VSEARCH

Introduction

The aim of this project is to create an alternative to the USEARCH tool developed by Robert C. Edgar (2010). The new tool should:

have open source code with an appropriate open source license
be free of charge, gratis
have a 64-bit design that handles very large databases and much more than 4GB of memory
be as accurate or more accurate than usearch
be as fast or faster than usearch

We have implemented a tool called VSEARCH which supports de novo and reference based chimera detection, clustering, full-length and prefix dereplication, reverse complementation, masking, all-vs-all pairwise global alignment, exact and global alignment searching, shuffling, subsampling and sorting. It also supports FASTQ file analysis, filtering and conversion.

VSEARCH stands for vectorized search, as the tool takes advantage of parallelism in the form of SIMD vectorization as well as multiple threads to perform accurate alignments at high speed. VSEARCH uses an optimal global aligner (full dynamic programming Needleman-Wunsch), in contrast to USEARCH which by default uses a heuristic seed and extend aligner. This usually results in more accurate alignments and overall improved sensitivity (recall) with VSEARCH, especially for alignments with gaps.

VSEARCH binaries are provided for x86-64 systems running GNU/Linux or OS X (10.7 or higher).

VSEARCH can directly read input query and database files that are compressed using gzip and bzip2 (.gz and .bz2) if the zlib and bzip2 libraries are available.

Most of the nucleotide based commands and options in USEARCH version 7 are supported, as well as some in version 8. The same option names as in USEARCH version 7 has been used in order to make VSEARCH an almost drop-in replacement. VSEARCH does not support amino acid sequences or local alignments. These features may be added in the future.

Getting Help

If you can't find an answer in the VSEARCH documentation, please visit the VSEARCH Web Forum to post a question or start a discussion.

Example

In the example below, VSEARCH will identify sequences in the file database.fsa that are at least 90% identical on the plus strand to the query sequences in the file queries.fsa and write the results to the file alnout.txt.

./vsearch --usearch_global queries.fsa --db database.fsa --id 0.9 --alnout alnout.txt

Download and install

Source distribution To download the source distribution from a release and build the executable and the documentation using Autotools, use the following commands (replace x.y.z with the current version):

wget https://github.com/torognes/vsearch/archive/vx.y.z.tar.gz
tar xzf vx.y.z.tar.gz
cd vsearch-x.y.z
./configure
make
make install  # as root or sudo make install

You may customize the installation directory using the --prefix=DIR option to configure. If the compression libraries zlib and/or bzip2 are installed on the system, they will be detected automatically and support for compressed files will be included in vsearch. Support for compressed files may be disabled using the --disable-zlib and --disable-bzip2 options to configure. A PDF version of the manual will be created from the vsearch.1 manual file if ps2pdf is available, unless disabled using the --disable-pdfman option to configure. Other options may also be applied to configure, please run configure -h to see them all. For versions older than 1.9.0 you'll need to have autoconf installed and run the autogen.sh script before you run configure.

Cloning the repo Instead of downloading the source distribution as a compressed archive, you could clone the repo and build it as shown below. The options to configure as described above are still valid.

git clone git@github.com:torognes/vsearch.git
cd vsearch
./configure
make
make install  # as root or sudo make install

Binary distribution Starting with version 1.4.0, binary distribution files (.tar.gz) for GNU/Linux on x86-64 and Apple Mac OS X on x86-64 containing pre-compiled binaries as well as the documentation (man and pdf files) will be made available as part of each release. The included executables include support for input files compressed by zlib and bzip2 (with files usually ending in .gz or .bz2). Download the appropriate executable for your system using the following commands if you are using a Linux system (replace x.y.z with the current version):

wget https://github.com/torognes/vsearch/releases/download/vx.y.z/vsearch-x.y.z-linux-x86_64.tar.gz
tar xzf vsearch-x.y.z-linux-x86_64.tar.gz

