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MMseqs2: ultra fast and sensitive search and clustering suite
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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 proteins/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.


Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

Steinegger M and Soeding J. Clustering huge protein sequence sets in linear time. Nature Communications, doi: 10.1038/s41467-018-04964-5 (2018).

BioConda Install Github All Releases Build Status Docker Pulls AppVeyor CI Travis CI Zenodo DOI


The MMseqs2 user guide is available in our GitHub Wiki or as a PDF file (Thanks to pandoc!). We provide a tutorial of MMseqs2 here.


Keep posted about MMseqs2/Linclust updates by following Martin on Twitter.

08/10/2018 ECCB18 tutorial of MMseqs2 is available here.

07/07/2018 Linclust has just been published at Nature Communications.

17/10/2017 MMseqs2 has just been published at Nature Biotechnology.


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 sse4.1
 wget; tar xvfz mmseqs-static_sse41.tar.gz; export PATH=$(pwd)/mmseqs2/bin/:$PATH
 # static build AVX2
 wget; tar xvfz mmseqs-static_avx2.tar.gz; export PATH=$(pwd)/mmseqs2/bin/:$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

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/ ]; then
            source /Path to MMseqs2/util/

Compile 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 git, g++ (4.6 or higher) and cmake (3.0 or higher) are needed. Afterwards, the MMseqs2 binary will be located in the build/bin/ directory.

    git clone
    cd MMseqs2
    mkdir build
    cd build
    make install 
    export PATH=$(pwd)/bin/:$PATH

❗️ To compile MMseqs2 on MacOS, first install the gcc compiler from Homebrew. The default MacOS clang compiler does not support OpenMP and MMseqs2 will only be able to use a single thread. Then use the following cmake call:

    CXX="$(brew --prefix)/bin/g++-8" cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=. ..

Easy workflows

We provide easy workflows to search and cluster. The easy-search searches directly with a FASTA/FASTQ file against a either another FASTA/FASTQ file or an already existing MMseqs2 target database.

    mmseqs createdb examples/DB.fasta targetDB
    mmseqs easy-search examples/QUERY.fasta targetDB alnRes tmp 

For clustering, MMseqs2 easy-cluster and 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         

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     

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 optimal efficiency, we recommend to use MMseqs2 workflows and modules directly.

How to search

You can use the query database "QUERY.fasta" and target database "DB.fasta" in the examples folder to test the search workflow. First, you need to convert the FASTA files into the MMseqs2 database format.

    mmseqs createdb examples/QUERY.fasta queryDB
    mmseqs createdb examples/DB.fasta targetDB

If the target database will be used several times, we recommend to precompute an index of targetDB as this saves overhead computations. The index should be created on a computer that has the at least the same amount of memory as the computer that performs the search.

    mmseqs createindex targetDB tmp

MMseqs2 stores intermediate results in tmp. Using a fast local drive can reduce load on a shared filesystem and increase speed.

To run the search execute:

    mmseqs search queryDB targetDB resultDB tmp

The sensitivity of the search can be adjusted with -s parameter and should be adapted based on your use case (see setting sensitivity -s parameter).

If you require the exact alignment information (Sequence identity, alignment string, ...) in later steps add the option -a, without this parameter MMseqs2 will automatically decide if the exact alignment criteria to optimize computational time.

Please ensure that, in case of large input databases, the tmp directory provides enough free space. Our user guide provides or information about disk space requirements.

Then convert the result database into a BLAST-tab formatted database (format: qId, tId, seqIdentity, alnLen, mismatchCnt, gapOpenCnt, qStart, qEnd, tStart, tEnd, eVal, bitScore).

    mmseqs convertalis queryDB targetDB resultDB resultDB.m8

The output can be customized wit 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 in the convertalis module. Make sure that you used the option -a during the search (mmseqs search ... -a).

    mmseqs convertalis queryDB targetDB resultDB resultDB.pair --format-output "query,target,qaln,taln"

Other search modes

MMseqs2 provides many additional search modes:

Many modes can also be combined. You can, for example, do a translated nucleotide against protein profile search.

How to cluster

Before clustering, convert your database into the MMseqs2 database format:

    mmseqs createdb examples/DB.fasta DB

Then execute the clustering:

    mmseqs cluster DB clu tmp

or linear time clutering (faster but less sensitive):

    mmseqs linclust DB clu tmp

Please adjust the clustering criteria and check if temporary direcotry provides enough free space. For disk space requirements, see the user guide.

To generate a FASTA-style formatted output file from the ffindex output file, type:

    mmseqs createseqfiledb DB clu clu_seq 
    mmseqs result2flat DB DB clu_seq clu_seq.fasta

To generate a TSV-style formatted output file from the ffindex output file, type:

    mmseqs createtsv DB DB clu clu.tsv

To extract the representative sequences from the clustering result call:

    mmseqs result2repseq DB clu DB_clu_rep
    mmseqs result2flat DB DB DB_clu_rep DB_clu_rep.fasta --use-fasta-header

Read more about the format here.

Memory Requirements

MMseqs2 checks the available memory of the computer and automatically divide the target database in parts that fit into memory. Splitting the database will increase the runtime slightly.

The memory consumption grows linearly with the number of residues in the database. The following formula can be used to estimate the index size.

    M = (7 × N × L) byte + (8 × a^k) byte

Where L is the average sequence length and N is the database size.

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 can not use MPI. The version string of MMseqs2 will have a -MPI suffix, if it was build successfully with MPI support.

To search with multiple servers call the search or 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 -np 42" mmseqs search queryDB targetDB resultDB tmp
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