ends of each read to remove the UMI and
2bp constant seq as well as an potential read through. IDT's current strategy.
-i Write interlaced fastq to stdout for direct piping to other apps
-r Directory to save the modified fastqs, defaults to the parent of -f
-l Max length of barcode, defaults to all. Use to trim 3' end.
-a Append the line number to the read name to uniquify.
Example: java -Xmx1G -jar pathToUSeq/Apps/FastqBarcodeTagger -f lob_1.fastq.gz
-s lob_2.fastq.gz -b lob_barcode.fastq.gz -i | bwa mem -p /ref/hg19.fa
**************************************************************************************
**************************************************************************************
** Fastq Interlacer: April 2016 **
**************************************************************************************
Takes paired fastq files and writes interlaced/ interleaved fastq to stndOut.
Options:
-f First fastq file, .gz/.zip OK.
-s Second fastq file, .gz/.zip OK.
Example: java -Xmx1G -jar pathToUSeq/Apps/FastqInterlacer -f lob_1.fastq.gz
-s lob_2.fastq.gz | cutadapt | bwa | samblaster ....
**************************************************************************************
**************************************************************************************
** Fastq Renamer: April 2018 **
**************************************************************************************
Takes paired fastq files and replaces the header with the record count.
Options:
-f First fastq file, .gz/.zip OK.
-s Second fastq file, .gz/.zip OK.
-d Path to a directory for saving the modified fastq files.
Example: java -Xmx1G -jar pathToUSeq/Apps/FastqRenamer -f lob_1.fastq.gz
-s lob_2.fastq.gz -d UniquifiedFastq/
**************************************************************************************
**************************************************************************************
** FetchGenomicSequences: Feb 2013 **
**************************************************************************************
Given a file containing genomic coordinates, fetches and saves the sequence (column
output: chrom origStart origStop fetchedStart fetchedStop completeFetch seq).
-f Full path to a file or directory containing tab delimited chrom, start,
stop text files. Interbabase coordinates (zero based, stop excluded).
-s Full path directory text containing containing genomic fasta files. The fasta
header defines the name of the sequence, not the file name.
-b Fetch flanking bases, defaults to 0. Will set start to zero or stop to last base if
boundaries are exceeded.
-r Reverse complement fetched sequences, defaults to returning the + genomic strand.
-a Output fasta format.
Example: java -Xmx1000M -jar pathTo/T2/Apps/FetchGenomicSequences -f /data/miRNAs.txt
-s /genomes/human/v35.1/ -b 5000 -r
**************************************************************************************
**************************************************************************************
** Find Neighboring Genes: Nov 2008 **
**************************************************************************************
FNG takes a list of genes in UCSC Gene Table format and intersects them with a list of
regions finding the closest gene to each region as well as all of the genes that fall
within a given neighborhood. Distance is measured from the center of the region to the
transcription start site/ 1st base position in 1st exon. See Tables link under
http://genome.ucsc.edu/ . Note, output coordinates are zero based, stop inclusive.
-g Full path file text for a tab delimited UCSC Gene Table (text chrom strand txStart
txEnd cdsStart cdsEnd exonCount exonStarts exonEnds etc...) .
-p Full path file/directory text for tab delimited region list(s) (chr, start, stop) .
-b Size of neighborhood in bp, default is 10000
-f Find genes that overlap neighborhood irregardles of distance to TSS.
-c Only print closest genes.
-o Print neighbors on one line.
Example: java -jar pathTo/T2/Apps/FindNeighboringGenes -g /anno/hg17Ensembl.txt -p
/affy/p53/finalPicks.txt -b 5000 -c
**************************************************************************************
**************************************************************************************
** Find Overlapping Genes: Oct 2010 **
**************************************************************************************
Finds overlapping genes that converge, diverge, or contain one another given a UCSC
gene table.
Options:
-u UCSC RefFlat or RefSeq gene table file, full path. See,
http://genome.ucsc.edu/cgi-bin/hgTables, (name1 name2(optional) chrom strand
txStart txEnd cdsStart cdsEnd exonCount exonStarts exonEnds). NOTE:
this table should contain only one composite transcript per gene (e.g. Use
Ensembl genes NOT transcripts. See MergeUCSCGeneTable app.).
Example: java -Xmx4G -jar pathTo/USeq/Apps/FindOverlappingGenes -u
/data/zv8EnsemblGenes.ucsc.gz
**************************************************************************************
**************************************************************************************
** Find Shared Regions: Dec 2018 **
**************************************************************************************
Writes out a bed file of shared regions. Interbase coordinates.
Options:
-f First bed file (tab delimited: chr start stop ...).