Or these commands if you are using a Mac:

wget https://github.com/torognes/vsearch/releases/download/vx.y.z/vsearch-x.y.z-osx-x86_64.tar.gz
tar xzf vsearch-x.y.z-osx-x86_64.tar.gz

You will now have the binary distribution in a folder called something like vsearch-x.y.z-linux-x86_64 in which you will find three subfolders bin, man and doc. We recommend making a copy or a symbolic link to the vsearch binary bin/vsearch in a folder included in your $PATH, and a copy or a symbolic link to the vsearch man page man/vsearch.1 in a folder included in your $MANPATH. The PDF version of the manual is available in doc/vsearch_manual.pdf.

Old binaries Older VSEARCH binaries (until version 1.1.3) are available here for GNU/Linux on x86-64 systems and Apple Mac OS X on x86-64 systems. These executables include support for input files compressed by zlib and bzip2 (with files usually ending in .gz or .bz2). Download the appropriate executable and make a symbolic link to the vsearch binary in a folder included in your $PATH. You may use the following commands, assuming ~/bin is in your $PATH (substitute linux with osx in those lines if you're on a Mac):

cd ~
mkdir -p bin
cd bin
wget https://github.com/torognes/vsearch/releases/download/v1.1.3/vsearch-1.1.3-linux-x86_64
chmod a+x vsearch-1.1.3-linux-x86_64
ln -s vsearch-1.1.3-linux-x86_64 vsearch

Documentation The VSEARCH user's manual is available in the man folder in the form of a man page. A pdf version (vsearch_manual.pdf) will be generated by make. To install the manpage manually, copy the vsearch.1 file or a create a symbolic link to vsearch.1 in a folder included in your $MANPATH. The manual in both formats is also available with the binary distribution. The manual in PDF form (vsearch_manual.pdf) is also attached to the latest release.

Galaxy wrapper Thanks to the work of the Intergalactic Utilities Commission members, vsearch is now part of the Galaxy ToolShed.

Debian package Thanks to the Debian Med team, there is now a vsearch package in Debian. The example/test data is available in a separate vsearch-data package.

Homebrew package Thanks to Torsten Seeman, a vsearch package for Homebrew has been made.

Converting output to a biom file for use in QIIME and other software

With the from-uccommand in biom 2.1.5 or later, it is possible to convert data in a .uc file produced by vsearch into a biom file that can be read by QIIME and other software. It is described here.

Implementation details and initial assessment

Please note: The vsearch code has evolved substantially over time and the numbers below may not be accurate any more.

Search algorithm: VSEARCH indexes the unique kmers in the database in a way similar to USEARCH, but is currently limited to continuous words (non-spaced seeds) of length 7-15 (the length can be specified with the --wordlength option, default 8). It samples every unique kmer from each query sequence and identifies the number of matching kmers in each database sequence. It then examines the database sequences in order of decreasing number of kmer matches. A full global alignment is computed and those target sequences that satisfy all accept options are retained while the others are rejected. The --maxrejects and --maxaccepts options are supported in this process, indicating the maximum number of non-matching and matching target sequences considered, respectively. Please see the USEARCH paper and supplementary for details.

Kmer selection: How many and which kmers USEARCH chooses from the query sequence is not well documented. It is also not known exactly which database sequences are examined, and in which order. We have therefore experimented with various strategies in order to obtain good performance. Our procedure seems to give results equal to or better than USEARCH.

We have chosen to select all kmers occuring at least once from the query. At least 10 (by default, can be specified with --miwordmatches) of these kmers must be present in the database sequence before it will be considered. If the query has fewer than 10 kmers, all must be present in the database sequence. Furthermore, if several database sequences have the same number of kmer matches, they will be examined in order of decreasing sequence length.

It appears that there are differences in usearch between the searches performed by the --usearch_global command and the clustering commands. Notably, it appears like --usearch_global simply ignores the options --wordlength, --slots and --pattern, while the clustering commands takes them into account. VSEARCH supports the --wordlength option for kmer lengths from 7 to 15, but the options --slots and --pattern are ignored.

