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MMseqs2

imported

software/mmseqs2

MMseqs2: ultra fast and sensitive search and clustering suite

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
active
Maturity
deployed
Organization
soedinglab
Country
unknown
Homepage
mmseqs.com
Repository
github.com/soedinglab/MMseqs2
Documentation
unknown
Tags
alignment · bioinformatics · blast · linclust · metagenomics · mmseqs · profile-search · sequence-clustering
Regulatory
unknown
built by · 6

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • musclebioinformatics · sequence-clustering

    Multiple sequence and structure alignment with top benchmark scores scalable to thousands of sequences. Generates replicate alignments, enabling assessment of downstream analyses such as trees and…

  • metagraphalignment · metagenomics

    Scalable annotated de Bruijn graphs for DNA indexing, alignment, and assembly

  • jcvibioinformatics · blast

    Python library to facilitate genome assembly, annotation, and comparative genomics

  • sequenceserverbioinformatics · blast

    Intuitive graphical web interface for running BLAST bioinformatics tool (i.e. have your own custom NCBI BLAST site!)

  • anviobioinformatics · metagenomics

    An analysis and visualization platform for 'omics data

  • dada2bioinformatics · metagenomics

    Accurate sample inference from amplicon data with single nucleotide resolution

sources
  1. api.github.com/repos/soedinglab/MMseqs2
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-07-25, 2122 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/23.json→ .entries["mmseqs2"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.