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

Frédéric Mahé (1, 2) , Torbjørn Rognes (3, 4) , Christopher Quince (5) , Colomban de Vargas (1) , Micah Dunthorn (2)

1 EPEP - Evolution des Protistes et Ecosystèmes Pélagiques
2 University of Kaiserslautern [Kaiserslautern]
3 Department of Informatics [Oslo]
4 Department of Microbiology [Oslo]
5 School of Engineering

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by cen-troid 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.

Mots clés: Environmental diversity; Barcoding; Molecular operational taxonomic units

Domaines: Biodiversité et Ecologie; Microbiologie et Parasitologie

Frédéric Mahé, Torbjørn Rognes, Christopher Quince, Colomban de Vargas, Micah Dunthorn. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ, 2014, 2, pp.e593. ⟨10.7717/peerj.593⟩. ⟨hal-01245140⟩