-s Second bed file.
-r Results file.
-m Minimum length, defaults to 0.
Example: java -Xmx4G -jar pathTo/USeq/Apps/FindSharedRegions -f
/Res/firstBedFile.bed -s /Res/secondBedFile.bed -r /Res/common.bed -m 100
************************************************************************************
**************************************************************************************
** File Cross Filter: Sept 2017 **
**************************************************************************************
FCF takes one or more columns in the matcher file and uses these as a key to parse and
save matching keys in the to parse files. Use this to parse lines in files that match
those in another. Keys must be unique. The order and number of the rows in the matcher
file is preserved, if a match is not found in the parsed file, a blank line is inserted
instead.
-m Path to a tab delimited txt (.gz/.zip OK) file to use in matching.
-a One or more column indexs in the matcher file to use as the key.
-p Path to a file or directory of files to parse (.gz/.zip OK).
-b One or more column indexes in the parse file(s) to use as a key.
Example: java -jar pathTo/USeq/Apps/FileCrossFilter -m intRegions.bed -a 0,1,2
-p SpreadSheetData/ -b 0,1,2
**************************************************************************************
**************************************************************************************
** File Match Joiner: July 2008 **
**************************************************************************************
FMJ loads a file and a particular column containing unique entries, a key, and then
appends the key line to lines in the parsed file that match a particular column.
Usefull for appending say chromosome coordinates to snp ids data, etc.
-k Full path file text for a tab delimited txt file (key) containing unique entries.
-f Ditto but for the file to parse, can specify a directory too.
-i Collapse duplicate keys.
-j Skip duplicate keys.
-a Column index containing the unique IDs in key, defaults to 0.
-b Column index containing the unique IDs in parsers, defaults to 0.
-p Print only matches.
Example: java -jar pathTo/Apps/FileMatchJoiner -k /snpChromMap.txt -m /SNPData/
--b 2 -p
**************************************************************************************
**************************************************************************************
** File Joiner: Feb 2016 **
**************************************************************************************
Joins text files into a single file, avoiding line concatenations. This is a problem
with using 'cat * >> combine.txt'. Removes empty lines. Option to follow custom order.
Parameters:
-f Full path text for the directory containing the text files.
-o (Optional) Order the files using this comma delimited list, no spaces. Not all
need to exist.
-c (Optional) Concatinated results file.
Example: java -jar pathTo/T2/Apps/FileJoiner -f /affy/SplitFiles/
-o 1.fasta,2.fasta,3.fasta,4.fasta
**************************************************************************************
**************************************************************************************
** File Splitter: April 2020 **
**************************************************************************************
Splits a big text file into smaller files given a maximum number of lines.
Required Parameters:
-f Full path file text or directory for the text file(s) (.zip/.gz OK).
-n Maximum number of lines to place in each.
-g GZip split files.
Example: java -Xmx256M -jar pathTo/T2/FileSplitter -f /affy/bpmap.txt -n 50000
**************************************************************************************
**************************************************************************************
** Filter Intersecting Regions: Dec 2018 **
**************************************************************************************
Flattens the mask regions and uses it to split the split file(s) into intersecting
and non intersecting regions based on the minimum fraction intersection. For UCSC gene
tables, exons are compared and if any in the gene intersect, the whole gene is moved
accordingly.
Options:
-m Full path file text for the masking bed file (tab delim: chr start stop ...).
-s Full path file or directory containing bed, gtf/gff, or ucsc gene table files to
split.
-t Type of files to split, indicate: bed, gff, or ucsc
-i Minimum fraction of each split region required to score as an intersection with
the flattened mask, defaults to 1x10-1074
-b Expand start and stop of regions to mask by xxx bps, defaults to 0
Example: java -Xmx4000M -jar pathTo/Apps/FilterIntersectingRegions -i 0.5
-m /ArrayDesigns/repMskedDesign.bed -b /ArrayDesigns/ -t bed
************************************************************************************
**************************************************************************************
** Filter Point Data: May 2016 **
**************************************************************************************
FPD drops or saves observations from PointData that intersect a list of regions
(e.g. repeats, interrogated regions).
Options:
-p Point Data directories, full path, comma delimited. These should contain
chromosome specific xxx.bar.zip files.
-r Full path file text for a tab delimited text file containing regions to use in
filtering the intersecting data (chr start stop ..., interbase coordinates).
-i Select data that intersects the list of regions, defaults to selecting data that
doesn't intersect.
-a Acceptible intersection, fraction, defaults to 0.5
-n Just calculate the number of observations after filtering, don't save any data.
