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

Cluster analysisAmpliconSwarm behaviourComputer scienceAmplicon sequencingData miningCentroidScalabilityDependency (UML)Consensus clusteringSelection (genetic algorithm)Artificial intelligencePattern recognition (psychology)Correlation clusteringCURE data clustering algorithmBiologyPolymerase chain reaction

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Year
2014
Type
article
Volume
2
Pages
e593-e593
Citations
907
Access
Closed

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Frédéric Mahé, Torbjørn Rognes, Christopher Quince et al. (2014). Swarm: robust and fast clustering method for amplicon-based studies. PeerJ , 2 , e593-e593. https://doi.org/10.7717/peerj.593

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DOI
10.7717/peerj.593