Commit 34dc873d authored by Steffen Möller's avatar Steffen Möller
Browse files

New upstream version 10-6d92c+ds

parent 3926e77f

.gitmodules

0 → 100644
+3 −0
Original line number Diff line number Diff line 
[submodule "util/regression"]

	path = util/regression

	url = https://github.com/soedinglab/MMseqs2-Regression.git

.travis.yml

+7 −16
Original line number Diff line number Diff line
@@ -24,6 +24,7 @@ matrix: 
        - zlib1g-dev

        - libbz2-dev

        - vim-common

        - shellcheck

    env: CC=clang-3.6 CXX=clang++-3.6

  - os: linux

    dist: trusty
@@ -41,6 +42,7 @@ matrix: 
        - zlib1g-dev

        - libbz2-dev

        - vim-common

        - shellcheck

    env: CC=clang-7 CXX=clang++-7

  - os: linux

    dist: trusty
@@ -54,6 +56,7 @@ matrix: 
        - zlib1g-dev

        - libbz2-dev

        - vim-common

        - shellcheck

    env: CC=gcc-4.8 CXX=g++-4.8

  - os: linux

    dist: trusty
@@ -69,6 +72,7 @@ matrix: 
        - zlib1g-dev

        - libbz2-dev

        - vim-common

        - shellcheck

    env: CC=gcc-8 CXX=g++-8

  - os: linux

    dist: trusty
@@ -85,6 +89,7 @@ matrix: 
        - libbz2-dev

        - vim-common

        - libopenmpi-dev

        - shellcheck

    env: MPI=1 CC=gcc-8 CXX=g++-8

  - os: osx

    osx_image: xcode10.1
@@ -96,6 +101,7 @@ matrix: 
        - gcc@8

        - zlib

        - bzip2

        - shellcheck

    env: CC=gcc-8 CXX=g++-8

  allow_failures:

  - env: QEMU_ARM=1
@@ -116,7 +122,7 @@ script: 
    elif [[ "$TRAVIS_OS_NAME" == "linux" ]]; then \

      if [[ -n "$MPI" ]]; then MPI=1; else MPI=0; fi; \

      mkdir build; cd build; \

      cmake -G Ninja -DENABLE_WERROR=1 -DHAVE_MPI="$MPI" -DHAVE_SSE4_1=1 .. \

      cmake -G Ninja -DENABLE_WERROR=1 -DHAVE_MPI="$MPI" -DHAVE_SSE4_1=1 -DHAVE_TESTS=1 -DREQUIRE_OPENMP=0 .. \

        || exit 1; ninja || exit 1; \

    elif [[ "$TRAVIS_OS_NAME" == "osx" ]]; then \

      ./util/build_osx.sh . build || exit 1; \
@@ -124,18 +130,3 @@ script: 
      exit 1; \

    fi

after_success:

  - |

    if [[ "$TRAVIS_PULL_REQUEST" != "false" ]] || [[ "$TRAVIS_OS_NAME" != "osx" ]]; then \

      exit 0; \

    fi; \

    if [[ "$encrypted_4188a201d0b5_key" == "" ]] || [[ "$encrypted_4188a201d0b5_iv" == "" ]]; then \

      exit 0; \

    fi; \

    openssl aes-256-cbc -K "$encrypted_4188a201d0b5_key" -iv "$encrypted_4188a201d0b5_iv" -in ./util/.travis.enc -out "$HOME/.ssh/id_rsa" -d; \

    chmod 400 "$HOME/.ssh/id_rsa"; \

    ssh -o StrictHostKeyChecking=no codeship@uniclust.mmseqs.com \

      "mkdir -p /home/mirdita/repositories/mmseqs-webserver/archive/${TRAVIS_COMMIT}"; \

    cd build; \

    scp -o StrictHostKeyChecking=no mmseqs-osx-static_sse41.tar.gz mmseqs-osx-static_avx2.tar.gz \

        codeship@uniclust.mmseqs.com:/home/mirdita/repositories/mmseqs-webserver/archive/${TRAVIS_COMMIT};

