Sam/Bam Manipulation

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: 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.

Usage: java -jar picard.jar CollectAlignmentSummaryMetrics R=reference_sequence.fasta I=input.bam O=output.txt

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: Extract OxoG metrics from generalized artifacts metrics.

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

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: 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: 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: Identifies duplicate reads.

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

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

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

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: 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: 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

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:

Usage: java -jar picard.jar CollectInsertSizeMetrics I=input.bam O=insert_size_metrics.txt H=insert_size_histogram.pdf M=0.5

Function: converts BAM to a number of other formats

Usage: bamtools convert -format json -in myData.bam -out myData.json