Swarm: robust and fast clustering method for amplicon-based studies

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local thresh...

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Detalles Bibliográficos
Autores principales: Mahé, Frédéric, Rognes, Torbjørn, Quince, Christopher, de Vargas, Colomban, Dunthorn, Micah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2014
Materias:
Biodiversity
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4178461/
https://www.ncbi.nlm.nih.gov/pubmed/25276506
http://dx.doi.org/10.7717/peerj.593
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author Mahé, Frédéric
Rognes, Torbjørn
Quince, Christopher
de Vargas, Colomban
Dunthorn, Micah
author_facet Mahé, Frédéric
Rognes, Torbjørn
Quince, Christopher
de Vargas, Colomban
Dunthorn, Micah
author_sort Mahé, Frédéric
collection PubMed
description Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.
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spelling pubmed-41784612014-09-30 Swarm: robust and fast clustering method for amplicon-based studies Mahé, Frédéric Rognes, Torbjørn Quince, Christopher de Vargas, Colomban Dunthorn, Micah PeerJ Biodiversity Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PeerJ Inc. 2014-09-25 /pmc/articles/PMC4178461/ /pubmed/25276506 http://dx.doi.org/10.7717/peerj.593 Text en © 2014 Mahé et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , 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) and either DOI or URL of the article must be cited.
spellingShingle Biodiversity
Mahé, Frédéric
Rognes, Torbjørn
Quince, Christopher
de Vargas, Colomban
Dunthorn, Micah
Swarm: robust and fast clustering method for amplicon-based studies
title Swarm: robust and fast clustering method for amplicon-based studies
title_full Swarm: robust and fast clustering method for amplicon-based studies
title_fullStr Swarm: robust and fast clustering method for amplicon-based studies
title_full_unstemmed Swarm: robust and fast clustering method for amplicon-based studies
title_short Swarm: robust and fast clustering method for amplicon-based studies
title_sort swarm: robust and fast clustering method for amplicon-based studies
topic Biodiversity
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4178461/
https://www.ncbi.nlm.nih.gov/pubmed/25276506
http://dx.doi.org/10.7717/peerj.593
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AT devargascolomban swarmrobustandfastclusteringmethodforampliconbasedstudies
AT dunthornmicah swarmrobustandfastclusteringmethodforampliconbasedstudies

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