Abstract
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.
Highlights
Traditional de novo amplicon clustering methods that can handle large high-throughput sequencing datasets (e.g., Edgar, 2010; Ghodsi, Liu & Pop, 2011; Fu et al, 2012) suffer from two fundamental problems
We previously introduced the open source Swarm v1 program that implemented an initial clustering phase written in C++, a breaking phase written in Python (Mahe et al, 2014)
There can be under-grouping of closely related amplicons leading to small operational taxonomic units (OTUs) surrounding a larger OTU. To address this problem in Swarm v2, we introduced a new step—called the fastidious option—to graft low abundant OTUs onto more abundant ones by postulating a linking amplicon (Fig. 1C)
Summary
Traditional de novo amplicon clustering methods that can handle large high-throughput sequencing datasets (e.g., Edgar, 2010; Ghodsi, Liu & Pop, 2011; Fu et al, 2012) suffer from two fundamental problems. Comm., 2015) found that in comparison to other clustering methods, Swarm v1 tended to produce relatively more low abundant OTUs; e.g., singletons and doubletons. Swarm v1 and other current de novo algorithms could not cluster today’s largest high-throughout sequencing datasets within a reasonable amount of time (Rideout et al, 2014).
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