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Multiple comparative metagenomics using multiset k-mer counting

Authors :
Gaëtan Benoit
Pierre Peterlongo
Mahendra Mariadassou
Erwan Drezen
Sophie Schbath
Dominique Lavenier
Claire Lemaitre
Source :
PeerJ Computer Science, Vol 2, p e94 (2016)
Publication Year :
2016
Publisher :
PeerJ Inc., 2016.

Abstract

Background Large 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 novo methods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results. Methods These limitations motivated the development of a new de novo metagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts by k-mer counts. Simka scales-up today’s metagenomic projects thanks to a new parallel k-mer counting strategy on multiple datasets. Results Experiments 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 the k-mer level is highly correlated with extremely precise de novo comparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling.

Details

Language :
English
ISSN :
23765992
Volume :
2
Database :
Directory of Open Access Journals
Journal :
PeerJ Computer Science
Publication Type :
Academic Journal
Accession number :
edsdoj.14a278f10cd14792be92c5fbc9623bb1
Document Type :
article
Full Text :
https://doi.org/10.7717/peerj-cs.94