Datasets:
File size: 2,699 Bytes
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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).
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