Abstract
MotivationPreviously we presented swarm, an open-source amplicon clustering programme that produces fine-scale molecular operational taxonomic units (OTUs) that are free of arbitrary global clustering thresholds. Here, we present swarm v3 to address issues of contemporary datasets that are growing towards tera-byte sizes.ResultsWhen compared with previous swarm versions, swarm v3 has modernized C++ source code, reduced memory footprint by up to 50%, optimized CPU-usage and multithreading (more than 7 times faster with default parameters), and it has been extensively tested for its robustness and logic.Availability and implementationSource code and binaries are available at https://github.com/torognes/swarm.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
In emerging planetary biology, large-scale amplicon sequencing datasets are used to unravel global ecological and evolutionary patterns within and across biomes and biota (de Vargas et al, 2015; Mahé et al, 2017; Giner et al, 2020)
Motivation: Previously we presented swarm, an open-source amplicon clustering program that produces fine-scale molecular operational taxonomic units (OTUs) that are free of arbitrary global clustering thresholds
Availability: Source code and binaries are available at https://github.com/torognes/swarm Contact: frederic.mahe@cirad.fr Supplementary information: Supplementary data are available at Bioinformatics online
Summary
Large-scale amplicon sequencing datasets are used to unravel global ecological and evolutionary patterns within and across biomes and biota (de Vargas et al, 2015; Mahé et al, 2017; Giner et al, 2020). A critical bioinformatics step in the handling of these massive metabarcoding datasets is to cluster the sequencing reads into operational taxonomic units (OTUs). Swarm v1 (Mahé et al, 2014) was introduced as a novel approach to cluster amplicons into OTUs, inspired by previous single-linkage methods such as DOTUR (Schloss & Handelsman, 2005). The key underlying idea of swarm was to use a local, iterative, single-linkage clustering process to group closely related sequences (by default with one difference in their nucleotide sequences, i.e. d = 1). The code could only be executed on GNU/Linux and macOS on x86-64 CPUs. And swarm v2 was multithreaded and fast, its time and memory requirements could become a limiting factor on very large current and future datasets, especially as amplicon sequences become longer.
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