Storing, transmitting, and archiving data produced by next generation sequencing is a significant computational burden. New compression techniques tailored to short-read sequence data are needed.
Researchers from Carnegie Mellon University and Stony Brook University have devloped an approach to compression that reduces the difficulty of managing large-scale sequencing data. This novel approach sits between pure reference-based compression and reference-free compression and combines much of the benefit of reference-based approaches with the flexibility of de novo encoding. The new method, called path encoding, draws a connection between storing paths in de Bruijn graphs and context-dependent arithmetic coding. Supporting this method is a system to compactly store sets of kmers that is of independent interest. The researchers were able to encode RNA-seq reads using 3% – 11% of the space of the sequence in raw FASTA files, which is on average more than 34% smaller than competing approaches. They also show that even if the reference is very poorly matched to the reads that are being encoded, good compression can still be achieved.
Availability and implementation: Source code and binaries freely available for download at http://www.cs.cmu.edu/~ckingsf/software/pathenc/, implemented in Go and supported on Linux and Mac OS X.