CMakeLists.txt

+19 −13
Original line number Diff line number Diff line
@@ -6,6 +6,7 @@ set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake") 


set(FRAMEWORK_ONLY 0 CACHE BOOL "Framework Mode")

set(HAVE_SANITIZER 0 CACHE BOOL "Have Sanitizers")

set(INSTALL_UTIL 1 CACHE BOOL "Install util scripts")

set(VERSION_OVERRIDE "" CACHE STRING "Override version string in help and usage messages")



#Sanitizers
@@ -21,7 +22,7 @@ if (NOT CMAKE_BUILD_TYPE) 
endif (NOT CMAKE_BUILD_TYPE)



# find compiler

if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")

if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang")

    # using Clang

    message("-- Compiler is clang(++)")

    set(CMAKE_COMPILER_IS_CLANG 1)
@@ -58,26 +59,31 @@ if (APPLE) 
    set(MMSEQS_CXX_FLAGS "${MMSEQS_CXX_FLAGS} -D__APPLE_API_STRICT_CONFORMANCE")

endif ()



if(CMAKE_SYSTEM_NAME STREQUAL "FreeBSD")

    set(MMSEQS_CXX_FLAGS "${MMSEQS_CXX_FLAGS} -D_WITH_GETLINE")

endif()



# zstd

SET(ZSTD_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/lib/zstd")

SET(CMAKE_INSTALL_LIBDIR bin)

# We use ZSTD_findDecompressedSize which is only available with ZSTD_STATIC_LINKING_ONLY

# Thus we cannot use a system provided libzstd

set(ZSTD_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/lib/zstd")

set(CMAKE_INSTALL_LIBDIR bin)

set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/lib/zstd/build/cmake/CMakeModules")

OPTION(ZSTD_LEGACY_SUPPORT "LEGACY SUPPORT" OFF)

OPTION(ZSTD_BUILD_STATIC "BUILD STATIC LIBRARIES" ON)

OPTION(ZSTD_BUILD_SHARED "BUILD SHARED LIBRARIES" OFF)

OPTION(ZSTD_MULTITHREAD_SUPPORT "MULTITHREADING SUPPORT" OFF)

OPTION(ZSTD_BUILD_PROGRAMS "BUILD PROGRAMS" OFF)

OPTION(ZSTD_BUILD_CONTRIB "BUILD CONTRIB" OFF)

OPTION(ZSTD_BUILD_TESTS "BUILD TESTS" OFF)

option(ZSTD_LEGACY_SUPPORT "LEGACY SUPPORT" OFF)

option(ZSTD_BUILD_STATIC "BUILD STATIC LIBRARIES" ON)

option(ZSTD_BUILD_SHARED "BUILD SHARED LIBRARIES" OFF)

option(ZSTD_MULTITHREAD_SUPPORT "MULTITHREADING SUPPORT" OFF)

option(ZSTD_BUILD_PROGRAMS "BUILD PROGRAMS" OFF)

option(ZSTD_BUILD_CONTRIB "BUILD CONTRIB" OFF)

option(ZSTD_BUILD_TESTS "BUILD TESTS" OFF)

include_directories(lib/zstd/lib)

add_subdirectory(lib/zstd/build/cmake/lib EXCLUDE_FROM_ALL)



# tinyexpr

OPTION(TE_NAT_LOG "Define the log function as natural logarithm." ON)

include_directories(lib/tinyexpr)

add_subdirectory(lib/tinyexpr EXCLUDE_FROM_ALL)



include_directories(lib)

include_directories(lib/kseq)

include_directories(lib/simd)

include_directories(lib/gzstream)

include_directories(lib/alp)
@@ -88,6 +94,6 @@ add_subdirectory(lib/alp) 
add_subdirectory(lib/ksw2)

add_subdirectory(data)

add_subdirectory(src)

if (NOT FRAMEWORK_ONLY)

if (NOT FRAMEWORK_ONLY AND INSTALL_UTIL)

    add_subdirectory(util)

endif ()

README.md

+57 −97
Original line number Diff line number Diff line 
# 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.

