Journal
PEERJ
Volume 2, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj.593
Keywords
Environmental diversity; Barcoding; Molecular operational taxonomic units
Categories
Funding
- EU EraNet BiodivErsA program BioMarKs [2008-6530]
- French government Investissements d'Avenir project OCEANOMICS [ANR-11-BTBR-0008]
- EU FP7 program MicroB3 [287589]
- Deutsche Forschungsgemeinschaft [DU1319/1-1]
- Centre of Excellence grant from the Research Council of Norway
- EPSRC Career Acceleration Fellowship [EP/H003851/1]
- EPSRC [EP/H003851/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H003851/1] Funding Source: researchfish
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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.
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