Swarm: robust and fast clustering method for amplicon-based studies
Centre National de la Recherche Scientifique · University of Kaiserslautern · +6 more institutions
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.
Citation impact
- FWCI
- 20.77
- Percentile
- 100%
- References
- 27
Authors
5- FMFrédéric MahéCorresponding
Centre National de la Recherche Scientifique, University of Kaiserslautern, Station Biologique de Roscoff, Sorbonne Université, Adaptation et Diversité en Milieu Marin
- TRTorbjørn Rognes
Oslo University Hospital, University of Oslo
- CQChristopher Quince
University of Glasgow
- CDColomban de Vargas
Centre National de la Recherche Scientifique, Station Biologique de Roscoff, Sorbonne Université, Adaptation et Diversité en Milieu Marin
- MDMicah Dunthorn
University of Kaiserslautern
Topics & keywords
- Cluster analysis
- Amplicon
- Swarm behaviour
- Computer science
- Amplicon sequencing
- Data mining
- Centroid
- Scalability