A robust and fast clustering method for amplicon-based studies.

The purpose of swarm is to provide a novel clustering algorithm that handles massive sets of amplicons. Results of traditional clustering algorithms are strongly input-order dependent, and rely on an arbitrary global clustering threshold. swarm results are resilient to input-order changes and rely on a small local linking threshold d, representing the maximum number of differences between two amplicons. swarm forms stable, high-resolution clusters, with a high yield of biological information.

To help users, we describe a complete pipeline starting from raw fastq files, clustering with swarm and producing a filtered OTU table.

swarm 2.0 introduces several novelties and improvements over swarm 1.0:

• built-in breaking phase now performed automatically,
• possibility to output OTU representatives in fasta format (option -w),
• fast algorithm now used by default for d = 1 (linear time complexity),
• a new option called fastidious that refines d = 1 results and reduces the number of small OTUs,

swarm is a single-linkage clustering method, with some superficial similarities with other clustering methods (e.g., Huse et al, 2010). swarm's novelty is its iterative growth process and the use of sequence abundance values to delineate OTUs. swarm properly delineates large OTUs (high recall), and can distinguish OTUs with as little as two differences between their centers (high precision).

swarm uses a local clustering threshold (d), not a global clustering threshold like other algorithms do. Users may be tempted to convert a 97%-global similarity threshold into a number of differences, and to use large d values. This is not a correct use of swarm. OTUs produced by swarm are naturally larger than d, and tests have shown that using the default d value (d = 1) gives good results on most datasets. Using the new fastidious option further improves the quality of results. For long amplicons or shallow sequencing, higher d values can be used (d = 2 or d = 3, very rarely more).

swarm produces high-resolution results, especially when using d = 1. Under certain rare conditions though, a given marker may not evolve fast enough to distinguish molecular taxa. If it concerns abundant sequences, swarm may form an OTU with a large radius, whereas classic clustering methods will cut through randomly, forcing delineation where the 97%-threshold falls. So, keep in mind that molecular markers have limitations too.

swarm most simple usage is:

./swarm amplicons.fasta

That command will apply default parameters (-d 1) to the fasta file amplicons.fasta. The fasta file must be formatted as follows:

>seqID1_1000
acgtacgtacgtacgt
>seqID2_25
cgtcgtcgtcgtcgt

where sequence identifiers are unique and end with a value indicating the number of occurrences of the sequence (e.g., _1000). Alternative format is possible with the option -z, please see the user manual. Swarm requires each fasta entry to present a number of occurrences to work properly. That crucial information can be produced during the dereplication step.

Use swarm -h to get a short help, or see the user manual for a complete description of input/output formats and command line options.

The memory footprint of swarm is roughly 1.6 times the size of the input fasta file. When using the fastidious option, memory footprint can increase significantly. See options -c and -y to control and cap swarm's memory consumption.

Get the latest binaries for GNU/Linux or macOS from the release page. Get the source code from GitHub using the ZIP button or git, and compile swarm:

git clone https://github.com/torognes/swarm.git
cd swarm/src/
make
cd ../bin/

If you have administrator privileges, you can make swarm accessible for all users. Simply copy the binary to /usr/bin/. The man page can be installed this way:

cd ./man/
gzip -c swarm.1 > swarm.1.gz
mv swarm.1.gz /usr/share/man/man1/

Once installed, the man page can be accessed with the command man swarm.

To facilitate the use of swarm, we provide examples of shell commands that can be use to format and check the input fasta file. Warning, these examples may not be suitable for very large files.

We assume your SFF or FASTQ files have been properly pair-assembled (with pear for example), trimmed from adaptors and primers (with cutadapt for example), and converted to fasta.

swarm accepts wrapped fasta files as well as linear fasta files. However, linear fasta files where amplicons are written on two lines (one line for the fasta header, one line for the sequence) are easier to manipulate. For instance, many post-clustering queries can be easily done with grep when fasta files are linear. You can use the following code to linearize your fasta files. Code tested with GNU Awk 4.0.1.

awk 'NR==1 {print ; next} {printf /^>/ ? "\n"$0"\n" : $1} END {printf "\n"}' amplicons.fasta > amplicons_linearized.fasta

In a sample, or collection of sample, a given sequence is likely to appear several times. That number of strictly identical occurrences represents the abundance value of the sequence. Swarm requires all fasta entries to present abundance values to be able to produce high-resolution clusters, like this:

>seqID1_1000
acgtacgtacgtacgt
>seqID2_25
cgtcgtcgtcgtcgt

were seqID1 has an abundance of 1,000 and seqID2 has an abundance of 25 (alternative formats are possible, please see the user manual).

The role of the dereplication step is to identify, merge and sort identical sequences by decreasing abundance. Here is a command using vsearch v1.3.3 or superior:

vsearch \
    --derep_fulllength amplicons.fasta \
    --sizeout \
    --relabel_sha1 \
    --fasta_width 0 \
    --output amplicons_linearized_dereplicated.fasta

The command performs the dereplication, the linearization (--fasta_width 0) and the renaming with hashing values (--relabel_sha1). If you can't or don't want to use vsearch, here is an example using standard command line tools:

grep -v "^>" amplicons_linearized.fasta | \
grep -v [^ACGTacgt] | sort -d | uniq -c | \
while read abundance sequence ; do
    hash=$(printf "${sequence}" | sha1sum)
    hash=${hash:0:40}
    printf ">%s_%d_%s\n" "${hash}" "${abundance}" "${sequence}"
done | sort -t "_" -k2,2nr -k1.2,1d | \
sed -e 's/\_/\n/2' > amplicons_linearized_dereplicated.fasta

Amplicons containing characters other than "ACGT" are discarded. The dereplicated amplicons receive a meaningful unique name (hash values), and are sorted by decreasing number of occurrences and by hash values (to guarantee a stable sorting). The use of a hashing function also provides an easy way to compare sets of amplicons. If two amplicons from two different sets have the same hash code, it means that the sequences they represent are identical.

If for some reason your fasta entries don't have abundance values, and you still want to run swarm, you can easily add fake abundance values:

sed '/^>/ s/$/_1/' amplicons.fasta > amplicons_with_abundances.fasta

Alternatively, you may specify a default abundance value with swarm's --append-abundance (-a) option to be used when abundance information is missing from a sequence.

Here is a typical way to use swarm:

./swarm -f -t 4 -w OTU_representatives.fasta amplicons.fasta > /dev/null

swarm will partition your dataset with the finest resolution (local number of differences d = 1 by default, built-in elimination of potential chained OTUs, fastidious processing) using 4 CPU-cores. OTU representatives will be written to a new fasta file, other results will be discarded (/dev/null).

See the user manual for details on swarm's options and parameters.

To facilitate the use of swarm, we provide examples of options or shell commands that can be use to parse swarm's output. We assume that the amplicon fasta file was prepared as describe above (linearization and dereplication).

The chain-breaking, which used to be performed in a second step in swarm 1.0, is now built-in and performed by default. It is possible to deactivate it with the --no-otu-breaking option, but it is not recommended. The fastidious option is recommended when using d = 1, as it will reduce the number of small OTUs while maintaining a high clustering resolution. The principle of the fastidious option is described in the figure below:

You might want to check the size distribution of OTUs (number of amplicons in each OTU), and count the number of singletons (OTUs containing only one amplicon). It can be easily done with the --statistics-file filename option. Each line in the output file represents an OTU and provides different metrics. See the manual for a complete description.

It is frequent for subsequent analyses to keep only one representative amplicon per OTU (usually the seed) to reduce the computational burden. That operation is easily done with swarm by using the -w filename option.

For each OTU, get the fasta sequences for all amplicons. Warning, this loop can generate a very large number of files. To limit the number of files, a test can be added to exclude swarms with less than n elements. See this wiki page for more examples.

INPUT_SWARM="amplicons.swarms"
INPUT_FASTA="amplicons.fasta"
OUTPUT_FOLDER="swarms_fasta"
AMPLICONS=$(mktemp)
mkdir "${OUTPUT_FOLDER}"
while read OTU ; do
    tr " " "\n" <<< "${OTU}" | sed -e 's/^/>/' > "${AMPLICONS}"
    seed=$(head -n 1 "${AMPLICONS}")
    grep -A 1 -F -f "${AMPLICONS}" "${INPUT_FASTA}" | \
        sed -e '/^--$/d' > "./${OUTPUT_FOLDER}/${seed/>/}.fasta"
done < "${INPUT_SWARM}"
rm "${AMPLICONS}"

If swarm exits with an error message saying This program requires a processor with SSE2, your computer is too old to run swarm (or based on a non x86-64 architecture). swarm only runs on CPUs with the SSE2 instructions, i.e. most Intel and AMD CPUs released since 2004.

