Swarm v2: highly-scalable and high-resolution amplicon clustering

By F. Mahé, Torbjørn Rognes, Christopher Quince, C. de Vargas, Micah Dunthorn. PeerJ, 2015

529 citations recorded by the source corpus.

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f46c758d007ec833cb86132a789d5778d0d7687f

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Citations in the corpus

Bats as potential suppressors of multiple agricultural pests: A case study from Madagascar (2019, methodology): “0 algorithm (Mahé et al., 2015) with a distance value of d= 13, which offers a conservative solution to the high variability of the COI gene (Siegenthaler et al.”
Genetic and Manual Survey Methods Yield Different and Complementary Views of an Ecosystem (2017, methodology): “…reads with a custom Unix-based script (O’Donnell, 2015), which calls third-party programs Swarm, Cutadapt, and PEAR (Martin, 2011; Zhang et al., 2014; Mahé et al., 2015) to move from raw sequence data to a quality-controlled dataset of counts of sequences from operational taxonomic units (OTUs).”
Exact sequence variants should replace operational taxonomic units in marker-gene data analysis (2017, methodology): “De novo OTUs lack consistent labels so all data must be pooled for assignment, causing computational costs to scale quadratically with total sequencing effort and rendering common de novo methods prohibitively costly as study sizes increase (Rideout et al, 2014; Mahe et al, 2015).”
Marine Vertebrate Biodiversity and Distribution Within the Central California Current Using Environmental DNA (eDNA) Metabarcoding and Ecosystem Surveys (2019, methodology): “Sequences were clustered using Swarm v2 (Mahé et al., 2015) to create operational taxonomic units (OTU; clustering = 97%).”