-f Save directory, defaults to derivative of parent.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/FilterPointData -p /data/PointData
-r /repeats/hg18RepeatMasker.bed -a 0.75
**************************************************************************************
**************************************************************************************
** Foundation Vcf Comparator: Nov 2019 **
**************************************************************************************
FVC compares a Foundation vcf generated with the FoundationXml2Vcf to a recalled vcf.
Exact recall vars are so noted and removed. Foundation vcf with no exact but one
overlapping record can be merged with -k. Be sure to vt normalize each before running.
Recall variants failing FILTER are not saved.
Options:
-f Path to a FoundationOne vcf file, see the FoundationXml2Vcf app.
-r Path to a recalled snv/indel vcf file.
-m Path to named vcf file for saving the results.
-c Append chr if absent in chromosome name.
-e Exclude Foundation ##contig header lines.
-v Insert mock sample info for VarSeq filtering.
-k Attempt to merge Foundation records that overlap a recall and are the same type.
Defaults to printing both.
 This is an inprecise process with some
insertions, multi snv, and multi vars. Output vcf variants have not been normalized.
Recommend running vt normalize, see https://genome.sph.umich.edu/wiki/Vt
Options:
-x Path to a FoundationOne xml report or directory containing such.
-s Path to a directory for saving the results.
-f Path to the reference fasta with xxx.fai index used by Foundation for coordinates.
-o Skip variants that clearly fail to convert, e.g. var seq doesn't match fasta.
Defaults to marking 'ci' in FILTER field.
-i Include PHI in spreadsheet output.
-a Print this list of attributes in spreadsheet, comma delimited, case sensitive, no
spaces. Defaults to all.
Example: java -Xmx2G -jar pathToUSeq/Apps/FoundationXml2Vcf -x FXmlReports/
-f /Ref/b37.fasta -s FVcfs/ -a ReportId,Gender,SubmittedDiagnosis,MSI,TMB
**************************************************************************************
**************************************************************************************
** Freebayes VCF Parser: Mar 2017 **
**************************************************************************************
Parses Freebayes VCF files, filtering for read depth, allele frequency diff ratio, etc.
Inserts AF and DP into for the tumor sample into the INFO field. Changes the sample
order to Normal and Tumor and updates the #CHROM line. Put the tumor bam first when
calling freebayes.
Required Options:
-v Full path file or directory containing xxx.vcf(.gz/.zip OK) file(s).
-t Minimum tumor allele frequency (AF), defaults to 0.
-n Maximum normal AF, defaults to 1.
-u Minimum tumor alignment depth, defaults to 0.
-o Minimum normal alignment depth, defaults to 0.
-d Minimum T-N AF difference, defaults to 0.
-r Minimum T/N AF ratio, defaults to 0.
-p Remove non PASS filter field records.
Example: java -jar pathToUSeq/Apps/FreebayesVCFParser -v /VCFFiles/ -t 0.05 -n 0.5
-u 100 -o 20 -d 0.05 -r 2
**************************************************************************************
**************************************************************************************
** Gatk Called Segment Annotator: April 2023 **
**************************************************************************************
Annotates GATKs CallCopyRatioSegments output with denoised copy ratio and heterozygous
allele frequency data from the tumor and matched normal samples. Enables filtering
using these values to remove copy ratio calls with high normal background. Adds
intersecting gene names.
Options:
-r Results directory to save the passing and failing segments.
-s Called segment file from GATKs CallCopyRatioSegments app, e.g. xxx.called.seg
-t Tumor denoised copy ratio file, from GATKs DenoiseReadCounts app. Bgzip compress
and tabix index it with https://github.com/samtools/htslib :
grep -vE '(@|CONTIG)' tumor.cr.tsv > tumor.cr.txt
~/HTSLib/bgzip tumor.cr.txt
~/HTSLib/tabix -s 1 -b 2 -e 3 tumor.cr.txt.gz
-n (Optional) Normal denoised copy ratio file, ditto.
-u Tumor allele frequency file, from GATKs ModelSegments app. Bgzip compress
and tabix index it with https://github.com/samtools/htslib :
grep -vE '(@|CONTIG)' gbm7.hets.tsv > gbm7.hets.txt
~/HTSLib/bgzip gbm7.hets.txt
~/HTSLib/tabix -s 1 -b 2 -e 2 gbm7.hets.txt.gz
-o (Optional) Normal allele frequency file, ditto.
-g RefFlat UCSC gene file, run USeq's MergeUCSCGeneTable to collapse transcripts.
