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metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics

Abstract : Motivation: The availability of large metagenomic data offers great opportunities for the population geno-mic analysis of uncultured organisms, especially for small eukaryotes that represent an important part of the unexplored biosphere while playing a key ecological role. However, the majority of these species lacks reference genome or transcriptome which constitutes a technical barrier for classical population genomic analyses. Results: We introduce the metavariant species (MVS) model, a representation of the species only by intra-species nucleotide polymorphism. We designed a method combining reference-free variant calling, multiple density-based clustering and maximum weighted independent set algorithms to cluster intra-species variant into MVS directly from multisample metagenomic raw reads without reference genome or reads assembly. The frequencies of the MVS variants are then used to compute population genomic statistics such as FST in order to estimate genomic differentiation between populations and to identify loci under natural selection. The MVSs construction was tested on simulated and real metagenomic data. MVs showed the required quality for robust population genomics and allowed an accurate estimation of genomic differentiation (∆FST < 0.0001 and < 0.03 on simulated and real data respectively). Loci predicted under natural selection on real data were all found by MVSs. MVSs represent a new paradigm that may simplify and enhance holistic approaches for population genomics and evolution of microorganisms. Availability: The method was implemented in a R package, metaVaR.
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Submitted on : Monday, February 3, 2020 - 4:02:57 PM
Last modification on : Sunday, June 26, 2022 - 2:46:37 AM
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Romuald Laso-Jadart, Christophe Ambroise, Pierre Peterlongo, Mohammed-Amin Madoui. metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics. PLoS ONE, Public Library of Science, 2020, ⟨10.1371/journal.pone.0244637⟩. ⟨hal-02464983⟩



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