@DocumentedFeature
public class DownsampleSam
extends CommandLineProgram

Summary

This tool applies a downsampling algorithm to a SAM or BAM file to retain only a (deterministically random) subset of the reads. Reads from the same template (e.g. read-pairs, secondary and supplementary reads) are all either kept or discarded as a unit, with the goal of retaining reads from PROBABILITY * (input templates). The results will contain approximately PROBABILITY * (input reads), however 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 more than 50,000 templates (read names). For smaller inputs, HighAccuracy is recommended instead.

The number of records written can be output to a CollectQualityYieldMetrics.QualityYieldMetrics metrics file via the METRICS_FILE.

Usage examples:

Downsample file, keeping about 10% of the reads

 java -jar picard.jar DownsampleSam \
       I=input.bam \
       O=downsampled.bam \
       P=0.1
 

Downsample file, keeping 2% of the reads

 java -jar picard.jar DownsampleSam \
       I=input.bam \
       O=downsampled.bam \
       STRATEGY=Chained \
       P=0.02 \
       ACCURACY=0.0001
 

Downsample file, keeping 0.001% of the reads (may require more memory)

 java -jar picard.jar DownsampleSam \
       I=input.bam \
       O=downsampled.bam \
       STRATEGY=HighAccuracy \
       P=0.00001 \
       ACCURACY=0.0000001