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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2014-09-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/593.pdf |
| _version_ | 1797418526333468672 |
|---|---|
| author | Frédéric Mahé Torbjørn Rognes Christopher Quince Colomban de Vargas Micah Dunthorn |
| author_facet | Frédéric Mahé Torbjørn Rognes Christopher Quince Colomban de Vargas Micah Dunthorn |
| author_sort | Frédéric Mahé |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:34:11Z |
| format | Article |
| id | doaj.art-62fb3025145649aea4b5424375ec7d36 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:34:11Z |
| publishDate | 2014-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-62fb3025145649aea4b5424375ec7d362023-12-03T11:00:28ZengPeerJ Inc.PeerJ2167-83592014-09-012e59310.7717/peerj.593593Swarm: robust and fast clustering method for amplicon-based studiesFrédéric Mahé0Torbjørn Rognes1Christopher Quince2Colomban de Vargas3Micah Dunthorn4CNRS, UMR 7144, EPEP – Évolution des Protistes et des Écosystèmes Pélagiques, Station Biologique de Roscoff, Roscoff, FranceDepartment of Microbiology, Oslo University Hospital, Rikshospitalet, Oslo, NorwaySchool of Engineering, University of Glasgow, Glasgow, UKCNRS, UMR 7144, EPEP – Évolution des Protistes et des Écosystèmes Pélagiques, Station Biologique de Roscoff, Roscoff, FranceDepartment of Ecology, University of Kaiserslautern, Kaiserslautern, GermanyPopular 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.https://peerj.com/articles/593.pdfEnvironmental diversityBarcodingMolecular operational taxonomic units |
| spellingShingle | Frédéric Mahé Torbjørn Rognes Christopher Quince Colomban de Vargas Micah Dunthorn Swarm: robust and fast clustering method for amplicon-based studies PeerJ Environmental diversity Barcoding Molecular operational taxonomic units |
| 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 | Environmental diversity Barcoding Molecular operational taxonomic units |
| url | https://peerj.com/articles/593.pdf |
| work_keys_str_mv | AT fredericmahe swarmrobustandfastclusteringmethodforampliconbasedstudies AT torbjørnrognes swarmrobustandfastclusteringmethodforampliconbasedstudies AT christopherquince swarmrobustandfastclusteringmethodforampliconbasedstudies AT colombandevargas swarmrobustandfastclusteringmethodforampliconbasedstudies AT micahdunthorn swarmrobustandfastclusteringmethodforampliconbasedstudies |