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

1. 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.
2. 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.
3. 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.
4. 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}},
}