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Fast and sensitive taxonomic assignment to metagenomic contigs

SUMMARY: MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by...

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Autores principales: Mirdita, M, Steinegger, M, Breitwieser, F, Söding, J, Levy Karin, E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479651/
https://www.ncbi.nlm.nih.gov/pubmed/33734313
http://dx.doi.org/10.1093/bioinformatics/btab184
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author Mirdita, M
Steinegger, M
Breitwieser, F
Söding, J
Levy Karin, E
author_facet Mirdita, M
Steinegger, M
Breitwieser, F
Söding, J
Levy Karin, E
author_sort Mirdita, M
collection PubMed
description SUMMARY: MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2–18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. AVAILABILITY AND IMPLEMENTATION: MMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-84796512021-09-30 Fast and sensitive taxonomic assignment to metagenomic contigs Mirdita, M Steinegger, M Breitwieser, F Söding, J Levy Karin, E Bioinformatics Applications Notes SUMMARY: MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2–18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. AVAILABILITY AND IMPLEMENTATION: MMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-03-18 /pmc/articles/PMC8479651/ /pubmed/33734313 http://dx.doi.org/10.1093/bioinformatics/btab184 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Mirdita, M
Steinegger, M
Breitwieser, F
Söding, J
Levy Karin, E
Fast and sensitive taxonomic assignment to metagenomic contigs
title Fast and sensitive taxonomic assignment to metagenomic contigs
title_full Fast and sensitive taxonomic assignment to metagenomic contigs
title_fullStr Fast and sensitive taxonomic assignment to metagenomic contigs
title_full_unstemmed Fast and sensitive taxonomic assignment to metagenomic contigs
title_short Fast and sensitive taxonomic assignment to metagenomic contigs
title_sort fast and sensitive taxonomic assignment to metagenomic contigs
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479651/
https://www.ncbi.nlm.nih.gov/pubmed/33734313
http://dx.doi.org/10.1093/bioinformatics/btab184
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