# soedinglab/MMseqs2

MMseqs2: ultra fast and sensitive search and clustering suite

Repository: https://github.com/soedinglab/MMseqs2
Canonical: https://ross.abutalabs.com/products/mmseqs2
Homepage: https://mmseqs.com
Language: C
License: MIT
License Family: permissive
Topics: bioinformatics, sequence-clustering, profile-search, sequence-search, linclust, mmseqs, metagenomics, alignment, blast, taxonomy
Last push: 2026-08-24T12:05:35+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 16, longevity 100
- inputs: {"age_days": 3696, "days_push": 9, "days_rel": 402, "gap_med": 121.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2131, forks 299 (observed 2026-08-28T04:06:17.222420+00:00)

## What it is
MMseqs2 is an open-source C++ suite for ultra-fast, sensitive search and clustering of huge protein and nucleotide sequence sets, running orders of magnitude faster than BLAST with comparable sensitivity. It supports profile searches, taxonomic assignment of metagenomic contigs, and GPU-accelerated homology search across multi-core and server environments.

## Use cases
- search large protein sequence databases faster than BLAST
- cluster millions of protein sequences in linear time
- run PSI-BLAST-like profile searches at high speed
- assign taxonomy to metagenomic contigs
- cluster nucleotide sequence sets with linclust
- run interactive local sequence searches via a web server app
- accelerate homology search on GPUs

## When to choose
- you need to search or cluster very large sequence datasets where BLAST is too slow
- you want near-BLAST sensitivity at a fraction of the runtime
- you need scalable multi-core or GPU-accelerated sequence search
- you are doing metagenomic analysis or massive protein set clustering

## When to avoid
- you only need to search a handful of sequences and prefer BLAST's familiar output
- you need pairwise alignment scoring details beyond MMseqs2's approximations
- you require a Windows-native build without cygwin
- you need structural or RNA-specific alignment tools instead of sequence search

## Facets
- artifact type: application
- maturity: active
- function: search-engine, cli, gpu-computing
- domain: bioinformatics, big-data
- platform: windows, cpp, cli
- tags: sequence-search, sequence-clustering, blast-alternative, protein-sequences, nucleotide-sequences, profile-search, taxonomy-assignment, metagenomics, high-performance-computing, search, linux, macos, gpu, docker

## Member repositories
- soedinglab/MMseqs2 (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:17.222420+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:52:12.397498+00:00, confidence not recorded.
  - readme: https://github.com/soedinglab/MMseqs2 (fetched 2026-08-28T04:06:17.222420+00:00, sha c11400c58447)
  - homepage: https://mmseqs.com (fetched 2026-08-29T10:32:11.016562+00:00, sha 956989abe4ae)
- Data as of 2026-08-30T08:39:29.467469+00:00.