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PeerJ. 2014 Sep 25;2:e593. doi: 10.7717/peerj.593. eCollection 2014.

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

Author information

1
CNRS, UMR 7144, EPEP - Évolution des Protistes et des Écosystèmes Pélagiques, Station Biologique de Roscoff , Roscoff , France ; Sorbonne Universités, UPMC Univ Paris 06, UMR 7144, Station Biologique de Roscoff , Roscoff , France ; Department of Ecology, University of Kaiserslautern , Kaiserslautern , Germany.
2
Department of Microbiology, Oslo University Hospital, Rikshospitalet , Oslo , Norway ; Department of Informatics, University of Oslo , Oslo , Norway.
3
School of Engineering, University of Glasgow , Glasgow , UK.
4
CNRS, UMR 7144, EPEP - Évolution des Protistes et des Écosystèmes Pélagiques, Station Biologique de Roscoff , Roscoff , France ; Sorbonne Universités, UPMC Univ Paris 06, UMR 7144, Station Biologique de Roscoff , Roscoff , France.
5
Department of Ecology, University of Kaiserslautern , Kaiserslautern , Germany.

Abstract

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.

KEYWORDS:

Barcoding; Environmental diversity; Molecular operational taxonomic units

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