VSEARCH: a versatile open source tool for metagenomics

Abstract

Background. VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing metagenomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. Methods. When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. Results. VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or de novo), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e. format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. Discussion. VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.

A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units

“…Linkage based clustering approaches were not considered as it was unfeasible to generate distance matrices between the large number of unique reads within the data set. OTUs were clustered at 97% similarity using two alternate greedy algorithms within QIIME—VSEARCH and SUMACLUST (Rognes et al, 2016; Mercier et al, 2013). The open-source algorithm VSEARCH was used in place of the QIIME default USEARCH to overcome the memory limitations of its free version.…”

“…VSEARCH de novo clustering was implemented within the QIIME wrappers using an alias to run VSERARCH in place of USEARCH (Rognes et al, 2016; Edgar, 2010). VSEARCH is not restricted to the same memory limitations as the free version of USEARCH, enabling its use across our whole data set.…”

“…A variety of methods are available to collapse 16S data to OTUs (Edgar, 2010; Edgar, 2013; Rognes et al, 2016; Mercier et al, 2013; Mahé et al, 2014; Schloss & Handelsman, 2005; Eren et al, 2014), often implemented within software wrappers such as QIIME and Mothur (Caporaso et al, 2010; Schloss et al, 2009). One of the main divides in approaches is whether experimental sequences are clustered against a reference database of sequences (Liu et al, 2008), termed closed reference clustering (Navas-Molina et al, 2013), or solely clustered within the experimental data itself, generating what are termed de novo OTUs (Schloss & Handelsman, 2005; Navas-Molina et al, 2013).…”

“…Once a reference or de novo based approach has been selected, a number of different algorithms can be used to cluster sequences by similarity (Schloss & Handelsman, 2005; Caporaso et al, 2010; Edgar, 2010; Edgar, 2013; Rognes et al, 2016; Mercier et al, 2013; Mahé et al, 2014; Eren et al, 2014). Linkage based methods calculate pairwise distances between all sequences allowing hierarchical clustering to OTUs (Schloss & Handelsman, 2005).…”

“…Linkage based methods calculate pairwise distances between all sequences allowing hierarchical clustering to OTUs (Schloss & Handelsman, 2005). There are also multiple greedy algorithms available, which aim to reduce computation time using heuristic approaches to finding optimal groups without calculating all possible distances (Edgar, 2010; Edgar, 2013; Rognes et al, 2016). Furthermore, there have been a number of methods proposed to summarise 16S data without using a predetermined global similarity threshold.…”