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

Frédéric Mahé, Torbjørn Rognes, Christopher Quince, Colomban de Vargas, Micah Dunthorn

Research output: Contribution to journalArticlepeer-review

586 Citations (Scopus)


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

Original languageEnglish
Article numbere593
Issue number1
Publication statusPublished - 2014


  • Barcoding
  • Environmental diversity
  • Molecular operational taxonomic units

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