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java -jar picard.jar java -jar picard.jar ValidateSamFile I=input.bam MODE=SUMMARY Validates a SAM or BAM file. Show
java -jar picard.jar java -jar picard.jar FilterSamReads I=input.bam O=output.bam READ_LIST_FILE=read_names.txt FILTER=filter_value Subset read data from a SAM or BAM fileThis tool takes a SAM or BAM file and subsets it to a new file that either excludes or only includes either aligned or unaligned reads (set using FILTER), or specific reads based on a list of reads names supplied in the READ_LIST_FILE. Show
java -jar picard.jar java -jar picard.jar UmiAwareMarkDuplicatesWithMateCigar Identifies duplicate reads using information from read positions and UMIs. Show
java -jar picard.jar java -jar picard.jar EstimateLibraryComplexityI=input.bamO=est_lib_complex_metrics.txt Estimates the numbers of unique molecules in a sequencing library. Show
java -jar picard.jar java -jar picard.jar FastqToSam F1=file_1.fastq O=fastq_to_bam.bam SM=for_tool_testing Converts a FASTQ file to an unaligned BAM or SAM file. This tool extracts read sequences and base qualities from the input FASTQ file and writes them out to a new file in unaligned BAM (uBAM) format. Read group information can be provided on the command line. Three versions of FASTQ quality scales are supported: FastqSanger, FastqSolexa and FastqIllumina (see http://maq.sourceforge.net/fastq.shtml for details). Input FASTQ files can be in GZip format (with .gz extension). Show
java -jar picard.jar java -jar picard.jar CollectMultipleMetrics I=input.bam O=multiple_metrics R=reference_sequence.fasta Collect multiple classes of metrics. This 'meta-metrics' tool runs one or more of the metrics collection modules at the same time to cut down on the time spent reading in data from input files. Available modules include CollectAlignmentSummaryMetrics, CollectInsertSizeMetrics, QualityScoreDistribution, MeanQualityByCycle, CollectBaseDistributionByCycle, CollectGcBiasMetrics, RnaSeqMetrics, CollectSequencingArtifactMetrics, and CollectQualityYieldMetrics. The tool produces outputs of '.pdf' and '.txt' files for each module, except for the CollectAlignmentSummaryMetrics module, which outputs only a '.txt' file. Output files are named by specifying a base name (without any file extensions). Show
bam2wig.py $ python2.7 bam2wig.py -s hg19.chrom.sizes -i sample.bam -o out -u Convert BAM file into wig/bigWig format. Show
java -jar picard.jar java -jar picard.jar FifoBuffer Provides a large, configurable, FIFO buffer that can be used to buffer input and output streams between programs with a buffer size that is larger than that offered by native unix FIFOs (usually 64k). Show
java -jar picard.jar java -jar picard.jar FindMendelianViolations Finds mendelian violations of all types within a VCF. Takes in VCF or BCF and a pedigree file and looks for high confidence calls where the genotype of the offspring is incompatible with the genotypes of the parents. Assumes the existence of format fields AD, DP, GT, GQ, and PL fields. Take note that the implementation assumes that reads from the PAR will be mapped to the female chromosomerather than the male. This requires that the PAR in the male chromosome be masked so that the aligner has a single coting to map to. This is normally done for the public releases of the human reference. Usage example: java -jar picard.jar FindMendelianViolations I=input.vcf \ TRIO=family.ped \ OUTPUT=mendelian.txt \ MIN_DP=20 Show
java -jar picard.jar java -jar picard.jar ConvertSequencingArtifactToOxoGI=artifact_metricsR=reference.fasta Extract OxoG metrics from generalized artifacts metrics. Show
java -jar picard.jar java -jar picard.jar FilterVcf Applies one or more hard filters to a VCF file to filter out genotypes and variants. Show
java -jar picard.jar Example 1: Sample(s) with either no barcode or barcoded without multiplexing java -jar picard.jar IlluminaBasecallsToFastq READ_STRUCTURE=25T8B25T BASECALLS_DIR=basecallDirectory LANE=001 OUTPUT_PREFIX=noBarcode.1 RUN_BARCODE=run15 FLOWCELL_BARCODE=abcdeACXX Example 2: Multiplexed samples java -jar picard.jar IlluminaBasecallsToFastq READ_STRUCTURE=25T8B25T BASECALLS_DIR=basecallDirectory LANE=001 MULTIPLEX_PARAMS=demultiplexed_output.txt RUN_BARCODE=run15 FLOWCELL_BARCODE=abcdeACXX Generate FASTQ file(s) from Illumina basecall read data. Show
