Sam/Bam Manipulation

Function: Extract OxoG metrics from generalized artifacts metrics.

Usage: java -jar picard.jar ConvertSequencingArtifactToOxoG I=artifact_metricsR=reference.fasta

Function: Identifies duplicate reads, accounting for mate CIGAR. This tool locates and tags duplicate reads (both PCR and optical) in a BAM or SAM file, where duplicate reads are defined as originating from the same original fragment of DNA, taking into account the CIGAR string of read mates. It is intended as an improvement upon the original MarkDuplicates algorithm, from which it differs in several ways, includingdifferences in how it breaks ties. It may be the most effective duplicate marking program available, as it handles all cases including clipped and gapped alignments and locates duplicate molecules using mate cigar information. However, please note that it is not yet used in the Broad's production pipeline, so use it at your own risk. Note also that this tool will not work with alignments that have large gaps or deletions, such as those from RNA-seq data. This is due to the need to buffer small genomic windows to ensure integrity of the duplicate marking, while large skips (ex. skipping introns) in the alignment records would force making that window very large, thus exhausting memory.

Usage: java -jar picard.jar MarkDuplicatesWithMateCigar I=input.bam O=mark_dups_w_mate_cig.bam M=mark_dups_w_mate_cig_metrics.txt

Function: Provided a BAM/SAM file and reference gene model, this module will calculate how mapped reads were distributed over genome feature (like CDS exon, 5’UTR exon, 3’ UTR exon, Intron, Intergenic regions). When genome features are overlapped (e.g. a region could be annotated as both exon and intron by two different transcripts) , they are prioritize as: CDS exons > UTR exons > Introns > Intergenic regions, for example, if a read was mapped to both CDS exon and intron, it will be assigned to CDS exons.

Usage: read_distribution.py -i Pairend_StrandSpecific_51mer_Human_hg19.bam -r hg19.refseq.bed12

Function: 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).

Usage: java -jar picard.jar CompareSAMs file_1.bam file_2.bam

Function: This tool will generate multiple output datasets for each redagroup from the input dataset.

Usage: samtools split [options] merged.sam|merged.bam|merged.cram

Function: This tool uses samtools sort command to sort BAM datasets in coordinate or read name order.

Usage: samtools sort [-l level] [-m maxMem] [-o out.bam] [-O format] [-n] [-T tmpprefix] [-@ threads] [in.sam|in.bam|in.cram]

Function: Identifies duplicate reads.

Usage: java -jar picard.jar MarkDuplicates I=input.bam O=marked_duplicates.bam M=marked_dup_metrics.txt

Function: Equally divide BAM file (m alignments) into n parts. Each part contains roughly m/n alignments that are randomly sampled from total alignments.

Usage: divide_bam.py -n 3 -i SingleEnd_StrandSpecific_50mer_Human_hg19.bam -o output

Function: 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).

Usage: java -jar picard.jar CreateSequenceDictionary R=reference.fasta O=reference.dict

Function: Collect whole genome sequencing-related metrics. This tool computes metrics that are useful for evaluating coverage and performance of whole genome sequencing experiments. These metrics include the percentages of reads that pass minimal base- and mapping- quality filters as well as coverage (read-depth) levels. The histogram output is optional and for a given run, displays two separate outputs on the y-axis while using a single set of values for the x-axis. Specifically, the first column in the histogram table (x-axis) is labeled 'coverage' and represents different possible coverage depths. However, it also represents the range of values for the base quality scores and thus should probably be labeled 'sequence depth and base quality scores'. The second and third columns (y-axes) correspond to the numbers of bases at a specific sequence depth 'count' and the numbers of bases at a particular base quality score 'baseq_count' respectively.Although similar to the CollectWgsMetrics tool, the default thresholds for CollectRawWgsMetrics are less stringent. For example, the CollectRawWgsMetrics have base and mapping quality score thresholds set to '3' and '0' respectively, while the CollectWgsMetrics tool has the default threshold values set to '20' (at time of writing). Nevertheless, both tools enable the user to input specific threshold values.

Usage: java -jar picard.jar CollectRawWgsMetrics I=input.bam O=raw_wgs_metrics.txt R=reference_sequence.fasta INCLUDE_BQ_HISTOGRAM=true

Function: Uses samtools flagstat command to print descriptive information for a BAM dataset.

Usage: samtools flagstat in.sam|in.bam|in.cram

Function: Gathers multiple VCF files from a scatter operation into a single VCF file. Input files must be supplied in genomic order and must not have events at overlapping positions.

Usage: java -jar picard.jar GatherVcfs

Function: Sorts one or more VCF files. This tool sorts the records in VCF files according to the order of the contigs in the header/sequence dictionary and then by coordinate. It can accept an external sequence dictionary. If no external dictionary is supplied, the VCF file headers of multiple inputs must have the same sequence dictionaries.If running on multiple inputs (originating from e.g. some scatter-gather runs), the input files must contain the same sample names in the same column order.

Usage: java -jar picard.jar SortVcf I=vcf_1.vcf I=vcf_2.vcf O=sorted.vcf

Function: Shuffles and groups reads together by their names. A faster alternative to a full query name sort, collate ensures that reads of the same name are grouped together in contiguous groups, but doesn't make any guarantees about the order of read names between groups. The output from this command should be suitable for any operation that requires all reads from the same template to be grouped together.

Usage: samtools collate [options] in.sam|in.bam|in.cram [out.prefix]

Function: Produces RNA alignment metrics for a SAM or BAM file.

Usage: java -jar picard.jar CollectRnaSeqMetrics I=input.bam O=output.RNA_Metrics REF_FLAT=ref_flat.txt STRAND=SECOND_READ_TRANSCRIPTION_STRAND RIBOSOMAL_INTERVALS=ribosomal.interval_list