Alignment: VSEARCH uses a 8-way 16-bit SIMD vectorized implementation of the full dynamic programming algorithm (Needleman-Wunsch) for global sequence alignment. It is an adaptation of the method described by Rognes (2011). Due to the extreme memory requirements of this method when aligning two long sequences (e.g. more than 5000bp long), an alternative algorithm described by Hirschberg (1975) and Myers and Miller (1988) is used when aligning a pair of long sequences. This alternative algorithm uses only a linear amount of memory but is much slower. USEARCH by default uses a heuristic procedure involving seeding, extension and banded dynamic programming. If the --fulldp option is specified to USEARCH it will also use a full dynamic programming approach, but USEARCH is then considerably slower.

Search Accuracy: The accuracy of VSEARCH searches has been assessed and compared to USEARCH version 7.0.1090. The Rfam 11.0 database was used for the assessment, as described on the USEARCH website. A similar procedure was described in the USEARCH paper using the Rfam 9.1 database.

The database was initially shuffled. Then the first sequence from each of the 2085 Rfam families with at least two members was selected as queries while the rest was used as the database. The ability of VSEARCH and USEARCH to identify another member of the same family as the top hit was measured, and then recall and precision was calculated.

When USEARCH was run without the --fulldp option, VSEARCH had much better recall than USEARCH, but the precision was lower. The F₁-score was considerably higher for VSEARCH. When USEARCH was run with --fulldp, VSEARCH had slightly better recall, precision and F-score than USEARCH.

The recall of VSEARCH was usually about 92.3-93.5% and the precision was usually 93.0-94.1%. When run without the --fulldp option the recall of USEARCH was usually about 83.0-85.3% while precision was 98.5-99.0%. When run with the --fulldp option the recall of USEARCH was usually about 92.0-92.8% and the precision was about 92.2-93.0%.

Please see the files in the eval folder for the scripts used for this assessment.

Search speed: The speed of VSEARCH searches appears to be somewhat faster than USEARCH when USEARCH is run without the --fulldp option. When USEARCH is run with the --fulldp option, VSEARCH may be considerable faster, but it depends on the options and sequences used.

For the accuracy assessment searches in Rfam 11.0 with 100 replicates of the query sequences, VSEARCH needed 46 seconds, whereas USEARCH needed 60 seconds without the --fulldp option and 70 seconds with --fulldp. This includes time for loading, masking and indexing the database (about 2 seconds for VSEARCH, 5 seconds for USEARCH). The measurements were made on a Apple MacBook Pro Retina 2013 with four 2.3GHz Intel Core i7 cores (8 virtual cores) using the default number of threads (8).

Memory: VSEARCH is a 64-bit program and supports very large databases if you have enough memory. Search and clustering might use a lot of memory, especially if run with many threads. Memory usage has not been compared with USEARCH yet.

Clustering: The clustering commands --cluster_smallmem and --cluster_fast have been implemented. These commands support multiple threads. The only difference between --cluster_smallmem and --cluster_fast is that --cluster_fast will sort the sequences by length before clustering, while --cluster_smallmem require the sequences to be in length-sorted order unless the --usersort option is specified. An additional clustering command called --cluster_size has been added that will sort your sequences by abundance before clustering. There is no significant difference in speed or memory usage between these commands. The increased sensitivity of VSEARCH often leads to larger and fewer clusters than USEARCH.

The speed of clustering with VSEARCH relative to USEARCH depends on how many threads are used. Running with a single thread VSEARCH currently seems to be 2-4 times slower than with USEARCH, depending on parameters. Clustering has been parallelized with threads in VSEARCH, but clustering does not seem to be parallelized in USEARCH (despite what the name and documentation for --cluster_fast seems to indicate). Clustering with VSEARCH using 4-8 threads is often faster than USEARCH. The speed of VSEARCH might be further improved with an intra-sequence SIMD-vectorized aligner.

