Datasets:

flair-bio
/

mgnify

license: cc-by-4.0
task_categories:
  - fill-mask
  - feature-extraction
tags:
  - protein
  - proteins
  - biology
  - bioinformatics
  - protein-language-model
  - metagenomics
  - fasta
  - sequence-clustering
pretty_name: MGnify — FLAIR Preprocessed
size_categories:
  - 1B<n<10B
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/*.parquet

Dataset Card for `flair-bio/mgnify`

Dataset Summary

This dataset is a cleaned, deduplication-clustered, and quality-scored version of the MGnify peptide database (EBI Metagenomics), a large collection of predicted protein sequences derived from environmental metagenomic and metatranscriptomic assemblies. It has been reprocessed by the FLAIR modules/data pipeline into a single training-ready Parquet dataset (sharded), with per-sequence redundancy reduction (MMseqs2 cascaded clustering) and quality scores (RED — Residue Embedding Diversity) added, for use in pretraining protein language models (pLMs).

Curated by: FLAIR (Mila / flair-bio)
Source data: MGnify peptide database, 2024_04 release (EMBL-EBI)
Language(s): Not applicable (protein sequences; FASTA header metadata is English free-text)
License: CC BY 4.0 for the FLAIR-processed release. The original MGnify data is made available by EMBL-EBI under open terms (EBI Terms of Use); please review those terms for the raw source data.

Dataset Details

Source Data & Provenance

The MGnify peptide database (2024_04 release) is distributed as 25 gzipped FASTA parts:

https://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2024_04/mgy_proteins_{1..25}.fa.gz

Each part is downloaded with aria2c (2-way split per file, up to 3 files concurrently). Files were fetched in August 2026; 2024_04 was the most recent dated release published upstream at that time.

Processing Pipeline

Processed by the shared FLAIR data pipeline (modules/data):

Download → Preprocess → Cluster → Score → Assemble → Upload

Preprocess — the 25 FASTA parts are decompressed, linearized, and concatenated into a monolithic FASTA, split into 1,000,000-sequence chunks, and converted to Parquet shards in parallel. Each sequence is assigned a unique, collision-free ID of the form MGNIFY_000000000000 (a dataset prefix followed by a zero-padded 12-digit counter), with the original FASTA header preserved as description.
Cluster — cascaded MMseqs2 (easy-linclust) redundancy reduction at identity thresholds [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3] (coverage threshold 0.8), producing one cluster- representative column per threshold.
Score — each sequence is scored with the RED (Residue Embedding Diversity) metric using flair-bio/amplify-350m as the embedding model — a measure of how distinct/informative a sequence's contextual residue embeddings are.
Assemble — base sequences, cluster assignments, and RED scores are joined on the shared sequence_id into the final sharded Parquet dataset published here.

Dataset Structure

One train split, stored as multiple Parquet shards (train/*.parquet).

Column	Type	Description
`sequence_id`	string	Unique FLAIR-assigned ID (`MGNIFY_{12-digit counter}`)
`original_id`	string	Original MGnify sequence identifier
`description`	string	Remaining original FASTA header text
`sequence`	string	Protein sequence (amino acid string)
`cluster_rep_at_90` … `cluster_rep_at_30`	string	Cluster representative ID at each identity threshold (90%, 80%, 70%, 60%, 50%, 40%, 30%)
`dataset_id`	string	Source dataset identifier stamp (`mgnify`)
`sequence_length`	uint32	Sequence length in residues
`red_score`	float16	RED (Residue Embedding Diversity) quality score

Data Statistics

Computed over the full assembled dataset:

Statistic	Value
Number of sequences	2,444,174,488
Sequence length — min / mean / median / max	20 / 189.7 / 162.0 / 57,795
Sequence length — std	159.79
Total residues (sum of lengths)	4,183,014,619
RED score — mean / median / std	0.0194 / 0.0156 / 0.0202
RED score — min / max	3.25e-04 / 0.937
RED score — p1 / p5 / p25 / p75 / p95 / p99	0.0029 / 0.0038 / 0.0069 / 0.0261 / 0.0461 / 0.0816
Ambiguous residues (count)	166,248,760
Sequences containing ambiguous residues	34,231,621 (4.0%)
Cluster count @ `cluster_rep_at_30` / `_at_60` / `_at_90`	240,163,979 / 326,446,084 / 658,241,220

Uses

Intended for self-supervised pretraining (e.g., masked language modeling) and representation learning of protein language models. The cluster_rep_at_* columns allow downstream users to subsample at a desired redundancy level (e.g., dedup at 30% identity for maximum diversity, or 90% for near-duplicate-only removal), and red_score can be used to filter or weight sequences by estimated informational diversity.

Bias, Risks, and Limitations

MGnify is a metagenomic database of environmental samples; sequence provenance, quality, and functional annotation completeness vary widely and largely lack experimental validation.
RED scores are a heuristic computed from a specific pLM's embeddings (flair-bio/amplify-350m) and should not be interpreted as a ground-truth quality or functional label.
Clustering provides an approximate indication of protein relatedness. These datasets use MMseqs2 Linclust, and resulting clusters can vary depending on the clustering algorithm and its parameters.
Clustering thresholds reduce but do not eliminate redundancy; sequences sharing a cluster representative at coarse thresholds (e.g., 30%) may still be highly similar within.

Citation

If you use this dataset, please cite the original MGnify source, MMseqs2, RED, and FLAIR:

@article{steinegger2017mmseqs2,
  title   = {MMseqs2 enables sensitive protein sequence searching for the analysis of massive sequence datasets},
  author  = {Steinegger, Martin and S{\"o}ding, Johannes},
  journal = {Nature Biotechnology},
  volume  = {35},
  pages   = {1026--1028},
  year    = {2017},
  doi     = {10.1038/nbt.3988}
}

@article{lebreton2026plm,
  title   = {pLM representations unlock metagenomic space beyond homology},
  author  = {Le Breton, Lola and Heurtel-Depeiges, David and Millar, Douglas C. and Zetzsche, Lara E. and Vernon, Robert M. and Langmead, Christopher James and Chandar, Sarath and Fournier, Quentin},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.07.28.739874}
}

@article{richardson2023mgnify,
  title={MGnify: the microbiome sequence data analysis resource in 2023},
  author={Richardson, Lorna and Allen, Ben and Baldi, Germana and Beracochea, Martin and Bileschi, Maxwell L and Burdett, Tony and Burgin, Josephine and Caballero-P{\'e}rez, Juan and Cochrane, Guy and Colwell, Lucy J and others},
  journal={Nucleic acids research},
  volume={51},
  number={D1},
  pages={D753--D759},
  year={2023},
  publisher={Oxford University Press}
}

@misc{flair-plm,
  title        = {FLAIR: Protein Language Model Pretraining Data Pipeline},
  author       = {{Applied Machine Learning Research Team (AMLRT) collaborators at Mila and FLAIR-bio research students}},
  howpublished = {\url{https://github.com/flair-bio/amplify/tree/main/modules/data}},
}

Dataset Card for flair-bio/mgnify