README.md

---
license: cc-by-4.0
task_categories:
  - fill-mask
  - feature-extraction
tags:
  - protein
  - proteins
  - antibody
  - immunology
  - biology
  - bioinformatics
  - protein-language-model
  - fasta
  - sequence-clustering
pretty_name: OAS (Observed Antibody Space, Paired) — FLAIR Preprocessed
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train/*.parquet"
---

# Dataset Card for `flair-bio/oas`

## Dataset Summary

This dataset is a cleaned and quality-scored version of the **paired heavy/light chain subset of
the Observed Antibody Space (OAS)** database (Oxford Protein Informatics Group), a large
repository of Next-Generation Sequencing (NGS) antibody repertoires. It has been reprocessed by
the [FLAIR](https://github.com/flair-bio/amplify) `modules/data` pipeline into a single
training-ready Parquet dataset (sharded — one shard per source study/run), with per-sequence
redundancy reduction (MMseqs2 cascaded clustering) and quality scores (RED — Residue Embedding
Diversity) added, for use in pretraining antibody/protein language models.

- **Curated by:** FLAIR (Mila / flair-bio)
- **Source data:** [Observed Antibody Space (OAS), paired sequences](https://opig.stats.ox.ac.uk/webapps/oas/) (Oxford Protein Informatics Group)
- **Language(s):** Not applicable (protein sequences; header/metadata text is English)
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) for the FLAIR-processed
  release, consistent with the original OAS terms; please review [OAS's terms of use](https://opig.stats.ox.ac.uk/webapps/oas/) for the underlying source data and constituent studies.

## Dataset Details

### Source Data & Provenance

Paired heavy/light antibody repertoire CSVs are downloaded directly from OPIG's OAS web service,
covering multiple published studies (e.g. Alsoiussi 2020, Eccles 2020, Goldstein 2019, Jaffe
2022), one gzipped CSV per sequencing run:

```
https://opig.stats.ox.ac.uk/webapps/ngsdb/paired/<Study_Year>/csv/<Run_ID>_paired.csv.gz
```

Each file is downloaded with `aria2c` (16-way split per file, single concurrent download). OAS is
a continuously growing database; the studies/runs included here were fetched in **August 2026**,
reflecting the most recent OAS snapshot available at that time — later OAS pulls may include
additional studies not present in this release.

### Processing Pipeline

Processed by the shared FLAIR data pipeline
([`modules/data`](https://github.com/flair-bio/amplify/tree/main/modules/data)), with an
OAS-specific preprocessing path:

```
Download → Preprocess → Cluster → Score → Assemble → Upload
```

1. **Preprocess** — unlike the FASTA-based datasets, OAS is read directly from the paired CSVs
   with pandas. Rows where ANARCI flags either chain as `"Shorter"` are dropped. Each retained
   pair is written as a FASTA record (`heavy_seq` + `XXXXX` linker + `light_seq`, used for
   statistics only) and, since the source already provides structured columns, the base Parquet
   shard is written **directly from the CSV** (one shard per source run), bypassing the generic
   FASTA→Parquet conversion. Each pair is assigned a unique, collision-free ID of the form
   `OAS_000000000000` (a dataset prefix followed by a zero-padded 12-digit counter).
2. **Cluster** — cascaded MMseqs2 (`easy-linclust`) redundancy reduction at identity thresholds
   `[0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.9, 0.85, 0.8]` (coverage threshold
   `0.95`) — notably finer-grained and higher-identity than the other FLAIR datasets, reflecting
   the naturally lower diversity/higher similarity of paired antibody sequences.
3. **Score** — each paired sequence is scored with the **RED** (Residue Embedding Diversity)
   metric using `flair-bio/amplify-350m` as the embedding model.
4. **Assemble** — base sequences, cluster assignments, and RED scores are joined on the shared
   `sequence_id` into the final sharded Parquet dataset published here (one output shard per
   source run).

### Dataset Structure

One `train` split, stored as multiple Parquet shards (`train/*.parquet`, one shard per source
sequencing run).

| Column | Type | Description |
|---|---|---|
| `sequence_id` | string | Unique FLAIR-assigned ID (`OAS_{12-digit counter}`) |
| `original_id` | string | Original OAS pair identifier |
| `description` | string | Remaining original header text |
| `sequence` | string | Paired sequence: `heavy_seq` + `XXXXX` linker + `light_seq` |
| `cluster_rep_at_99` … `cluster_rep_at_80` | string | Cluster representative ID at each identity threshold (99%, 98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 85%, 80%) |
| `dataset_id` | string | Source dataset identifier stamp (`oas`) |
| `sequence_length` | uint32 | Combined paired sequence length in residues (including linker) |
| `red_score` | float16 | RED (Residue Embedding Diversity) quality score |

### Data Statistics

Computed over the full assembled dataset:

| Statistic | Value |
|---|---|
| Number of sequences (pairs) | 1,887,487 |
| Sequence length — min / mean / median / max | 212 / 232.6 / 232.0 / 277 |
| Sequence length — std | 4.54 |
| Total residues (sum of lengths) | 439,101,472 |
| RED score — mean / median / std | 0.0858 / 0.0825 / 0.0287 |
| RED score — min / max | 0.0327 / 0.734 |
| RED score — p1 / p5 / p25 / p75 / p95 / p99 | 0.0600 / 0.0666 / 0.0756 / 0.0901 / 0.1065 / 0.1704 |
| Ambiguous residues (count) | 0 |
| Sequences containing ambiguous residues | 0 (0.0%) |
| Cluster count @ `cluster_rep_at_80` / `_at_90` / `_at_99` | 1,117,924 / 1,559,067 / 1,787,995 |

## Uses

Intended for self-supervised pretraining (e.g., masked language modeling) and representation
learning of antibody/protein language models, particularly modeling heavy/light chain co-
occurrence. The `cluster_rep_at_*` columns allow downstream users to subsample at a desired
redundancy level, and `red_score` can be used to filter or weight sequences by estimated
informational diversity.

## Bias, Risks, and Limitations

- OAS aggregates repertoires from multiple studies/donors/species; sampling depth, immunization
  status, and health/disease state vary by source study and are not exhaustively normalized here.
- The `XXXXX` linker is an artificial separator introduced for sequence-level modeling and stats
  computation; it is not a biological sequence.
- 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.
- Antibody sequences are naturally low-diversity relative to general proteomes; even at the
  finest clustering threshold (99%), many near-identical sequences may remain.

## Citation

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

```bibtex
@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}
}
```

```bibtex
@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}
}
```

```bibtex
@article{oas,
  title   = {Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences},
  author  = {Olsen, Tobias H. and Boyles, Fergus and Deane, Charlotte M.},
  journal = {Protein Science},
  year    = {2022},
  doi     = {10.1002/pro.4205}
}
```

```bibtex
@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}},
}
```