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Section: New Results

Parallel algorithm for de Bruijn graph compaction

Constructing a de Bruijn graph is an important step in the analysis of NGS data. This data structure is used in several applications, such as de novo assembly, variant detection, and transcriptome quantification. However, the representation of this graph often consumes prohibitive amounts of memory for large datasets. An operation, called compaction, enables to represent the graph more efficiently. However, so far, there was no algorithm for compacting the graph quickly and in low memory.

Along with colleagues at Inria Rennes and at Penn State University, we introduced a parallel algorithm and an implementation, BCALM 2, for constructing directly a compacted de Bruijn graph given a set of reads. Our results show that this algorithm enables to construct the graph for very large datasets, such as the spruce and pine genomes, in reasonable time and memory on a single machine. This represents a performance improvement of two orders of magnitude compared to previously available methods. BCALM 2 is open-source and was published at ISMB 2016 [20].