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
@@ -7,11 +7,12 @@ MMseqs2 (Many-against-Many sequence searching) is a software suite to search and 


[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)



[![BioConda Install](https://img.shields.io/conda/dn/bioconda/mmseqs2.svg?style=flag&label=BioConda%20install)](https://anaconda.org/bioconda/mmseqs2)

[![Github All Releases](https://img.shields.io/github/downloads/soedinglab/mmseqs2/total.svg)](https://github.com/soedinglab/mmseqs2/releases/latest)

[![Docker Pulls](https://img.shields.io/docker/pulls/soedinglab/mmseqs2.svg)](https://hub.docker.com/r/soedinglab/mmseqs2)

[![Build Status](https://dev.azure.com/themartinsteinegger/mmseqs2/_apis/build/status/soedinglab.MMseqs2?branchName=master)](https://dev.azure.com/themartinsteinegger/mmseqs2/_build/latest?definitionId=2&branchName=master)

![AppVeyor CI](https://ci.appveyor.com/api/projects/status/lq8nxeb0j8v38d1a?svg=true)

[![Travis CI](https://travis-ci.org/soedinglab/MMseqs2.svg?branch=master)](https://travis-ci.org/soedinglab/MMseqs2)

[![Zenodo DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.840208.svg)](https://zenodo.org/record/1718312)
@@ -21,15 +22,8 @@ MMseqs2 (Many-against-Many sequence searching) is a software suite to search and 
## 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).



## News

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



08/10/2018 ECCB18 tutorial of MMseqs2 is available [here](https://github.com/soedinglab/metaG-ECCB18-partII).



07/07/2018 Linclust has just been published at [Nature Communications](https://www.nature.com/articles/s41467-018-04964-5).



17/10/2017 MMseqs2 has just been published at [Nature Biotechnology](https://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3988.html).



## 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).
@@ -39,10 +33,10 @@ MMseqs2 can be used by compiling from source, downloading a statically compiled 
     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

     # 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).
@@ -55,73 +49,72 @@ MMseqs2 comes with a bash command and parameter auto completion, which can be ac 
        fi

</pre>

         

### 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.

### 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

        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:

: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:



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

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

## 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).



        mmseqs createdb examples/DB.fasta targetDB

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

### 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         

        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     

                

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.

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.

                

        mmseqs createdb examples/QUERY.fasta queryDB

        mmseqs createdb examples/DB.fasta targetDB

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.



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. 

### Search

         

        mmseqs createindex targetDB tmp

The `easy-search` searches directly with a FASTA/FASTQ files against either another FASTA/FASTQ file or an already existing MMseqs2 database.

        

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

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

 

To run the search execute:

It is also possible to pre-compute the index for the target database:



        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](https://github.com/soedinglab/mmseqs2/wiki#set-sensitivity--s-parameter)). 

        mmseqs createdb examples/DB.fasta targetDB

        mmseqs createindex targetDB tmp

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

        

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.

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).



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](https://github.com/soedinglab/mmseqs2/wiki#prefiltering-module).

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).



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

### 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 convertalis queryDB targetDB resultDB resultDB.m8

        mmseqs createdb examples/DB.fasta targetDB

        mmseqs createtaxdb targetDB tmp

        mmseqs createindex targetDB tmp

        mmseqs easy-taxonomy examples/QUERY.fasta targetDB alnRes tmp



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](https://github.com/soedinglab/mmseqs2/wiki#custom-alignment-format-with-convertalis) in the `convertalis` module. Make sure that you used the option `-a` during the search (`mmseqs search ... -a`).

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.



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

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.



### Other search modes

### Supported search modes



MMseqs2 provides many additional search modes:

 * Iterative sequences-profile searches (like PSI-BLAST) with the `--num-iterations` parameter
@@ -129,58 +122,25 @@ MMseqs2 provides many additional search modes: 
 * [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)

 * 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.



## 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](https://github.com/soedinglab/MMseqs2/wiki#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](https://github.com/soedinglab/mmseqs2/wiki#clustering-format).



### 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.

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`.

   

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

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



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

appveyor.yml

deleted100644 → 0
+0 −38

File deleted.

Preview size limit exceeded, changes collapsed.