-c Minimum absolute tumor log2 copy ratio, defaults to 0.15
-x Maximum absolute normal log2 copy ratio, defaults to 0.5
-m Minimum absolute log2 TN ratio of copy ratios, defaults to 0.15
-a Maximum bp gap for intersecting a segment with a gene, defaults to 1000
Example: java -Xmx4G -jar pathTo/USeq/Apps/GatkCalledSegmentAnnotator -r AnnoResults/
-s gbm7.called.seg -t tumor.cr.txt.gz -n normal.cr.txt.gz -u gbm7.hets.txt.gz
-o gbm7.hets.normal.txt.gz -g ~/UCSC/hg38RefSeq_Merged.refFlat.gz -a 100
**************************************************************************************
**************************************************************************************
** Gatk Runner: Jan 2022 **
**************************************************************************************
Takes a bed file of target regions, splits it by the number of threads, writes out
each, executes the GATK commands, and merges the results. Set the -Xmx to the
maximum available on the machine to enable correct thread usage.
Options:
-r A regions bed file (chr, start, stop,...) to intersect, see
http://genome.ucsc.edu/FAQ/FAQformat#format1 , gz/zip OK.
-s Path to a directory for saving the results.
-p Minimum number processors per job, defaults to 4
-g Minimum GB RAM per job, defaults to 5
-c Command to execute, see the example below, modify to match your enviroment.
Most resources require full paths. Don't set -o, -O, -l, or -L
-l Use lowercased -l for Lofreq compatability, defaults to -L
-u Use uppercase -O for GATK 4 compatability, defaults to -o
-b Add a -bamout argument and merge bam chunks.
Example: java -Xmx128G -jar pathToUSeq/Apps/GatkRunner -b -r /SS/targets.bed -s
/SS/HC/ -c 'java -Xmx4G -jar /SS/GenomeAnalysisTK.jar -T MuTect2
-R /SS/human_g1k_v37.fasta --dbsnp /SS/dbsnp_138.b37.vcf
--cosmic /SS/v76_GRCh37_CosmicCodingMuts.vcf.gz -I:tumor /SS/sar.bam -I:normal
/SS/normal.bam' -u
**************************************************************************************
**************************************************************************************
** Gatk Joint Genotyper: July 2023 **
**************************************************************************************
The GJG takes a bed file of target regions, splits it into 250 jobs, executes the GATK
GenomicsDBImporter and GenotypeGVCFs on each, and merges the results. Set the -Xmx for
this app to the maximum available on the machine to enable correct thread usage. This
app also works for CreateSomaticPanelOfNormals, just provide -p.
Options:
-b Bed file of sorted regions (chr, start, stop,... gz/zip OK), to call variants. No
overlaps. Run the USeq MergeRegions if unsure.
-g Directory containing GATK xxx.g.vcf.gz files to call, bgzippped, tabix indexed.
-v Path to write the final results joint genotyped vcf file, must end with vcf.gz
-t Tmp directory.
-e Path to the gatk executable.
-f Path to the indexed fasta reference.
-r GB RAM per worker thread, defaults to 10, increase if memory errors occur.
-c Number of chunks to split bed file, defaults to 250.
-d Don't delete the tmp directory.
-p (Only For Som PoN, beta) Path to a germline resource like af-only-gnomad.hg38.vcf.gz
Example: java -Xmx128G -jar pathToUSeq/Apps/GatkJointGenotyper
-b hg38NimIdtMergedPad150bp.bed.gz -g ToGenotype/ -v jg.vcf.gz -r 20
-t /scratch/local/u0028003/4239066/ -e ~/BioApps/GATK/gatk-<IP_ADDRESS>/gatk
-f ~/TNRunner/GATKResourceBundleAug2021/Homo_sapiens_assembly38.fasta
**************************************************************************************
**************************************************************************************
** GeneiASE Parser: Sept 2016 **
**************************************************************************************
Combines the GeneiASE results file with the input data file.
Required Options:
-r GeneiASE results output file
-d GeneiASE input data file
-o Output file for the summary spreadsheet
Example: java -Xmx4G -jar pathTo/USeq/Apps/AllelicExpressionDetector -b Bam/RPENormal/
-n D002-14,D005-14,D006-14,D009-14 -d GenotypingResults.txt.gz -s SNPMap_Ref2Alt_Int.txt
-r RPENormal -t ~/Anno/b37EnsGenes7Sept2016_Exons.bed.gz
**************************************************************************************
**************************************************************************************
** Graph 2 Bed: Feb 2011 **
**************************************************************************************
Converts USeq stair step and heat map graphs into region bed files using a threshold.
Do not use this with non USeq generated graphs. Won't work with bar or point graphs.
