# 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.

##  Publications

[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)](https://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3988.html).

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

[Mirdita M, Steinegger M and Soeding J. MMseqs2 desktop and local web server app for fast, interactive sequence searches. Bioinformatics, doi: 10.1093/bioinformatics/bty1057 (2019)](https://academic.oup.com/bioinformatics/article/35/16/2856/5280135)

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<p align="center"><img src="https://raw.githubusercontent.com/soedinglab/mmseqs2/master/.github/mmseqs2_logo.png" height="256" /></p>


## Documentation
The MMseqs2 user guide is available in our [GitHub Wiki](https://github.com/soedinglab/mmseqs2/wiki) or as a [PDF file](https://mmseqs.com/latest/userguide.pdf) (Thanks to [pandoc](https://github.com/jgm/pandoc)!). We provide a tutorial of MMseqs2 [here](https://github.com/soedinglab/metaG-ECCB18-partII).

Keep posted about MMseqs2/Linclust updates by following Martin on [Twitter](https://twitter.com/thesteinegger).

## Installation
MMseqs2 can be used by compiling from source, downloading a statically compiled version, using [Homebrew](https://github.com/Homebrew/brew), [conda](https://github.com/conda/conda) or [Docker](https://github.com/moby/moby). 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](https://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:

<pre>
        if [ -f /<b>Path to MMseqs2</b>/util/bash-completion.sh ]; then
            source /<b>Path to MMseqs2</b>/util/bash-completion.sh
        fi
</pre>
         
### 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 `git`, `g++` (4.8 or later) and `cmake` (2.8.12 or later) 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 -j 4
        make install
        export PATH=$(pwd)/bin/:$PATH

:exclamation: 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:

        CC="$(brew --prefix)/bin/gcc-9" CXX="$(brew --prefix)/bin/g++-9" cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=. ..
                

## Getting started
We provide `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](https://github.com/soedinglab/mmseqs2/wiki).

### Cluster

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 --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     
                
Sequence identity is in default [estimated](https://github.com/soedinglab/MMseqs2/wiki#how-does-mmseqs2-compute-the-sequence-identity) to output real sequence identity use `--alignment-mode 3`.
Read more about the [clustering format](https://github.com/soedinglab/mmseqs2/wiki#clustering-format) in our user guide.
                
Please adjust the [clustering criteria](https://github.com/soedinglab/MMseqs2/wiki#clustering-criteria) and check if temporary directory provides enough free space. For disk space requirements, see the user guide.

### Search
         
The `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](https://github.com/soedinglab/mmseqs2/wiki#set-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](https://github.com/soedinglab/MMseqs2/wiki#how-to-control-the-speed-of-the-search).

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](https://github.com/soedinglab/mmseqs2/wiki#custom-alignment-format-with-convertalis).

### Taxonomy
The `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 `--lca-mode`.

        mmseqs createdb examples/DB.fasta targetDB
        mmseqs createtaxdb targetDB tmp
        mmseqs createindex targetDB tmp
        mmseqs easy-taxonomy examples/QUERY.fasta targetDB alnRes tmp

In default `createtaxdb` assigns every sequence with a Uniprot accession to a taxonomical identifier and downloads the NCBI taxonomy. We also support [BLAST](https://github.com/soedinglab/MMseqs2/wiki#create-a-sequence-database-with-taxonomic-information-from-an-existing-blast-database), [SILVA](https://github.com/soedinglab/MMseqs2/wiki#create-a-sequence-database-with-taxonomic-information-for-silva) or [custom taxonomical](https://github.com/soedinglab/MMseqs2/wiki#manually-annotate-a-sequence-database-with-taxonomic-information) databases.

Read more about the [taxonomy format](https://github.com/soedinglab/MMseqs2/wiki#taxonomy-format) and the [classification](https://github.com/soedinglab/MMseqs2/wiki#taxonomy-assignment-using-mmseqs-taxonomy) in our user guide.

### Supported search modes

MMseqs2 provides many additional search modes:
 * Iterative sequences-profile searches (like PSI-BLAST) with the `--num-iterations` parameter
 * [Translated searches](https://github.com/soedinglab/MMseqs2/wiki#translated-sequence-searching) of nucleotides against proteins (blastx), proteins against nucleotides (tblastn) or nucleotide against nucleotide (tblastx)
 * [Iterative increasing sensitivity searches](https://github.com/soedinglab/MMseqs2/wiki#how-to-find-the-best-hit-the-fastest-way) to find only the best hits faster
 * [Taxonomic assignment](https://github.com/soedinglab/MMseqs2/wiki#taxonomy-assignment-using-mmseqs-taxonomy) using 2bLCA or LCA
 * Fast ungapped alignment searches to find [very similar sequence matches](https://github.com/soedinglab/MMseqs2/wiki#mapping-very-similar-sequences-using-mmseqs-map)
 * Very fast and sensitive searches against [profile databases such as the PFAM](https://github.com/soedinglab/MMseqs2/wiki#how-to-create-a-target-profile-database-from-pfam)
 * [Reciprocal best hits search](https://github.com/soedinglab/MMseqs2/wiki#reciprocal-best-hit-using-mmseqs-rbh)
 * [Web search API and user interface](https://github.com/soedinglab/MMseqs2-App)

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

### Memory Requirements
MMseqs2 minimum memory requirements for `cluster` or `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 `~1.7`.
   
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 `--split-memory-limit`.

### 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 `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 -pernode -np 42" mmseqs search queryDB targetDB resultDB tmp


