VSEARCH: a versatile open source tool for metagenomics - PubMed (original) (raw)

VSEARCH: a versatile open source tool for metagenomics

Torbjørn Rognes et al. PeerJ. 2016.

Abstract

Background: VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics 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.

Keywords: Alignment; Chimera detection; Clustering; Dereplication; Masking; Metagenomics; Parallellization; Searching; Sequences; Shuffling.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1

Figure 1. Search accuracy on the RFAM v11 dataset.

USEARCH version 7 (blue), USEARCH version 8 (orange) and VSEARCH (black) was run using the usearch_global command on subsets of the RFAM dataset to identify members of the same families. The plot shows the true positive rate (also known as the recall or sensitivity) as a function of the false discovery rate at varying sequence similarity levels. This curve is based on data from 20 shufflings of the dataset.

Figure 2

Figure 2. Clustering accuracy on the even dataset.

USEARCH version 7 (blue) and 8 (orange) and VSEARCH (black) was run using abundance sorting (cluster_smallmem) (A, C, E) and length sorting (cluster_fast) (B, D, F) on the even dataset. The performance is indicated with the adjusted Rand index (A, B), recall (C, D) and precision (E, F) metrics.

Figure 3

Figure 3. Clustering accuracy on the uneven dataset.

USEARCH version 7 (blue) and 8 (orange) and VSEARCH (black) was run using abundance sorting (cluster_smallmem) (A, C, E) and length sorting (cluster_fast) (B, D, F) on the uneven dataset. The performance is indicated with the adjusted Rand index (A, B), recall (C, D) and precision (E, F) metrics.

Figure 4

Figure 4. Clustering speed.

Median wall time in seconds to cluster the even (A, B) and uneven (C, D) datasets using USEARCH version 7 (blue) and 8 (orange) and VSEARCH (black) using abundance sorting (cluster_smallmem) (A, C) and length sorting (cluster_fast) (B, D).

Figure 5

Figure 5. Chimera detection performance with the Greengenes dataset shown with ROC curves.

USEARCH version 7 (blue) and 8 (orange) and VSEARCH (black) was run using the uchime_denovo (A, B) and the uchime_ref (C, D) commands on simulated Illumina data based on the Greengenes database that has either been clustered with a 97% identity threshold (using the cluster_fast command in VSEARCH) (A, C) or dereplicated (using the derep_fulllength command in VSEARCH) (B, D).

Figure 6

Figure 6. Chimera detection performance on the SILVA dataset shown with ROC curves.

USEARCH version 7 (blue) and 8 (orange) and VSEARCH (black) was run using the uchime_denovo (A, B) and the uchime_ref (C, D) commands on simulated Illumina data based on the SILVA database that has either been clustered with a 97% identity threshold (using the cluster_fast command in USEARCH) (A, C) or dereplicated (using the derep_fulllength command in VSEARCH) (B, D).

Figure 7

Figure 7. Subsampling performance.

The observed distribution of the maximum amplicon abundance in 10,000 random subsamplings of 5% of the TARA V9 dataset using VSEARCH (A, black) and USEARCH version 8 (B, orange) is shown. The expected mean abundance is 782,133.5 (blue dashed line).

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