Options:
-p Point Data directories, full path, comma delimited. Should contain chromosome
specific xxx.bar.zip or xxx_-_.bar files. May point this to a single directory
of such too.
-t Threshold, regions exceeding it will be saved, defaults to 0.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/Graph2Bed -t 9 -p /data/ReadCoverage
**************************************************************************************
**************************************************************************************
** Gr2Bar: Nov 2006 **
**************************************************************************************
Converts xxx.gr.zip files to chromosome specific bar files.
-f The full path directory/file text for your xxx.gr.zip file(s).
-v Genome version (ie H_sapiens_Mar_2006), get from UCSC Browser,
http://genome.ucsc.edu/FAQ/FAQreleases
-o Orientation of GR file. If not specified, orientation is left as '.'
Example: java -Xmx1500M -jar pathTo/T2/Apps/Gr2Bar -f /affy/GrFiles/ -v hg17
**************************************************************************************
**************************************************************************************
** Intersect Lists: Dec 2008 **
**************************************************************************************
IL intersects two lists (of genes) and using randomization, calculates the
significance of the intersection and the fold enrichment over random. Note, duplicate
items are filtered from each list prior to analysis.
-a Full path file text for list A (or directory containing), one item per line.
-b Full path file text for list B (or directory containing), one item per line.
-t The total number of unique items from which A and B were drawn.
-n Number of permutations, defaults to 1000.
-p Print the intersection sets (common, unique to A, unique to B) to screen.
Example: java -Xmx1500M -jar pathTo/Apps/IntersectLists -a /Data/geneListA.txt -b
/Data/geneListB.txt -t 28356 -n 10000
**************************************************************************************
**************************************************************************************
** Intersect Key With Regions: July 2012 **
**************************************************************************************
IR intersects lists of genomicRegions (chrom start stop(inclusive)) with a key, assumes the
lists are sorted from most confident to least confident. Multiple hits to the same key
region are ignored.
-k Full path file text for the key genomicRegions file, tab delimited (chr start
stop(inclusive)).
-r Full path file text or directory containing your region files to score.
-g Max gap, defaults to -1. A max gap of 0 = genomicRegions must abut, negative values force
overlap (ie -1= 1bp overlap, be careful not to exceed the length of the smaller
region), positive values enable gaps (ie 1=1bp gap).
-s Subtract 1 from end coordinates. Use for interbase.
Example: java -Xmx1500M -jar pathTo/Apps/IntersectKeyWithRegions -k /data/key.txt
-r /data/HitLists/
**************************************************************************************
**************************************************************************************
** Intersect Regions: May 2017 **
**************************************************************************************
IR intersects lists of regions (tab delimited: chrom start stop(inclusive)). Random
regions can also be used to calculate a p-value and fold enrichment.
-f First regions files, a single file, or a directory of files.
-s Second regions files, a single file, or a directory of files.
-g Max gap, defaults to 0. A max gap of 0 = regions must at least abut or overlap,
negative values force overlap (ie -1= 1bp overlap, be careful not to exceed the
length of the smaller region), positive values enable gaps (ie 1=1bp gap).
-e Score intersections where second regions are entirely contained by first regions.
-r Make random regions matched to the second regions file(s) and intersect with the
first. Enter either a bed file or full path directory that contains chromosome
specific interrogated regions files (ie named: chr1, chr2 ...: chrom start stop).
-c Match GC content of second regions file(s) when selecting random regions, rather
slow. Provide a full path directory text containing chromosome specific genomic
sequences.
-n Number of random region trials, defaults to 1000.
-w Write intersections and differences.
-x Write paired intersections.
-p Print length distribution histogram for gaps between first and closest second.
-q Parameters for histogram, comma delimited list, no spaces:
minimum length, maximum length, number of bins. Defaults to -100, 2400, 100.
Example: java -Xmx1500M -jar pathTo/Apps/IntersectRegions -f /data/miRNAs.txt
-s /data/DroshaLists/ -g 500 -n 10000 -r /data/InterrogatedRegions/
**************************************************************************************
**************************************************************************************
** Job Cleaner : May 2022 **
**************************************************************************************
Zip archives particular folders, deletes particular files. Use to clean up analysis
result directories prior to cloud upload.
Options:
-r Root directory to recursively look for files and folders.
-e File extensions and file names to delete, comma delimited, no spaces.
-n Directory names to zip archive and then delete, comma delimited, no spaces.
-d Dry run, just list the files and directories.
