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.
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 https://mmseqs.com/latest/mmseqs-static_sse41.tar.gz; tar xvfz mmseqs-static_sse41.tar.gz; export PATH=$(pwd)/mmseqs2/bin/:$PATH
# static build AVX2
wget https://mmseqs.com/latest/mmseqs-static_avx2.tar.gz; 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 mmseqs.com/latest.
MMseqs2 comes with a bash command and parameter auto completion, which can be activated by adding the following lines to your $HOME/.bash_profile:
if [ -f /Path to MMseqs2/util/bash-completion.sh ]; then
source /Path to MMseqs2/util/bash-completion.sh
fi
Compiling MMseqs2 from source has the advantage that it will be optimized to the specific system, which should improve its performance. To compile MMseqs2 git, g++ (4.6 or higher) and cmake (3.0 or higher) are needed. Afterwards, the MMseqs2 binary will be located in the build/bin/ directory.
git clone https://github.com/soedinglab/MMseqs2.git
cd MMseqs2
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=. ..
make
make install
export PATH=$(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=. ..
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.
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"
MMseqs2 provides many additional search modes:
- Iterative sequences-profile searches (like PSI-BLAST) with the
--num-iterationsparameter - 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
Many modes can also be combined. You can, for example, do a translated nucleotide against protein profile search.
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.
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.
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