SkyWhal3/ClinVar-STXBP1-NLP-Dataset-Pathogenic

stxbp1_clinvar_curated_pathogenic

Curated set of 307,587 pathogenic and likely pathogenic STXBP1 and related variants from ClinVar, ready for LLM, variant curation, and biomedical NLP applications.

(Updated Jun 10th 2025. - Fields containing {null} or {} were removed.) <<<

Dataset Overview

A hand-curated, LLM-friendly dataset of 307,587 STXBP1 and family variants from ClinVar, filtered for clinical significance (Pathogenic, Likely_pathogenic).
Ideal for medical language modeling, rare disease NLP, AI-powered variant curation, and biomedical Q&A.

Formats included:

• Structured JSONL (.jsonl, main split)
• Q/A pairs (.txt, for demo/fine-tuning)
• Parquet conversion recommended for large-scale use

---

Curation Criteria

Variants included here are:

• Annotated as Pathogenic or Likely_pathogenic in ClinVar
• Matching gene family:
  • STXBP1, MUNC18, STXBP2, STXBP3, STXBP4, STXBP5, STXBP6
  • Related SNARE-complex/CRISPR/neurological disorder keywords

---

Features

• Natural language clinical summaries for each variant
• Structured JSONL (parquet-compatible) for data science and NLP
• Q/A pairs for LLM training and evaluation
• Full coverage: variant, gene, disease, clinical significance, HGVS, database links, review status, and more

---

Dataset Statistics

Format	Size (bytes)	Number of Examples/Lines
QA (.txt)	163,561,472	615,174
JSONL	157,364,224	307,587

Main split for Hugging Face: JSONL format (see above for statistics).

---

Schema

Field	Description
ID	ClinVar Variation ID
chrom	Chromosome
pos	Genomic position (GRCh38)
ref	Reference allele
alt	Alternate allele
gene	Gene symbol
disease	Disease/phenotype name
significance	Clinical significance (e.g., Pathogenic, Likely_pathogenic)
hgvs	HGVS variant description
review	ClinVar review status
molecular_consequence	Sequence Ontology + effect
variant_type	SNV, Insertion, Deletion, etc.
clndisdb	Disease database links (OMIM, MedGen, etc.)
clndnincl	Included variant disease name
clndisdbincl	Included variant disease database links
onc_fields	Dict of oncogenicity fields
sci_fields	Dict of somatic clinical impact fields
incl_fields	Dict of included fields (INCL)

---

Data Example

JSON record:

{
  "ID": "3385321",
  "chrom": "1",
  "pos": "66926",
  "ref": "AG",
  "alt": "A",
  "gene": "STXBP1",
  "disease": "Developmental and epileptic encephalopathy, 4",
  "significance": "Pathogenic",
  "hgvs": "NC_000001.11:g.66927del",
  "review": "criteria_provided, single_submitter",
  "molecular_consequence": "SO:0001627: intron_variant",
  "variant_type": "Deletion",
  "clndisdb": "Human_Phenotype_Ontology:HP:0000547,MONDO:MONDO:0019200,MeSH:D012174,MedGen:C0035334,OMIM:268000",
  "clndnincl": null,
  "clndisdbincl": null,
  "onc_fields": {},
  "sci_fields": {},
  "incl_fields": {}
}

===================================================================================================================

You can easily load this dataset using the 🤗 Datasets library.

The Hugging Face infrastructure will automatically use the efficient Parquet files by default, but you can also specify the JSONL if you prefer.

Install dependencies (if needed):

pip install datasets

Load the full dataset (JSONL, recommended)

ds = load_dataset("SkyWhal3/ClinVar-STXBP1-NLP-Dataset", data_files="ClinVar-STXBP1-NLP-Dataset.jsonl", split="train")
print(ds[0])

Parquet conversion (for large scale)

df = pd.read_json("ClinVar-STXBP1-NLP-Dataset.jsonl", lines=True)
df.to_parquet("ClinVar-STXBP1-NLP-Dataset.parquet")

Other ways to use the data

Load all Parquet shards with pandas

import glob

# Load all Parquet shards in the train directory
parquet_files = glob.glob("default/train/*.parquet")
df = pd.concat([pd.read_parquet(pq) for pq in parquet_files], ignore_index=True)
print(df.shape)
print(df.head())

Filter for a gene (e.g., STXBP1)

stxbp1_df = df[df["gene"] == "STXBP1"]
print(stxbp1_df.head())

Randomly sample a subset

print(sample)

Load with Polars (for high performance)

df = pl.read_parquet("default/train/0000.parquet")
print(df.head())

Query with DuckDB (SQL-style)

con = duckdb.connect()
df = con.execute("SELECT * FROM 'default/train/0000.parquet' WHERE gene='STXBP1' LIMIT 5").df()
print(df)

Streaming mode with 🤗 Datasets

for record in ds.take(5):
    print(record)

Created by Adam Freygang, A.K.A. SkyWhal3

---

License:
This dataset is licensed under the ODC Public Domain Dedication and License (PDDL).
To the extent possible under law, the author(s) have dedicated this data to the public domain worldwide by waiving all rights to the work under copyright law, including all related and neighboring rights, to the extent allowed by law.
NO WARRANTY is provided.
See ODC-PDDL for full legal text.