java -jar picard.jar java -jar picard.jar DownsampleSam I=input.bam O=downsampled.bam Downsample a SAM or BAM file. This tool applies a random downsampling algorithm to a SAM or BAM file to retain only a random subset of the reads. Reads in a mate-pair are either both kept or both discarded. Reads marked as not primary alignments are all discarded. Each read is given a probability P of being retained so that runs performed with the exact same input in the same order and with the same value for RANDOM_SEED will produce the same results.All reads for a template are kept or discarded as a unit, with the goal of retaining readsfrom PROBABILITY * input templates. While this will usually result in approximately PROBABILITY * input reads being retained also, for very small PROBABILITIES this may not be the case. A number of different downsampling strategies are supported using the STRATEGY option: ConstantMemory: Downsamples a stream or file of SAMRecords using a hash-projection strategy such that it can run in constant memory. The downsampling is stochastic, and therefore the actual retained proportion will vary around the requested proportion. Due to working in fixed memory this strategy is good for large inputs, and due to the stochastic nature the accuracy of this strategy is highest with a high number of output records, and diminishes at low output volumes. HighAccuracy: Attempts (but does not guarantee) to provide accuracy up to a specified limit. Accuracy is defined as emitting a proportion of reads as close to the requested proportion as possible. In order to do so this strategy requires memory that is proportional to the number of template names in the incoming stream of reads, and will thus require large amounts of memory when running on large input files. Chained: Attempts to provide a compromise strategy that offers some of the advantages of both the ConstantMemory and HighAccuracy strategies. Uses a ConstantMemory strategy to downsample the incoming stream to approximately the desired proportion, and then a HighAccuracy strategy to finish. Works in a single pass, and will provide accuracy close to (but often not as good as) HighAccuracy while requiring memory proportional to the set of reads emitted from the ConstantMemory strategy to the HighAccuracy strategy. Works well when downsampling large inputs to small proportions (e.g. downsampling hundreds of millions of reads and retaining only 2%. Should be accurate 99.9% of the time when the input contains >= 50,000 templates (read names). For smaller inputs, HighAccuracy is recommended instead. Show
java -jar picard.jar java -jar picard.jar FixMateInformation I=input.bam O=fixed_mate.bam Verify mate-pair information between mates and fix if needed.This tool ensures that all mate-pair information is in sync between each read and its mate pair. If no OUTPUT file is supplied then the output is written to a temporary file and then copied over the INPUT file. Reads marked with the secondary alignment flag are written to the output file unchanged. Show
java -jar picard.jar java -jar picard.jar CompareSAMs file_1.bam file_2.bam Compare two input ".sam" or ".bam" files. This tool initially compares the headers of SAM or BAM files. If the file headers are comparable, the tool will examine and compare readUnmapped flag, reference name, start position and strand between the SAMRecords. The tool summarizes information on the number of read pairs that match or mismatch, and of reads that are missing or unmapped (stratified by direction: forward or reverse). Show
java -jar picard.jar java -jar picard.jar GenotypeConcordance CALL_VCF=input.vcf CALL_SAMPLE=sample_name O=gc_concordance.vcf TRUTH_VCF=truth_set.vcf TRUTH_SAMPLE=truth_sample# Evaluate genotype concordance between callsets.This tool evaluates the concordance between genotype calls for samples in different callsets where one is being considered as the truth (aka standard, or reference) and the other as the call that is being evaluated for accuracy. Show
samtools samtools sort [-l level] [-m maxMem] [-o out.bam] [-O format] [-n] [-T tmpprefix] [[email protected] threads] [in.sam|in.bam|in.cram] This tool uses samtools sort command to sort BAM datasets in coordinate or read name order. Show
java -jar picard.jar java -jar picard.jar CreateSequenceDictionary R=reference.fasta O=reference.dict Creates a sequence dictionary for a reference sequence. This tool creates a sequence dictionary file (with ".dict" extension) from a reference sequence provided in FASTA format, which is required by many processing and analysis tools. The output file contains a header but no SAMRecords, and the header contains only sequence records.The reference sequence can be gzipped (both .fasta and .fasta.gz are supported). Show
java -jar picard.jar java -jar picard.jar CollectAlignmentSummaryMetrics R=reference_sequence.fasta I=input.bam O=output.txt Produces a summary of alignment metrics from a SAM or BAM file. This tool takes a SAM/BAM file input and produces metrics detailing the quality of the read alignments as well as the proportion of the reads that passed machine signal-to-noise threshold quality filters. Note that these quality filters are specific to Illumina data; for additional information, please see the corresponding GATK Dictionary entry. Show
samtools samtools index [-bc] [-m INT] aln.bam|aln.cram [out.index] Index a coordinate-sorted BAM or CRAM file for fast random access. (Note that this does not work with SAM files even if they are bgzip compressed — to index such files, use tabix(1) instead.) Show