Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph.

ABSTRACT:

Background

Data volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method.

Results

We present a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Our approach, implemented in the software LEON, employs techniques derived from existing assembly principles. The method is based on a reference probabilistic de Bruijn Graph, built de novo from the set of reads and stored in a Bloom filter. Each read is encoded as a path in this graph, by memorizing an anchoring kmer and a list of bifurcations. The same probabilistic de Bruijn Graph

SUBMITTER: Benoit G 

PROVIDER: S-EPMC4570262 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature