README.md

---
license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - dna
  - genomics
  - variant-effect-prediction
  - clinvar
size_categories:
  - 10K<n<100K
configs:
  - config_name: coding
    data_files:
      - split: test
        path: coding/*.parquet
  - config_name: non_coding
    data_files:
      - split: test
        path: non_coding/*.parquet
---

# ClinVar VEP

A ClinVar variant-effect-prediction (VEP) benchmark, for evaluating DNA language models on clinical variant pathogenicity, with two subsets: **coding** (39,473 variants) and **non-coding** (15,258 variants).

Each split is a balanced binary classification task: `label = 1` for pathogenic / likely pathogenic and `label = 0` for benign / likely benign.

## Subsets

| Config | # variants | benign | pathogenic | Region scope |
|---|---:|---:|---:|---|
| `coding` | 39,473 | 17,231 | 22,242 | exonic protein-coding |
| `non_coding` | 15,258 | 7,629 | 7,629 | intronic + 5′/3′ UTR |

```python
from datasets import load_dataset
coding     = load_dataset("hf-carbon/clinvar-vep-final", "coding",     split="test")
non_coding = load_dataset("hf-carbon/clinvar-vep-final", "non_coding", split="test")
```

## Coding subset — origin

For the coding ClinVar benchmark, we use the ClinVar VEP subset from [`GenerTeam/variant-effect-prediction`](https://huggingface.co/datasets/GenerTeam/variant-effect-prediction), which was originally used in the GPN-MSA study ([Benegas et al., 2025](https://www.nature.com/articles/s41587-024-02511-w)). However, wee found that it is largely coding:  **39,473 are coding and 1,503 are non-coding**. The non-coding subset is also highly label-imbalanced, with **10 pathogenic non-coding variants**. We therefore use this dataset as our coding ClinVar benchmark and construct a separate balanced non-coding subset from ClinVar.


## Non-coding subset — construction

To construct a separate non-coding ClinVar benchmark, we start from the original ClinVar VCF and restrict to single-nucleotide variants on chromosomes 1–22, X, and Y with binary clinical labels, mapping benign / likely benign to `label = 0` and pathogenic / likely pathogenic to `label = 1`. We retain reviewed variants, annotate each variant using ClinVar consequence terms into broad region classes (`coding`, `non_coding`, `splice`, `unknown`) and finer subtypes (`intronic`, `utr_5_prime`, `utr_3_prime`, etc.), and then select the clean non-coding subset with `coding_status == "non_coding"`.

This yields a **balanced non-coding benchmark of 15,258 variants: 7,629 benign and 7,629 pathogenic**, covering intronic (10,310) and UTR variants (4,948 total — 4,174 5′UTR + 774 3′UTR).