Multiple comparative metagenomics using multisetk-mer counting

Abstract

BackgroundLarge scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand,de novomethods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results.MethodsThese limitations motivated the development of a newde novometagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts byk-mer counts. Simka scales-up today’s metagenomic projects thanks to a new parallelk-mer counting strategy on multiple datasets.ResultsExperiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at thek-mer level is highly correlated with extremely precisede novocomparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling.