MMseqs2: ultra fast and sensitive protein search and clustering suite
MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.
Keep posted about MMseqs2/Linclust updates by following Martin on Twitter.
MMseqs2 can be used by compiling from source, downloading a statically compiled version, using Homebrew, conda or Docker. MMseqs2 requires a 64-bit system (check with
uname -a | grep x86_64) with at least the SSE4.1 instruction set (check by executing
cat /proc/cpuinfo | grep sse4_1 on Linux or
sysctl -a | grep machdep.cpu.features | grep SSE4.1 on MacOS).
# install by brew brew install mmseqs2 # install via conda conda install -c bioconda mmseqs2 # install docker docker pull soedinglab/mmseqs2 # static build with SSE4.1 wget https://mmseqs.com/latest/mmseqs-linux-sse41.tar.gz; tar xvfz mmseqs-linux-sse41.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH # static build with AVX2 wget https://mmseqs.com/latest/mmseqs-linux-avx2.tar.gz; tar xvfz mmseqs-linux-avx2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH
The AVX2 version is faster than SSE4.1, check if AVX2 is supported by executing
cat /proc/cpuinfo | grep avx2 on Linux and
sysctl -a | grep machdep.cpu.leaf7_features | grep AVX2 on MacOS).
We also provide static binaries for MacOS and Windows at mmseqs.com/latest.
MMseqs2 comes with a bash command and parameter auto completion, which can be activated by adding the following lines to your $HOME/.bash_profile:
if [ -f /Path to MMseqs2/util/bash-completion.sh ]; then source /Path to MMseqs2/util/bash-completion.sh fi
Compilation from source
Compiling MMseqs2 from source has the advantage that it will be optimized to the specific system, which should improve its performance. To compile MMseqs2
g++ (4.8 or later) and
cmake (2.8.12 or later) are needed. Afterwards, the MMseqs2 binary will be located in the
git clone https://github.com/soedinglab/MMseqs2.git cd MMseqs2 mkdir build cd build cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=. .. make -j 4 make install export PATH=$(pwd)/bin/:$PATH
easy workflows to cluster, search and assign taxonomy. These
easy workflows are a shorthand to deal directly with FASTA/FASTQ files as input and output. MMseqs2 provides many modules to transform, filter, execute external programs and search. However, these modules use the MMseqs2 database formats, instead of the FASTA/FASTQ format. For maximum flexibility, we recommend using MMseqs2 workflows and modules directly. Please read more about this in the documentation.
For clustering, MMseqs2
easy-linclust are available.
easy-cluster by default clusters the entries of a FASTA/FASTQ file using a cascaded clustering algorithm.
mmseqs easy-cluster examples/DB.fasta clusterRes tmp --min-seq-id 0.5 -c 0.8 --cov-mode 1
easy-linclust clusters the entries of a FASTA/FASTQ file. The runtime scales linearly with input size. This mode is recommended for huge datasets.
mmseqs easy-linclust examples/DB.fasta clusterRes tmp
Please adjust the clustering criteria and check if temporary directory provides enough free space. For disk space requirements, see the user guide.
easy-search searches directly with a FASTA/FASTQ files against either another FASTA/FASTQ file or an already existing MMseqs2 database.
mmseqs easy-search examples/QUERY.fasta DB.fasta alnRes tmp
It is also possible to pre-compute the index for the target database:
mmseqs createdb examples/DB.fasta targetDB mmseqs createindex targetDB tmp mmseqs easy-search examples/QUERY.fasta targetDB alnRes tmp
The speed and sensitivity of the
search can be adjusted with
-s parameter and should be adapted based on your use case (see setting sensitivity -s parameter). A very fast search would use a sensitivity of
-s 1.0, while a very sensitive search would use a sensitivity of up to
-s 7.0. A detailed guide how to speed up searches is here.
The output can be customized with the
--format-output option e.g.
--format-output "query,target,qaln,taln" returns the query and target accession and the pairwise alignments in tab separated format. You can choose many different output columns.
easy-taxonomy workflow can be used assign sequences taxonomical labels. It performs a search against a target sequence databases and computes the lowest common ancestor of all equal scoring top hits (default). Other assignment options are available through
mmseqs createdb examples/DB.fasta targetDB mmseqs createtaxdb targetDB tmp mmseqs createindex targetDB tmp mmseqs easy-taxonomy examples/QUERY.fasta targetDB alnRes tmp
Supported search modes
MMseqs2 provides many additional search modes:
- Iterative sequences-profile searches (like PSI-BLAST) with the
- Translated searches of nucleotides against proteins (blastx), proteins against nucleotides (tblastn) or nucleotide against nucleotide (tblastx)
- Iterative increasing sensitivity searches to find only the best hits faster
- Taxonomic assignment using 2bLCA or LCA
- Fast ungapped alignment searches to find very similar sequence matches
- Very fast and sensitive searches against profile databases such as the PFAM
- Reciprocal best hits search
- Web search API and user interface
Many modes can also be combined. You can, for example, do a translated nucleotide against protein profile search.
MMseqs2 minimum memory requirements for
linclust is 1 byte per sequence residue,
search needs 1 byte per target residue. Sequence databases can be compressed using the
--compress flag, DNA sequences can be reduced by a factor of
~3.5 and proteins by
MMseqs2 checks the available system memory and automatically divides the target database in parts that fit into memory. Splitting the database will increase the runtime slightly. It is possible to control the memory usage using
How to run MMseqs2 on multiple servers using MPI
MMseqs2 can run on multiple cores and servers using OpenMP and Message Passing Interface (MPI). MPI assigns database splits to each compute node, which are then computed with multiple cores (OpenMP).
Make sure that MMseqs2 was compiled with MPI by using the
-DHAVE_MPI=1 flag (
cmake -DHAVE_MPI=1 -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=. ..). Our precompiled static version of MMseqs2 cannot use MPI. The version string of MMseqs2 will have a
-MPI suffix, if it was built successfully with MPI support.
To search with multiple servers, call the
cluster workflow with the MPI command exported in the RUNNER environment variable. The databases and temporary folder have to be shared between all nodes (e.g. through NFS):
RUNNER="mpirun -pernode -np 42" mmseqs search queryDB targetDB resultDB tmp