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Function: Create plots to visualize base recalibration results
Usage: java -jar GenomeAnalysisTK.jar -T AnalyzeCovariates -R myrefernce.fasta -BQSR myrecal.table -plots BQSR.pdf
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Function: Write out sequence read data (for filtering, merging, subsetting etc)
Usage: java -jar GenomeAnalysisTK.jar -T PrintReads -R reference.fasta -I input1.bam -I input2.bam -o output.bam --read_filter MappingQualityZero // Prints the first 2000 reads in the BAM file java -jar GenomeAnalysisTK.jar -T PrintReads -R reference.fasta -I input.bam -o output.bam -n 2000 // Downsamples BAM file to 25% java -jar GenomeAnalysisTK.jar -T PrintReads -R reference.fasta -I input.bam -o output.bam -dfrac 0.25
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Function: Left-align indels in a variant callset
Usage: java -jar GenomeAnalysisTK.jar -T LeftAlignAndTrimVariants -R reference.fasta --variant input.vcf -o output.vcf --dontTrimAlleles
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Function: Count the number of bases in a set of reads
Usage: java -jar GenomeAnalysisTK.jar -R reference.fasta -T CountBases -I input.bam [-L input.intervals]
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Function: Compute the read error rate per position
Usage: java -jar GenomeAnalysisTK.jar -T ErrorRatePerCycle -R reference.fasta -I my_sequence_reads.bam -o error_rates.gatkreport.txt
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Function: Select a subset of variants from a larger callset
Usage: java -jar GenomeAnalysisTK.jar -R ref.fasta -T SelectVariants --variant input.vcf --maxFilteredGenotypes 5 --minFilteredGenotypes 2 --maxFractionFilteredGenotypes 0.60 --minFractionFilteredGenotypes 0.10
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Function: Randomly select variant records according to specified options
Usage: java -jar GenomeAnalysisTK.jar -T ValidationSiteSelectorWalker -R reference.fasta -V:foo input1.vcf -V:bar input2.vcf --numValidationSites 200 -sf samples.txt -o output.vcf -sampleMode POLY_BASED_ON_GT -freqMode UNIFORM -selectType INDEL
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Function: Left-align indels in a variant callset
Usage: java -jar GenomeAnalysisTK.jar -T LeftAlignAndTrimVariants -R reference.fasta --variant input.vcf -o output.vcf --splitMultiallelics --dontTrimAlleles --keepOriginalAC
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Function: Find variants meeting an autosomal dominant model.
Usage: gemini autosomal_dominant test.auto_dom.db --columns "chrom,start,end,gene"
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Function: Annotate variant calls with context information
Usage: java -jar GenomeAnalysisTK.jar -R reference.fasta -T VariantAnnotator -V input.vcf -o output.vcf --resource:foo resource.vcf --expression foo.AF --expression foo.FILTER
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Function: Call variants and identifies their somatic status (Germline/LOH/Somatic) using pileup files from a matched tumor-normal pair.
Usage: java -jar VarScan.jar copynumber [normal_pileup] [tumor_pileup] [output] OPTIONS
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Function: Calculates the GC content of the reference sequence for each interval
Usage: java -jar GenomeAnalysisTK.jar -T GCContentByInterval -R reference.fasta -o output.txt -L input.intervals
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Function: Validate a VCF file with an extra strict set of criteria
Usage: java -jar GenomeAnalysisTK.jar -T ValidateVariants -R reference.fasta -V input.vcf --dbsnp dbsnp.vcf
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Function: for comparison purposes, it's very useful to normalize the vcf output, especially for more complex graphs which can make large variant blocks that contain a lot of reference bases (Note: requires [vt](http://genome.sph.umich.edu/wiki/Vt)):
Usage: vt decompose_blocksub -a calls.vcf | vt normalize -r FASTA_FILE - > calls.clean.vcf
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Function: This module is used to check the nucleotide composition bias. Due to random priming, certain
patterns are over represented at the beginning (5’end) of reads. This bias could be easily
examined by NVC (Nucleotide versus cycle) plot. NVC plot is generated by overlaying all
reads together, then calculating nucleotide composition for each position of read
(or each sequencing cycle). In ideal condition (genome is random and RNA-seq reads is
randomly sampled from genome), we expect A%=C%=G%=T%=25% at each position of reads.
Usage: read_NVC.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o output