Chimera detection: Chimera detection using the algorithm described by Edgar et al. (2011) has been implemented in VSEARCH. Both the --uchime_ref and --uchime_denovo commands and all their options are supported.

A preliminary assessment of the accuracy of VSEARCH on chimera detection has been performed using the SIMM dataset described in the UCHIME paper. See the eval/chimeval.sh script and the results in eval/chimeval.txt for details. On the datasets with 1-5% substitutions, VSEARCH is generally on par with the original UCHIME implementation (version 4.2.40), and a bit more accurate than the implementation in USEARCH (version 7.0.1090). On the datasets with 1-5% indels, VSEARCH is clearly more accurate than both UCHIME and USEARCH.

VSEARCH is about 40% faster than USEARCH on de novo chimera detection and about 30% faster on detection against a reference database. In VSEARCH uchime_ref is multithreaded, while uchime_denovo is not.

Dereplication and sorting: The dereplication and sorting commands seems to be considerably faster in VSEARCH than in USEARCH.

Masking: VSEARCH by default uses an optimized multithreaded re-implementation of the well-known DUST algorithm by Tatusov and Lipman (source: ftp://ftp.ncbi.nlm.nih.gov/pub/tatusov/dust/version1/src/) to mask simple repeats and low-complexity regions in the sequences before searching and clustering. USEARCH by default uses an undocumented rapid masking method called "fastnucleo" that seems to mask fewer and smaller regions than dust. USEARCH may also be run with the DUST masking method, but the masking then takes something like 30 times longer.

Extensions: A shuffle command has been added. By specifying a FASTA file using the --shuffle option, and an output file with the --output option, VSEARCH will shuffle the sequences in a pseudo-random order. An integer may be specified as the seed with the --seed option to generate the same shuffling several times. By default, or when --seed 0 is specified, the pseudo-random number generator will be initialized with pseudo-random data from the machine to give different numbers each time it is run.

Another extension implemented is that --derep_fulllength and --cluster_fast will honour the --sizein option and add together the abundances for the sequences that are clustered.

An additional clustering command called --cluster_size has been added that will sort sequences by abundance before clustering.

The commands --sortbylength and --sortbysize supports the --topn option to output no more than the given number of sequences.

The width of FASTA formatted output files may be specified with the --fasta_width option and the width of alignments produced with the --alnout and --uchimealn options may be specified with the --rowlen and --alignwidth options, respectively. When an argument of zero (0) is specified for these options, sequences and alignments will not be wrapped.

VSEARCH implements the old USEARCH option --iddef to specify the definition of identity used to rank the hits. Values accepted are 0 (CD-HIT definition using shortest sequence as numerator), 1 (edit distance), 2 (edit distance excluding terminal gaps, default), 3 (Marine Biological Lab definition where entire gaps are considered a single difference) or 4 (BLAST, same as 2). See the USEARCH User Guide 4.1 page 42-44 for details. Also id0, id1, id2, id3 and id4 are accepted as arguments to the --userfields option.

Dependencies

When compiling VSEARCH the header files for the following two optional libraries are required if support for gzip and bzip2 compressed FASTA and FASTQ input files is needed:

libz (zlib library) (zlib.h header file) (optional)
libbz2 (bzip2lib library) (bzlib.h header file) (optional)

VSEARCH will automatically check whether these libraries are available and load them dynamically.

VSEARCH license and third party licenses

The VSEARCH code is dual-licensed either under the GNU General Public License version 3 or under the BSD 2-clause license. Please see LICENSE.txt for details.

VSEARCH includes code from several other projects. We thank the authors for making their source code available.

VSEARCH includes code from Google's CityHash project by Geoff Pike and Jyrki Alakuijala, providing some excellent hash functions available under a MIT license.

