Compression of High Throughput Sequencing Data

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.

Researchers at INRIA/IRISA/GenScale have devloped a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Their 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 is used to perform a lossy transformation of the quality scores, which allows to obtain higher compression rates without losing pertinent information for downstream analyses.


Schematic description of LEON’s path encoding. In the upper part, the mapping of two reads to the de Bruijn Graph is represented. Kmer anchors are shown in blue, bifurcations to (read on the left side) or difference from the graph (read on the right side) are respectively highlighted in green and red. In the bottom part, the corresponding path encodings for these two reads are shown: the index of the kmer anchor, and for each side the path length and bifurcation list

LEON was run on various real sequencing datasets (whole genome, exome, RNA-seq or metagenomics). In all cases, LEON showed higher overall compression ratios than state-of-the-art compression software. On a C. elegans whole genome sequencing dataset, LEON divided the original file size by more than 20.

Availability – LEON is an open source software, distributed under GNU affero GPL License, available for download at

Benoit G, Lemaitre C, Lavenier D, Drezen E, Dayris T, Uricaru R, Rizk G. (2015) Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph. BMC Bioinformatics 16(1):288. [article]

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