--- 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/csv/_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}}, } ```