VSEARCH includes code derived from Tatusov and Lipman's DUST program that is in the public domain.

VSEARCH includes public domain code written by Alexander Peslyak for the MD5 message digest algorithm.

VSEARCH includes public domain code written by Steve Reid and others for the SHA1 message digest algorithm.

VSEARCH includes statistical data from PEAR by Zhang, Kobert, Flouri & Stamatakis. Used with permission.

The VSEARCH distribution includes code from GNU Autoconf which normally is available under the GNU General Public License, but may be distributed with the special autoconf configure script exception.

VSEARCH may include code from the zlib library copyright Jean-loup Gailly and Mark Adler, distributed under the zlib license.

VSEARCH may include code from the bzip2 library copyright Julian R. Seward, distributed under a BSD-style license.

Code

The code is written in C++ but most of it is actually mostly C with some C++ syntax conventions.

File	Description
abundance.cc	Code for extracting and printing abundance information from FASTA headers
align.cc	New Needleman-Wunsch global alignment, serial. Only for testing.
align_simd.cc	SIMD parallel global alignment of 1 query with 8 database sequences
allpairs.cc	All-vs-all optimal global pairwise alignment (no heuristics)
arch.cc	Architecture specific code (Mac/Linux)
bitmap.cc	Implementation of bitmaps
chimera.cc	Chimera detection
city.cc	CityHash code
cluster.cc	Clustering (cluster_fast and cluster_smallmem)
cpu.cc	Code dependent on specific cpu features (e.g. ssse3)
db.cc	Handles the database file read, access etc
dbhash.cc	Database hashing for exact searches
dbindex.cc	Indexes the database by identifying unique kmers in the sequences
derep.cc	Dereplication
dynlib.cc	Dynamic loading of compression libraries
fasta.cc	FASTA file parser
fastq.cc	FASTQ file parser
fastqops.cc	FASTQ file statistics etc
fastx.cc	Detection of FASTA and FASTQ files, wrapper for FASTA and FASTQ parsers
linmemalign.cc	Linear memory global sequence aligner
maps.cc	Various character mapping arrays
mask.cc	Masking (DUST)
md5.c	MD5 message digest
mergepairs.cc	Paired-end read merging
minheap.cc	A minheap implementation for the list of top kmer matches
msa.cc	Simple multiple sequence alignment and consensus sequence computation for clusters
pvalue.h	Statistical data (from PEAR) used for significance testing of merged paired-end reads
results.cc	Output results in various formats (alnout, userout, blast6, uc)
search.cc	Implements search using global alignment
searchcore.cc	Core search functions for searching, clustering and chimera detection
searchexact.cc	Exact search functions
sha1.c	SHA1 message digest
showalign.cc	Output an alignment in a human-readable way given a CIGAR-string and the sequences
shuffle.cc	Shuffle sequences
sortbylength.cc	Code for sorting by length
sortbysize.cc	Code for sorting by size (abundance)
subsample.cc	Subsampling reads from a FASTA file
unique.cc	Find unique kmers in a sequence
userfields.cc	Code for parsing the userfields option argument
util.cc	Various common utility functions
vsearch.cc	Main program file, general initialization, reads arguments and parses options, writes info.
xstring.h	Code for a simple string class

VSEARCH may be compiled with zlib or bzip2 integration that allows it to read compressed FASTA files. The zlib and the bzip2 libraries are needed for this.

Bugs

All bug reports are highly appreciated. You may submit a bug report here on GitHub as an issue, you could post a message on the VSEARCH Web Forum or you could send an email to torognes@ifi.uio.no.

Limitations

VSEARCH is designed for rather short sequences, and will be slow when sequences are longer than about 5,000 bp. This is because it always performs optimal global alignment on selected sequences.

Future work

Some issues to work on:

testing and debugging
performance evaluation
heuristics for alignment of long sequences (e.g. banded alignment around selected diagonals)?

