metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
27 01 2022
Historique:
received: 11 08 2021
revised: 12 10 2021
accepted: 14 11 2021
pubmed: 19 11 2021
medline: 3 2 2023
entrez: 18 11 2021
Statut: ppublish

Résumé

Taxonomic analysis of microbial communities is well supported at the level of species and strains. However, species can contain significant phenotypic diversity and strains are rarely widely shared across global populations. Stratifying the diversity between species and strains can identify 'subspecies', which are a useful intermediary. High-throughput identification and profiling of subspecies is not yet supported in the microbiome field. Here, we use an operational definition of subspecies based on single nucleotide variant (SNV) patterns within species to identify and profile subspecies in metagenomes, along with their distinctive SNVs and genes. We incorporate this method into metaSNV v2, which extends existing SNV-calling software to support further SNV interpretation for population genetics. These new features support microbiome analyses to link SNV profiles with host phenotype or environment and niche-specificity. We demonstrate subspecies identification in marine and fecal metagenomes. In the latter, we analyze 70 species in 7524 adult and infant subjects, supporting a common subspecies population structure in the human gut microbiome and illustrating some limits in subspecies calling. Source code, documentation, tutorials and test data are available at https://github.com/metasnv-tool/metaSNV and https://metasnv.embl.de. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 34791031
pii: 6430104
doi: 10.1093/bioinformatics/btab789
pmc: PMC8796361
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1162-1164

Subventions

Organisme : European Union's Horizon 2020 Research and Innovation Programme
ID : ERC-AdG-669830
Organisme : Bundesministerium für Bildung und Forschung
ID : 01GL1746B
Organisme : Swiss National Science Foundation (SNSF)
ID : 205321_184955
Organisme : Eidgenössische Technische Hochschule (ETH) Zürich
Organisme : European Molecular Biology Laboratory (EMBL)

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press.

Auteurs

Thea Van Rossum (T)

Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.

Paul I Costea (PI)

Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.

Lucas Paoli (L)

Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8092 Zürich, Switzerland.

Renato Alves (R)

Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.

Roman Thielemann (R)

Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.

Shinichi Sunagawa (S)

Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8092 Zürich, Switzerland.

Peer Bork (P)

Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.
Department of Bioinformatics, Biocenter, University of Würzburg, 97070 Würzburg, Germany.
Yonsei Frontier Lab (YFL), Yonsei University, Seoul 03722, South Korea.

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