To cite swarm, please refer to:

Mahé F, Rognes T, Quince C, de Vargas C, Dunthorn M. (2014) Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2:e593 doi: 10.7717/peerj.593

Mahé F, Rognes T, Quince C, de Vargas C, Dunthorn M. (2015) Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3:e1420 doi: 10.7717/peerj.1420

You are welcome to:

• submit suggestions and bug-reports at: https://github.com/torognes/swarm/issues
• send a pull request on: https://github.com/torognes/swarm/
• compose a friendly e-mail to: Frédéric Mahé [email protected] and Torbjørn Rognes [email protected]

swarm is available in third-party pipelines:

• FROGS: a Galaxy/CLI workflow designed to produce an OTU count matrix from high depth sequencing amplicon data.
• LotuS (v1.30): extremely fast OTU building, annotation, phylogeny and abundance matrix pipeline, based on raw sequencer output.
• QIIME (v1.9): a multi-purpose pipeline for performing microbiome analysis from raw DNA sequencing data.

If you want to try alternative free and open-source clustering methods, here are some links:

• VSEARCH
• Oligotyping
• DNAclust
• Sumaclust
• Crunchclust

swarm 2.2.2 fixes a bug causing Swarm to wait forever in very rare cases when multiple threads were used.

swarm 2.2.1 fixes a memory allocation bug for d=1.

swarm 2.2.0 fixes several problems and improves usability. Corrected output to structure and uclust files when using fastidious mode. Corrected abundance output in some cases. Added check for duplicated sequences and fixed check for duplicated sequence IDs. Checks for empty sequences. Sorts sequences by additional fields to improve stability. Improves compatibility with compilers and operating systems. Outputs sequences in upper case. Allows 64-bit abundances. Shows message when waiting for input from stdin. Improves error messages and warnings. Improves checking of command line options. Fixes remaining errors reported by test suite. Updates documentation.

swarm 2.1.13 removes a bug in the progress bar when writing seeds.

swarm 2.1.12 removes a debugging message.

swarm 2.1.11 fixes two bugs related to the SIMD implementation of alignment that might result in incorrect alignments and scores. The bug only applies when d>1.

swarm 2.1.10 fixes two bugs related to gap penalties of alignments. The first bug may lead to wrong aligments and similarity percentages reported in UCLUST (.uc) files. The second bug makes Swarm use a slightly higher gap extension penalty than specified. The default gap extension penalty used have actually been 4.5 instead of 4.

swarm 2.1.9 fixes a problem when compiling with GCC version 6.

swarm 2.1.8 fixes a rare bug triggered when clustering extremely short undereplicated sequences. Also, alignment parameters are not shown when d=1.

swarm 2.1.7 fixes more problems with seed output. Ignore CR characters in FASTA files. Improved help and error messsages.

swarm 2.1.6 fixes problems with older compilers that do not have the x86intrin.h header file. It also fixes a bug in the output of seeds with the -w option when d>1.

swarm 2.1.5 fixes minor bugs.

swarm 2.1.4 fixes minor bugs in the swarm algorithm used for d=1.

swarm 2.1.3 adds checks of numeric option arguments.

swarm 2.1.2 adds the -a (--append-abundance) option to set a default abundance value to be used when abundance information is missing from the input file. If this option is not specified, missing abundance information will result in a fatal error. The error message in that case is improved.

swarm 2.1.1 fixes a bug with the fastidious option that caused it to ignore some connections between heavy and light swarms.

swarm 2.1.0 marks the first official release of swarm 2.

swarm 2.0.7 writes abundance information in usearch style when using options -w (--seeds) in combination with -z (--usearch-abundance).

swarm 2.0.6 fixes a minor bug.

swarm 2.0.5 improves the implementation of the fastidious option and adds options to control memory usage of the Bloom filter (-y and -c). In addition, an option (-w) allows to output OTU representatives sequences with updated abundances (sum of all abundances inside each OTU). This version also enables dereplication when d = 0.

swarm 2.0.4 includes a fully parallelized fastidious option.

swarm 2.0.3 includes a working fastidious option.

swarm 2.0.2 fixes SSSE3 problems.

swarm 2.0.1 is a development release that partially implements the fastidious option.

swarm 2.0.0 simplifies the usage of swarm by using the fast algorithm and the built-in OTU breaking by default. Some options are changed and some new output options are introduced.

swarm 1.2.21 is supposed to fix some problems related to the use of the SSSE3 CPU instructions which are not always available.

