---
license: mit
pretty_name: BenchRep-T
size_categories:
  - 1K<n<10K
tags:
  - immunology
  - t-cell-receptor
  - tcr-beta
  - immune-repertoire
  - AIRR
  - immunoseq
  - repertoire-classification
configs:
  - config_name: Mal-ID
    data_files:
      - split: specimens
        path: immuneML/Mal-ID/metadata.csv
  - config_name: Savola_RA
    data_files:
      - split: specimens
        path: immuneML/immunoSEQ/Savola_RA/metadata.csv
  - config_name: Musvosvi_TB
    data_files:
      - split: specimens
        path: immuneML/immunoSEQ/Musvosvi_TB/metadata.csv
  - config_name: Rawat_T1D
    data_files:
      - split: specimens
        path: immuneML/immunoSEQ/Rawat_T1D/metadata.csv
  - config_name: Mitchell_T1D
    data_files:
      - split: specimens
        path: immuneML/immunoSEQ/Mitchell_T1D/metadata.csv
  - config_name: Emerson_CMV
    data_files:
      - split: specimens
        path: immuneML/immunoSEQ/Emerson_CMV/metadata.csv
---

# BenchRep-T

**BenchRep-T** is a benchmark for **TCRβ (T-cell receptor β-chain) immune-repertoire classification**.
It bundles the multi-disease **Mal-ID** cohort together with five external disease cohorts
(sequenced on the Adaptive Biotechnologies **immunoSEQ** platform), providing per-specimen
repertoire files, harmonized sample metadata, and auxiliary files used for the
sequencing-depth *scaling-law* and *driver-sequence* experiments.

Each **specimen** (one sequenced repertoire) is one example; the classification target is the
specimen-level `disease` label.

## Dataset structure

```
BenchRep-T/
├── Mal-ID/
│   ├── metadata.tsv                             # 550 specimen annotations + labels
│   ├── repertoires/                             # 550 per-specimen TCRβ rearrangement tables (.tsv.gz)
│   ├── scaling_exp_depth_indices_max75k.json.gz # sampling indices for the depth scaling-law experiment
│   └── vdjdb_minervina_driver_seq_matches.csv   # repertoire ↔ public/driver-clone matches (driver-seq experiment)
├── immunoSEQ/
│   ├── Savola_RA/         # Rheumatoid Arthritis
│   │   ├── metadata.tsv
│   │   └── repertoires/   # 91 .tsv.gz
│   ├── Musvosvi_TB/       # Tuberculosis (progression)
│   │   ├── metadata.tsv
│   │   └── repertoires/   # 140 .tsv.gz
│   ├── Rawat_T1D/         # Type 1 Diabetes
│   │   ├── metadata.tsv
│   │   └── repertoires/   # 614 .tsv.gz
│   ├── Mitchell_T1D/      # Type 1 Diabetes
│   │   ├── metadata.tsv
│   │   └── repertoires/   # 196 .tsv.gz
│   └── Emerson_CMV/       # Cytomegalovirus exposure
│       ├── metadata.tsv
│       └── repertoires/   # 761 .tsv.gz
└── immuneML/              # immuneML-ready AIRR export of every cohort (see immuneML/README.md)
    ├── Mal-ID/, immunoSEQ/<cohort>/   # metadata.csv + AIRR repertoires/*.tsv.gz
    ├── tasks/<task>/                  # task metadata + fixed fold{0,1,2}_{train,test}.csv
    ├── specs/<task>.yaml              # runnable immuneML specifications
    └── README.md, MANIFEST.tsv, export_report.json
```