The VSEARCH team

The main contributors to VSEARCH:

Torbjørn Rognes torognes@ifi.uio.no (Coding, testing, documentation, evaluation)
Frédéric Mahé mahe@rhrk.uni-kl.de (Documentation, testing, feature suggestions)
Tomáš Flouri tomas.flouri@h-its.org (Coding, testing)
Christopher Quince c.quince@warwick.ac.uk (Initiator, feature suggestions, evaluation)
Ben Nichols b.nichols.1@research.gla.ac.uk (Evaluation)

Acknowledgements

Thanks to the following people for patches and other suggestions for improvements:

Jeff Epler jepler@unpythonic.net
Andreas Tille tille@debian.org

Citing VSEARCH

No papers about VSEARCH have been published yet, but a manuscript is in preparation. For now, please cite the VSEARCH GitHub repository.

Test datasets

Test datasets (found in the separate vsearch-data repository) were obtained from the BioMarks project (Logares et al. 2014), the TARA OCEANS project (Karsenti et al. 2011) and the Protist Ribosomal Database (Guillou et al. 2012).

References

Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26 (19): 2460-2461. doi:10.1093/bioinformatics/btq461
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27 (16): 2194-2200. doi:10.1093/bioinformatics/btr381
Farrar M (2007) Striped Smith-Waterman speeds database searches six times over other SIMD implementations. Bioinformatics (2007) 23 (2): 156-161. doi:10.1093/bioinformatics/btl582
Guillou L., Bachar D., Audic S., Bass D., Berney C., Bittner L., Boutte C., Burgaud G., de Vargas C., Decelle J., del Campo J., Dolan J., Dunthorn M., Edvardsen B., Holzmann M., Kooistra W., Lara E., Lebescot N., Logares R., Mahé F., Massana R., Montresor M., Morard R., Not F., Pawlowski J., Probert I., Sauvadet A.-L., Siano R., Stoeck T., Vaulot D., Zimmermann P. & Christen R. (2013) The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Research, 41 (D1), D597-D604. doi:10.1093/nar/gks1160
Hirschberg D.S (1975) A linear space algorithm for computing maximal common subsequences. Comm ACM, 18(6), 341-343. doi:10.1145/360825.360861
Karsenti E., González Acinas S., Bork P., Bowler C., de Vargas C., Raes J., Sullivan M. B., Arendt D., Benzoni F., Claverie J.-M., Follows M., Jaillon O., Gorsky G., Hingamp P., Iudicone D., Kandels-Lewis S., Krzic U., Not F., Ogata H., Pesant S., Reynaud E. G., Sardet C., Sieracki M. E., Speich S., Velayoudon D., Weissenbach J., Wincker P. & the Tara Oceans Consortium (2011) A holistic approach to marine eco-systems biology. PLoS Biology, 9(10), e1001177. doi:10.1371/journal.pbio.1001177
Logares R., Audic S., Bass D., Bittner L., Boutte C., Christen R., Claverie J.-M., Decelle J., Dolan J. R., Dunthorn M., Edvardsen B., Gobet A., Kooistra W. H. C. F., Mahé F., Not F., Ogata H., Pawlowski J., Pernice M. C., Romac S., Shalchian-Tabrizi K., Simon N., Stoeck T., Santini S., Siano R., Wincker P., Zingone A., Richards T., de Vargas C. & Massana R. (2014) The patterning of rare and abundant community assemblages in coastal marine-planktonic microbial eukaryotes. Current Biology, 24(8), 813-821. doi:10.1016/j.cub.2014.02.050
Myers E.W., & Miller W. (1988) Optimal alignments in linear space. Comput Appl Biosci, 4(1), 11-17. doi:10.1093/bioinformatics/4.1.11
Rognes T (2011) Faster Smith-Waterman database searches by inter-sequence SIMD parallelisation. BMC Bioinformatics, 12: 221. doi:10.1186/1471-2105-12-221

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