Example: java -jar pathToUSeq/Apps/JobCleaner -d -n 'Logs,RunScripts' -r CJobs/ -e
'.tbi,.crai,.bai,COMPLETE'
**************************************************************************************
**************************************************************************************
** Joint Genotype VCF Parser: September 2021 **
**************************************************************************************
Splits and filters GATK and Strelka joint genotyped multi sample vcf files. Use vt to
decompose and normalize multi alts. See https://genome.sph.umich.edu/wiki/Vt. Replaces
the AF and DP INFO fields with the sample level values. Warning, some multi alts AF
cannot be resolved following Vt decomposing and are assigned a value of 0.
Required Params:
-v Path to vt decomposed GATK joint genotyped multi sample vcf file, gz/zip OK.
~/BioApps/vt decompose -s jointGenotyped.vcf.gz -o jointGenotyped.decomp.vcf.gz
-s Path to a directory to save the split files.
Optional Params:
-q Minimum QUAL value, defaults to 20
-d Minimum read depth based on the AD sample values, defaults to 10
-a Minimum AF allele freq, defaults to 0.025
-c Minimum alt read depth, defaults to 2
-g Minimum GQ genotype quality, defaults to 20
-x Use Strelka's recalibrated GQX instead of GQ scores for genotype quality filtering.
-k Comma delimited list of FILTER keys to use in failing records, no spaces, case
sensitive.
-f Print debugging output to screen
-i Simplify INFO to just AF, DP, and OLD_xxx info
-f Just filter the multi sample vcf, don't split or modify it.
Example: java -jar -Xmx2G pathToUSeq/Apps/HaplotypeVCFParser -d 20 -a 0.05 -g 30
-v jointGenotyped.decomp.vcf.gz -s SplitFilteredVcfs/ -q 30 -c 3 -i
**************************************************************************************
**************************************************************************************
** Known Splice Junction Scanner : Sept 2017 **
**************************************************************************************
Scores know splice junctions using the MaxEntScan algorithms. See Yeo and
Burge 2004, http://www.ncbi.nlm.nih.gov/pubmed/15285897 for details.
Required Options:
-r Name of a gzipped bed file to use in saving the results, will over write.
-f Path to the reference fasta with associated xxx.fai index
-u UCSC RefFlat or RefSeq transcript (not merged genes) file, full path. See RefSeq
http://genome.ucsc.edu/cgi-bin/hgTables, (uniqueName1 name2(optional) chrom
strand txStart txEnd cdsStart cdsEnd exonCount (commaDelimited)exonStarts
(commaDelimited)exonEnds). Example: ENSG00000183888 C1orf64 chr1 + 16203317
16207889 16203385 16205428 2 16203317,16205000 16203467,16207889 .
-m Full path directory name containing the me2x3acc1-9, splice5sequences and me2x5
splice model files. See USeqDocumentation/splicemodels/ or
http://genes.mit.edu/burgelab/maxent/download/
Example: java -Xmx10G -jar ~/USeq/Apps/KnownSpliceJunctionScanner -f ~/Hg19/hg19.fasta
-r ~/exm2.bed.gz -m ~/USeq/Documentation/splicemodels -u ~/hg19EnsTrans.ucsc.zip
**************************************************************************************
**************************************************************************************
** LiquidBiopsyCopyAnalysis: Nov 2022 **
**************************************************************************************
Required Options:
-s Path to a directory containing sample specific sub directories with two aligment files representing the cfDNA and matched germline cram or bam files with their indexes. The germline file name should contain the 'germ' string.
-b Path to a bed file of non overlapping regions to estimate copy ratio differences, xxx.bed.gz/.zip OK. The name column will be used to group each region into a larger area for merged copy ratio differences.
-r Path to the reference fasta with xxx.fai index used for alignment.
-o Path to the output results directory.
Default Options:
-m Minimum alignment mapping quality, defaults to 13
-p Number processors to use, defaults to all, reduce if out of memory errors occur.
Example: java -Xmx100G -jar pathTo/USeq/Apps/BamPileup
**************************************************************************************
**************************************************************************************
** Lofreq VCF Parser: May 2018 **
**************************************************************************************
Parses Lofreq vcf files with options for filtering for minimum QUAL, modifying the
FILTER field, removing non SNVs, and appending FORMAT info for downstream merging.
Required Params:
-v Full path file or directory containing xxx.vcf(.gz/.zip OK) file(s)
Optional Params:
-s File path to a directory for saving the modified vcfs
-m Minimum QUAL score, defaults to 0
-d Minimum DP read depth, defaults to 0
-t Minimum AF allele freq, defaults to 0
-r Minimum Alt count, defaults to 0
-i Remove non SNV records
-f Replace the FILTER field with '.'