## Cohorts

| Group | Path | Classification task | Repertoires | Label composition | Repertoire format | Source study |
|---|---|---|---|---|---|---|
| Mal-ID | `Mal-ID/` | Multi-disease vs. Healthy/Background | 550 | Healthy/Background 197, HIV 98, Lupus 64, Covid19 58, Influenza 37, T1D 96 | AIRR-style rearrangement table | Zaslavsky et al. 2025 |
| Savola_RA | `immunoSEQ/Savola_RA/` | Rheumatoid Arthritis vs. Healthy | 91 | RA 71, Healthy 20 | immunoSEQ export | Savola et al. 2017 |
| Musvosvi_TB | `immunoSEQ/Musvosvi_TB/` | TB Progressor vs. Controller | 140 | Progressor 63, Controller 77 | immunoSEQ export | Musvosvi et al. 2023 |
| Rawat_T1D | `immunoSEQ/Rawat_T1D/` | Type 1 Diabetes vs. Control | 614 | T1D 426, Healthy/Background 188 | immunoSEQ export (reduced) | Rawat et al. 2026 |
| Mitchell_T1D | `immunoSEQ/Mitchell_T1D/` | Type 1 Diabetes vs. Healthy | 196 | T1D 171, Healthy/Background 25 | immunoSEQ export | Mitchell et al. 2022 |
| Emerson_CMV | `immunoSEQ/Emerson_CMV/` | CMV+ vs. Healthy/Background | 761 | CMV 340, Healthy/Background 421 | immunoSEQ export | Emerson et al. 2017 |

## Files

- **`repertoires/*.tsv.gz`** — one gzip-compressed, tab-separated file per specimen; each row is one TCRβ
  sequencing read (Mal-ID; identical reads are not collapsed) or one unique rearrangement with its count (immunoSEQ cohorts). The file stem is the specimen identifier (for the immunoSEQ cohorts it matches the
  `specimen_label`/`sample_name`/`filename` column in that cohort's `metadata.tsv`; Rawat_T1D file names carry a `_TCRB` suffix).
- **`metadata.tsv`** — one row per specimen with the classification label and per-cohort annotations
  (schemas differ across cohorts — see below).
- **`Mal-ID/scaling_exp_depth_indices_max75k.json.gz`** — precomputed read-sampling indices (up to 75k reads/specimen)
  defining the subsampled repertoires used in the sequencing-depth scaling-law experiment.
- **`Mal-ID/vdjdb_minervina_driver_seq_matches.csv`** — matches between repertoire clones and known
  public / antigen-specific "driver" TCRs (VDJdb + Minervina et al.), used for the driver-sequence experiment.
  Columns: `disease, sample_cdr3, sample_vgene, sample_jgene, public_clone_cdr3, public_clone_vgene, public_clone_jgene, similarity, score, filename`.

## Repertoire schemas

**Mal-ID** (AIRR-style, ~120 columns) — key fields: `sequence_id, repertoire_id, locus, v_call, d_call, j_call,
cdr3, cdr3_aa, junction, junction_aa, productive, participant_label, specimen_time_point`, plus full
nucleotide alignment / insertion-deletion columns.

**immunoSEQ cohorts** (Savola_RA, Musvosvi_TB, Mitchell_T1D; Adaptive immunoSEQ export, ~54 columns) — key fields:
`nucleotide, cdr3_aa, count (templates/reads), frequencyCount (%), v_call, j_call, vGeneName, jGeneName,
sequenceStatus, estimatedNumberGenomes, sequence, num_reads, repertoire_id, participant_label`.

**Rawat_T1D** (reduced immunoSEQ-derived, 17 columns): `cdr3_aa, count (templates/reads), frequency, nucleotide,
v_call, d_gene, j_call, sequenceStatus, v_family, d_family, j_family, v_resolved, d_resolved, j_resolved,
sequence, num_reads`.

**Emerson_CMV** (minimal, 7 columns): `cdr3_aa, v_call, j_call, sequence, num_reads, repertoire_id, participant_label`.

## Metadata schemas

All `metadata.tsv` files share `participant_label`, `specimen_label`, and `disease` (the classification target).
Cross-validation fold assignments are provided per cohort (`CV_fold` / `fold` /
`malid_cross_validation_fold_id_when_in_test_set`). Additional per-cohort columns:

- **Mal-ID / Mitchell_T1D**: `specimen_time_point, study_name, available_gene_loci, disease_subtype, age, sex, ancestry`.
- **Savola_RA / Musvosvi_TB**: immunoSEQ sample statistics (`total_templates, productive_templates,
  fraction_productive, productive_simpson_clonality, sample_tags, sku, test_name`, …).
- **Rawat_T1D**: subject clinical/HLA fields (`diabetes_status, sex, age, duration, HLA A/B/C/DPB1/DQB1/DRB1…,
  hla_high_risk_type, autoantibody statuses, ML_class`).
- **Emerson_CMV**: same base fields as Mal-ID/Mitchell_T1D (`specimen_time_point, study_name, available_gene_loci,
  disease_subtype, age, sex, ancestry`) plus `repertoire_file, emerson_subject_id, cohort, cohort_name, race,
  ethnicity, race_and_ethnicity, known_cmv_status, metadata_source`.

