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ClinVar-STXBP1-NLP-Dataset-Pathogenic

Tasks:
Text Classification
Question Answering
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Text
Formats:
text
Languages:
English
Size:
100K - 1M
Tags:
stxbp1
clinvar
genomics
biomedical
variant
rare-disease
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metadata
license: pddl
task_categories:
  - text-classification
  - question-answering
  - text2text-generation
language:
  - en
tags:
  - stxbp1
  - clinvar
  - genomics
  - biomedical
  - variant
  - rare-disease
  - neurology
  - epilepsy
  - nlp
  - llm
  - question-answering
  - text-classification
  - bioinformatics
  - snare
  - gene-editing
  - crispr
  - cas9
  - open-data
  - instruction-tuning
pretty_name: STXBP1 ClinVar Pathogenic Variants (Curated)
size_categories:
  - 100K<n<1M

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.