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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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Frédéric Mahé
Torbjørn Rognes
Christopher Quince
ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)
PeerJ
SHILAP Revista de lepidopterología
Centre National de la Recherche Scientifique
Sorbonne Université
University of Glasgow
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Mahé et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69d939a3c7f0c3ae80a3c537 — DOI: https://doi.org/10.7717/peerj.593
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