Sam/Bam Manipulation

Function: This tool generates a bigWig or bedGraph file of fragment or read coverages.

Usage: bamCoverage [options] -b reads.bam -o coverage.bw

Function: Validates a SAM or BAM file.

Usage: java -jar picard.jar ValidateSamFile I=input.bam MODE=SUMMARY

Function: Convert sequence alignments in BAM format into BED, BED12, and/or BEDPE records

Usage: bedtools bamtobed [OPTIONS] -i <BAM>

Supported input format: BAM

Function: Convert BAM file into wig/bigWig format.

Usage: bam2wig.py -s chrom.sizes -i sample.bam -o out [options]

Function: Applies one or more hard filters to a VCF file to filter out genotypes and variants.

Usage: java -jar picard.jar FilterVcf

Function: 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).

Usage: java -jar picard.jar FifoBuffer

Function: 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.

Usage: java -jar picard.jar FilterSamReads I=input.bam O=output.bam READ_LIST_FILE=read_names.txt FILTER=filter_value

Function: 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).

Usage: java -jar picard.jar FastqToSam F1=file_1.fastq O=fastq_to_bam.bam SM=for_tool_testing

Function: Converts SAM dataset into its binary, BAM.

Usage: samtools sort -O bam -o sorted_input.bam [INPUT SAM]

Function: 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

Usage: java -jar picard.jar FindMendelianViolations

Function: 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).

Usage: java -jar picard.jar CollectMultipleMetrics I=input.bam O=multiple_metrics R=reference_sequence.fasta

Function: Estimates the numbers of unique molecules in a sequencing library.

Usage: java -jar picard.jar EstimateLibraryComplexity I=input.bamO=est_lib_complex_metrics.txt

Function: bedtools bamtobed is a conversion utility that converts sequence alignments in BAM format into BED, BED12, and/or BEDPE records.

Usage: bamToBed [OPTIONS] -i <BAM>

Function: 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.

Usage: java -jar picard.jar DownsampleSam I=input.bam O=downsampled.bam

Function: Identifies duplicate reads using information from read positions and UMIs.

Usage: java -jar picard.jar UmiAwareMarkDuplicatesWithMateCigar