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model · method

MMseqs2

MMseqs2 is a toolkit for searching and clustering large protein and nucleotide sequence collections.

0 evaluations · 0 metric rows

At a glance

Explanatory profile: source reviewed · Automated source review, 2026-09-16. This does not change the review status of its results.

Inputs, outputs and configuration
PropertyDescription and evidence
Method classSequence search and clustering softwaresoedinglab/MMseqs2 official source · README.md: introduction and supported workflows
Known versionsNot extracted or verified for this record.
Training dataNot extracted or verified for this record.
Context limitsNot extracted or verified for this record.
AccessNot extracted or verified for this record.
Code licenceNot extracted or verified for this record.
Weights licenceNot extracted or verified for this record.

How it works

Conceptual procedure

Schematic of the documented input, computation and output; not an executable configuration.

Conceptual procedureQuery sequences. Then: Reference database. Then: Search or clustering. Then: Alignments or clustersQuery sequencesReference databaseSearch or clusteringAlignments or clusters
Read the diagram as text
  1. Query sequences
  2. Reference database
  3. Search or clustering
  4. Alignments or clusters
soedinglab/MMseqs2 official source · README.md: introduction and supported workflows

A query collection is searched against a specified sequence or profile database, or clustered using configured similarity criteria. The selected command and database define the operational method.

soedinglab/MMseqs2 official source · README.md: introduction and supported workflows

Benchmarks and results

Release 2026-09-16-d74d282221a9 · 0 evaluations · 0 metric rows. Different protocols are not a single leaderboard.

No evaluations linked in this release.

Strengths and limitations

Strengths supported by sources

  • Provides established sequence-similarity procedures that can act as task-specific comparators.soedinglab/MMseqs2 official source · README.md: introduction and supported workflows

Limitations and conditions

  • Database contents, coverage thresholds and sensitivity settings must be matched before interpreting comparisons with learned models.soedinglab/MMseqs2 official source · README.md: introduction and supported workflows
Profile review details

Primary project documentation or paper inspected for the explanatory claims and cited locations. Reviewed coverage concerns this narrative, not complete metadata, independent reproduction or a performance ranking.

Stable record: discovery-model-mmseqs2

Applicable tests and references

Applicability is distinct from a completed evaluation.

  • CAFA · Proposed association

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: discovered

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Technical metadata and extraction receipts

Stable ID: discovery-model-mmseqs2

areas
protein-function
access
official_source_linked
benchmark applicability
candidate; not evidence of a reported evaluation
candidate benchmark ids
discovery-benchmark-cafa
entity level
method
missing metadata
checkpoint: unextracted; code licence: unextracted; parameters: unextracted; training cutoff: unextracted; training data: unextracted; version: unextracted; weights licence: unextracted
reported name
MMseqs2
version
Not reported
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