swarm 1.2.20 presents a production-ready version of the alternative algorithm (option -a), with optional built-in OTU breaking (option -n). That alternative algorithmic approach (usable only with d = 1) is considerably faster than currently used clustering algorithms, and can deal with datasets of 100 million unique amplicons or more in a few hours. Of course, results are rigourously identical to the results previously produced with swarm. That release also introduces new options to control swarm output (options -i and -l).

swarm 1.2.19 fixes a problem related to abundance information when the sequence identifier includes multiple underscore characters.

swarm 1.2.18 reenables the possibility of reading sequences from stdin if no file name is specified on the command line. It also fixes a bug related to CPU features detection.

swarm 1.2.17 fixes a memory allocation bug introduced in version 1.2.15.

swarm 1.2.16 fixes a bug in the abundance sort introduced in version 1.2.15.

swarm 1.2.15 sorts the input sequences in order of decreasing abundance unless they are detected to be sorted already. When using the alternative algorithm for d = 1 it also sorts all subseeds in order of decreasing abundance.

swarm 1.2.14 fixes a bug in the output with the swarm breaker option (-b) when using the alternative algorithm (-a).

swarm 1.2.13 updates the citation.

swarm 1.2.12 improves speed of new search strategy for d = 1.

swarm 1.2.11 corrects the number of differences reported in the break swarms output.

swarm 1.2.10 allows amplicon abundances to be specified using the usearch style in the sequence header (e.g. >id;size=1) when the -z option is chosen. Also fixes the bad URL shown in the previous version of swarm.

swarm 1.2.9 includes a parallelized variant of the new search strategy for d = 1. It seems to be fairly scalable up to about 16 threads for longer reads (~400bp), while up to about 8 threads for shorter reads (~150bp). Using about 50% more threads than available physical cores is recommended. This version also includes the d parameter in the beginning of the mothur-style output (e.g., swarm\_1). Also, in the break_swarms output the real number of differences between the seed and the amplicon is indicated in the last column.

swarm 1.2.8 fixes an error with the gap extension penalty. Previous versions effectively used a gap penalty twice as large as intended. This version also introduces an experimental new search strategy in the case where d = 1 that appears to be almost linear and faster at least for datasets of about half a million sequences or more. The new strategy can be turned on with the -a option.

swarm 1.2.7 incorporates a few small changes and improvements to make it ready for integration into QIIME.

swarm 1.2.6 add an option (-r or --mothur) to format the output file as a mothur-compatible list file instead of the native swarm format. When swarm encounters an illegal character in the input sequences it will now report the illegal character and the line number.

swarm 1.2.5 can be run on CPUs without the POPCNT feature. It automatically checks whether the CPU feature is available and uses the appropriate code. The code that avoids POPCNT is just slightly slower. Only basic SSE2 is now required.

swarm 1.2.4 changes the name of the new option from --break_swarms to --break-swarms for consistency with other options, and also adds a companion script swarm_breaker.py to refine swarm results (scripts folder).

swarm 1.2.3 adds an option (-b or --break_swarms) to output all pairs of amplicons to stderr. The data can be used for post-processing of the results to refine the swarms. The syntax of the inline assembly code is also changed for compatibility with more compilers.

swarm 1.2.2 fixes an issue with incorrect values in the statistics file (maximum generation and radius of swarms). This version is also a bit faster.

swarm 1.2.1 removes the need for a SSE4.1 capable CPU and should now be able to run on most servers, desktops and laptops.

swarm 1.2.0 introduces a pre-filtering of similar amplicons based on k-mers. This eliminates most of the time-consuming pairwise alignments and greatly improves speed. The speedup can be more than 100-fold compared to previous swarm versions when using a single thread with a large set of amplicons. Using multiple threads induces a computational overhead, but becomes more and more efficient as the size of the amplicon set increases.

swarm now works on Apple computers. This version also corrects an issue in the pairwise global alignment step that could lead to sub-optimal alignments. Slightly different alignments may result relative to previous version, giving slightly different swarms.

swarm 1.1.0 introduces new optimizations and is 20% faster than the previous version on our test dataset. It also introduces two new output options: statistics and uclust-like format.

By specifying the -s option to swarm it will now output detailed statistics about each swarm to a specified file. It will print the number of unique amplicons, the number of occurrences, the name of the seed and its abundance, the number of singletons (amplicons with an abundance of 1), the number of iterations and the maximum radius of the swarm (i.e. number of differences between the seed and the furthermost amplicon). When using input data sorted by decreasing abundance, the seed is the most abundant amplicon in the swarm.

Some pipelines use the uclust output format as input for subsequent analyses. swarm can now output results in this format to a specified file with the -u option.