-a Append FORMAT NORMAL TUMOR to #CHROM line and add empty columns to records
-n Mark variants failing thresholds FAIL instead of not printing
Example: java -jar pathToUSeq/Apps/LofreqVCFParser -v VCFFiles/ -m 32 -i -f -a
-s FilteredLofreqVcfs/ -r 3
**************************************************************************************
**************************************************************************************
** LoH : Nov 2022 **
**************************************************************************************
LoH compares heterozygous snv and indel allele counts between germline and somatic
sequencing datasets to identify potential loss of heterozygosity events. LoH is
defined here as a significant increase in the somatic allele fraction (AF) relative to
the matched germline. Adjacent variants passing p-value and AF difference thresholds
are merged and a combine p-value calculated for the window block.
Required:
-v Path to a germline, single sample, variant xxx.vcf(.gz/.zip OK) file. These should
be filtered, high confidence, vt normalized and decomposed short variants.
-g Path to the germline bampileup file, use the USeq BamPileup tool to generate the
xxx.bp.txt.gz and tabix index files.
-s Path to the somatic bampileup file, ditto.
-o Path to directory to write the bed and vcf output result files.
Optional:
-c Bed file containing passing regions from the TNRunner2 CopyRatio workflow.
-q Minimum vcf record QUAL, defaults to 20
-e Minimum vcf record sample GQ, defaults to 20
-x Use Strelka's recalibrated GQX genotype score, defaults to GQ
-r Minimum unique observation variant read depth for both germline and tumor samples,
defaults to 20
-l Minimum Fisher exact p-value for including a variant in a block, defaults to 0.1
-f Minimum difference in AF between the somatic and germline samples for including
a variant into a block, defaults to 0.1
-m Maximum bp gap between variants for grouping into a block, defaults to 1000
-p Minimum Benjamini-Hochberg adjusted window -10Log10(combine p-value), defaults to
8.239 (0.15)
-d Minimum window mean difference in AFs, defaults to 0.1
-w Window BP padding for reporting significant LoH regions, defaults to 100
Example: java -Xmx4G -jar pathTo/USeq/Apps/LoH -v gatkStrelkaGermline.vcf.gz
-g normal.bp.txt.gz -s tumor.bp.txt.gz -o LoHResults/ -d 0.15 -m 5000 -b
GATKCopyRatio_Hg38.called.seg.pass.bed.gz
**************************************************************************************
**************************************************************************************
** Maf Parser: Sept 2016 **
**************************************************************************************
Parses and manipulates variant maf files. Provide a path to the tabix executables
(https://github.com/samtools/htslib) for TQuery lookup and IGV compatibility.
Options:
-m Path to a xxx.maf file (xxx.maf.txt and .zip/.gz OK) or directory containing such.
-o Output directory, will overwrite.
-t To tabix index the output, provide a path to the dir containing bgzip and tabix
-c Convert M chroms to MT
Example: java -Xmx4G -jar pathTo/USeq/Apps/MafParser -m MafTCGAFiles/ -o Sorted/
-t ~/BioApps/HTSlib/1.3/bin/ -c
**************************************************************************************
**************************************************************************************
** Make Splice Junction Fasta: Nov 2010 **
**************************************************************************************
DEPRECIATED, don't use! See MakeTranscriptome app!
MSJF creates a multi fasta file containing sequences representing all possible linear
splice junctions. The header on each fasta is the chr_endPosExonA_startPosExonB. The
length of sequence collected from each junction is 2x the radius. A word of warning,
be very careful about the coordinate system used in the gene table to define the
start and stop of exons. UCSC uses interbase and this is assumed in this app. Check
a few of the junctions to be sure correct splices were made. All junction sequences
are from the top/ plus strand of the genome, they are not reverse complemented. Exon
sequence shorter than the radius will be appended with Ns.
Options:
-f Fasta file directory, should contain chromosome specific xxx.fasta files.
-u UCSC gene table file, full path. See, http://genome.ucsc.edu/cgi-bin/hgTables
-s Sequence length radius.
-r Results fasta file, full path.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/MakeSpliceJunctionFasta -s 32
-f /Genomes/Hg18/Fastas/ -u /Anno/Hg18/ucscKnownGenes.txt -r
/Genomes/Hg18/Fastas/hg18_32_splices.fasta
************************************************************************************
**************************************************************************************
** Make Transcriptome: June 2012 **
**************************************************************************************
Takes a UCSC ref flat table of transcripts and generates two multi fasta files of
transcripts and splices (known and theoretical). All possible unique splice junctions
are created given the exons from each gene's transcripts. In some cases this is
computationally intractable and theoretical splices from these are not complete.
