Initial release: coding + non_coding ClinVar VEP subsets

README.md

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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 — coding + non-coding benchmark
+
+A ClinVar variant-effect-prediction (VEP) benchmark with two complementary subsets — **coding** (39,473 variants) and **non-coding** (15,258 variants) — for evaluating DNA language models on clinical variant pathogenicity.
+
+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)). After annotating this dataset with ClinVar molecular consequence / region information, we found that it is overwhelmingly coding: among matched variants, **39,473 are coding and only 1,503 are non-coding**, with only **10 pathogenic non-coding variants** in the entire non-coding split — far too few and far too imbalanced for meaningful AUROC. We therefore keep this dataset as our coding ClinVar benchmark.
+
+The coding parquet preserves all upstream baseline scores (CADD, phyloP-100v, phyloP-241m, phastCons-100v, GPN-MSA, NT, NT-v2, HyenaDNA, Evo2-1B-Base, Evo2-7B-Base, Evo2-7B, GENERator-1B/3B, GENERator-v2-1B/3B) along with `chrom`, `pos`, `ref`, `alt`, `label`, `type`, `refseq_id`.
+
+## 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). Splice (250) and unknown-region variants (12,200) are kept separate from the main non-coding subset because they represent distinct or ambiguous categories, and live in the source repository [`hf-carbon/ClinVar-VEP`](https://huggingface.co/datasets/hf-carbon/ClinVar-VEP).
+
+## Citation
+
+If you use this benchmark, please cite GPN-MSA (Benegas et al., 2025) and the ClinVar database.

coding/clinvar_coding.parquet

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non_coding/clinvar_non_coding.parquet

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