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© 2016 Benoit et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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

Title
Multiple comparative metagenomics using multiset k-mer counting
Author
Benoit, Gaëtan; Peterlongo, Pierre; Mariadassou, Mahendra; Drezen, Erwan; Schbath, Sophie; Lavenier, Dominique; Lemaitre, Claire
Publication year
2016
Publication date
Nov 14, 2016
Publisher
PeerJ, Inc.
e-ISSN
23765992
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1950209577
Copyright
© 2016 Benoit et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.