Read through occurs with small exons to the next up or downstream so keep the sequence
length radius to a minimum to reduce the number of junctions. Overlapping exons are
assumed to be mutually exclusive. All sequence is from the plus genomic stand, no
reverse complementation. Interbase coordinates. This app can take a very long time to
run. Break up gene table by chromosome and run on a cluster.
To incorporate additional splice-junctions, add a new annotation line containing two
exons representing the junction to the table. If needed, set the -s option to skip
duplicates.
Options:
-f Fasta file directory, one per chromosome (e.g. chrX.fasta or chrX.fa, .gz/.zip OK)
-u UCSC RefFlat gene table file, full path. See,
http://genome.ucsc.edu/cgi-bin/hgTables, (geneName transcriptName chrom strand
txStart txEnd cdsStart cdsEnd exonCount (commaDelimited)exonStarts
(commaDelimited)exonEnds). Example: ENSG00000183888 ENST00000329454 chr1 +
16203317 16207889 16203385 16205428 2 16203317,16205000 16203467,16207889 .
-r Sequence length radius. Set to the read length - 4bp.
-n Max number splices per transcript, defaults to 100000.
-m Max minutes to process each gene's splices before interrupting, defaults to 10.
-s Skip subsequent occurrences of splices with the same coordinates. Memory intensive.
Example: java -Xmx4G -jar pathTo/USeq/Apps/MakeTranscriptome -f /Genomes/Hg18/Fastas/
-u /Anno/Hg18/ensemblGenes.txt.ucsc -r 46 -s
************************************************************************************
**************************************************************************************
** Mask Exons In Fasta Files: June 2011 **
**************************************************************************************
Replaces the exonic sequence with Ns.
Options:
-f Fasta file directory, one per chromosome (e.g. chrX.fasta or chrX.fa, .gz/.zip OK)
-u UCSC RefFlat gene table file, full path. See,
http://genome.ucsc.edu/cgi-bin/hgTables, (geneName transcriptName chrom strand
txStart txEnd cdsStart cdsEnd exonCount (commaDelimited)exonStarts
(commaDelimited)exonEnds). Example: ENSG00000183888 ENST00000329454 chr1 +
16203317 16207889 16203385 16205428 2 16203317,16205000 16203467,16207889 .
-s Save directory, full path.
Example: java -Xmx4G -jar pathTo/USeq/Apps/MaskExonsInFastaFiles -f
/Genomes/Hg18/Fastas/ -u /Anno/Hg18/ensemblTranscripts.txt.ucsc -s
/Genomes/Hg18/MaskedFastas/
************************************************************************************
**************************************************************************************
** Mask Regions In Fasta Files: Aug 2016 **
**************************************************************************************
Replaces the region (or non region) sequence with Ns. Interbase coordinates.
Options:
-f Fasta file directory, one per chromosome (e.g. chrX.fasta or chrX.fa, .gz/.zip OK)
-b Bed file of regions to mask.
-s Save directory, full path.
-r Mask sequence not in regions, reverse mask.
Example: java -Xmx4G -jar pathTo/USeq/Apps/MaskRegionsInFastaFiles -f
/Genomes/Hg18/Fastas/ -b /Anno/Hg18/badRegions.bed -s
/Genomes/Hg18/MaskedFastas/
************************************************************************************
**************************************************************************************
** MatchMates: February 2019 **
**************************************************************************************
This app attaches mates of aligned first of pair reads to the attributes and modifies
the start position to enable sorting by unclipped start. Call Consensus to cluster and
collapse alignments with related molecular barcodes.
Options:
-s (Required) Provide a directory path for saving the modified alignments.
-b Path to a query name sorted bam/sam alignment file, defaults to reading from STDIN.
-j Write summary stats in json format to this file.
Example: myAligner | java -Xmx2G -jar pathTo/USeq/Apps/MatchMates -s ReadyForConsensus
**************************************************************************************
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** MaxEntScanScore3: Nov 2013 **
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Implementation of Max Ent Scan's score3 algorithm for human splice site detection. See
Yeo and Burge 2004, http://www.ncbi.nlm.nih.gov/pubmed/15285897
Options:
-s Full path directory name containing the me2x3acc1-9 splice model files. See
USeq/Documentation/ or http://genes.mit.edu/burgelab/maxent/download/
-t Full path file name for 23mer test sequences, GATCgatc only, one per line. Fasta OK.
Example: java -Xmx10G -jar pathTo/USeq/Apps/MaxEntScanScore3 -s ~/MES/splicemodels -t
~/MES/seqsToTest.fasta
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** MaxEntScanScore5: Nov 2013 **
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Implementation of Max Ent Scan's score5 algorithm for human splice site detection. See