SkyWhal3/ClinVar-STXBP1-NLP-Dataset

language:

• en

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stxbp1_clinvar_curated

_ Curated STXBP1 and related variant records from ClinVar (24Million), ready for LLM and biomedical NLP applications._

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

Dataset Overview

A curated, LLM-friendly dataset of STXBP1 and related variant records from ClinVar, converted from ClinVar VCF and annotated for clinical, research, rare disease, and advanced AI applications.
This resource is suitable for medical language modeling, rare disease NLP, variant curation, and biomedical Q&A.

Formats included:

• Structured JSONL (main split)
• Q/A pairs (txt, 7,013,256 examples, 1.66 GB)
• Curated summaries (txt, 24,548,655 examples, 1.51 GB)
• Parquet conversion is recommended for large-scale use

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Curation Criteria

Variants were selected from ClinVar using the following inclusion keywords (case-insensitive):

• STXBP1
• MUNC18
• STXBP2
• STXBP3
• STXBP4
• STXBP5
• STXBP6
• syntaxin.binding
• CRISPR Cas9
• CRISPR Cas12
• encephalopathy
• SNARE

Any variant record containing one or more of these keywords (in gene symbols, molecular consequence, disease name, or database annotations) was included. This ensures comprehensive coverage of the STXBP gene family, SNARE-complex biology, CRISPR-mediated editing, and associated neurological disorders (notably epileptic encephalopathies).

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Features

• Natural language clinical summaries for each variant
• Structured JSONL (parquet-compatible) for easy data science/NLP use
• Ready-to-use Q/A pairs for instruction and LLM fine-tuning
• Full field coverage: variant position, gene, disease, clinical significance, HGVS description, database cross-links, review status, and more

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Dataset Statistics

Format	Size (bytes)	Number of Examples/Lines
QA (.txt)	1,664,122,880	7,013,256
Curated summaries	1,511,264,256	24,548,655
JSONL	1,598,603,264	3,506,628

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

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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, Benign, Uncertain)
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)

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Data Examples

JSON record:

{
  "ID": "3385321",
  "chrom": "1",
  "pos": "66926",
  "ref": "AG",
  "alt": "A",
  "gene": "OR4F5",
  "disease": "Retinitis_pigmentosa",
  "significance": "Uncertain_significance",
  "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,OMIM:PS268000,Orphanet:791",
  "clndnincl": null,
  "clndisdbincl": null,
  "onc_fields": {},
  "sci_fields": {},
  "incl_fields": {}
}

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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 (Parquet, recommended)

# This will automatically use the Parquet shards
ds = load_dataset("YOURPATH/ClinVar-STXBP1-NLP-Dataset")

# Access examples
print(ds["train"][0])

To force JSONL loading (if you prefer the original format):

# Specify data_files to point to JSONL file(s)
ds = load_dataset(
    "YOURPATH/ClinVar-STXBP1-NLP-Dataset",
    data_files="ClinVar-STXBP1-NLP-Dataset.jsonl",
    split="train"
)
print(ds[0])

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

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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.