## Usage

```python
from huggingface_hub import snapshot_download
import pandas as pd, glob, os

# Download one cohort (metadata + repertoires)
local = snapshot_download(
    repo_id="lordim/BenchRep-T",
    repo_type="dataset",
    allow_patterns="immunoSEQ/Savola_RA/*",
)

meta = pd.read_csv(f"{local}/immunoSEQ/Savola_RA/metadata.tsv", sep="\t")
rep_files = glob.glob(f"{local}/immunoSEQ/Savola_RA/repertoires/*.tsv.gz")
rep = pd.read_csv(rep_files[0], sep="\t")   # pandas reads .gz transparently
```

## immuneML-ready AIRR export

`immuneML/` holds every cohort above in one AIRR Rearrangement schema that [immuneML](https://github.com/uio-bmi/immuneML) can import directly:

- **Repertoires:** the same specimens and rows as the native files, in 20 AIRR columns that include all 14 required fields. `cdr3_aa` is unchanged. `junction`/`junction_aa` are rebuilt from the nucleotide data where they translate back to `cdr3_aa`: 99.78% of Mal-ID rows, and more than 99.99% in every other cohort.
- **Metadata:** one comma-separated `metadata.csv` per cohort, with `subject_id` set to the specimen and BenchRep-T's fixed `fold`.
- **Tasks and specs:** for each of the 11 benchmark tasks, the task's specimens, the three fixed train/test fold splits, and a runnable immuneML specification.

Tested with immuneML 3.0.27; install it with `pandas<3`. [`immuneML/README.md`](immuneML/README.md) documents every conversion rule and caveat.

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="lordim/BenchRep-T", repo_type="dataset", local_dir="BenchRep-T",
    allow_patterns=["immuneML/immunoSEQ/Savola_RA/*", "immuneML/tasks/ra/*",
                    "immuneML/specs/ra.yaml"],
)
# then, inside BenchRep-T/:  immune-ml immuneML/specs/ra.yaml results/ra
```

## License

Released under the **MIT License**.

## References

Source studies for each cohort:

- **Mal-ID** — Zaslavsky, Maxim E., et al. "Disease diagnostics using machine learning of B cell and T cell receptor sequences." *Science* 387.6736 (2025): eadp2407.
- **Savola_RA** — Savola, Paula, et al. "Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis." *Nature Communications* 8.1 (2017): 15869.
- **Musvosvi_TB** — Musvosvi, Munyaradzi, et al. "T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection." *Nature Medicine* 29.1 (2023): 258-269.
- **Rawat_T1D** — Rawat, Puneet, et al. "Identification of a type 1 diabetes–associated T cell receptor repertoire signature from the human peripheral blood." *Science Advances* 12.7 (2026): eadx7448.
- **Mitchell_T1D** — Mitchell, Angela M., et al. "Temporal development of T cell receptor repertoires during childhood in health and disease." *JCI Insight* 7.18 (2022): e161885.
- **Emerson_CMV** — Emerson, Ryan O., et al. "Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire." *Nature Genetics* 49.5 (2017): 659-665.

## Citation

BenchRep-T is described in the following paper, to appear in the **NeurIPS 2026 Evaluations and Datasets Track**:

> Alejandro Buendia\*, Chiho Im\*, Anshul Kundaje, Scott D Boyd, and Liel Cohen-Lavi. "BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics." *Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track*, 2026.
>
> \* Equal contribution (co-first authors).

```bibtex
@inproceedings{buendia2026benchrep,
  title     = {BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics},
  author    = {Buendia, Alejandro and Im, Chiho and Kundaje, Anshul and Boyd, Scott D and Cohen-Lavi, Liel},
  booktitle = {Advances in Neural Information Processing Systems, Evaluations and Datasets Track},
  year      = {2026}
}
```

Code: [github.com/boyd-lab/BenchRep-T](https://github.com/boyd-lab/BenchRep-T). A preprint is available on [bioRxiv](https://www.biorxiv.org/content/10.64898/2026.06.09.727013v1.abstract).