README.md

metadata

tags:
  - biology
  - medical
  - genomics
  - exome
pretty_name: ExomeBench
size_categories:
  - 100K<n<1M
license: cc
task_categories:
  - text-classification

Exome Bench: A Benchmark for Clinical Variant Interpretation in Exome Regions 🧬

Paper | GitHub

The ExomeBench dataset is derived from ClinVar, a publicly accessible database maintained by the National Center for Biotechnology Information (NCBI). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the Human Reference Genome (HRG).

This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. Exome-specific regions are critically important because they encompass all protein-coding regions of the genome, where disease-associated variants are most likely to occur. By focusing on exome-specific regions and using sequences from the Human Reference Genome, this dataset enables robust evaluation of models on clinically significant tasks.

Dataset Details

Data Collection

1. Source: Variants are sourced from the ClinVar database.
2. Clinical Significance: ClinVar provides detailed information on the clinical significance of each variant and its association with human diseases.

Data Filtering

1. Assertion Criteria: We include only variants with at least one submitter providing an interpretation and satisfying the assertion criteria for reliability.
2. Variant Type: Only single-nucleotide variants (SNVs) are selected.
3. Exome-Specific Regions: Filter the variants to include only those located in exome-specific regions.

Sequence Generation

1. Human Reference Genome (HRG): For each variant, generate input sequences from the HRG using genome.
2. Sequence Length: The length of the sequences is a parameter, typically set to 100 base pairs (bp).
3. Variant Positioning: The variant is centered within the sequence, which is read in from a FASTA file.

Dataset Format

Each dataset entry consists of two main fields:

• sequence (str): A DNA sequence centered around the variant.
• label (int): Task-specific integer-encoded class index.

Tasks

ExomeBench includes five, each framed as a classification problem:

1. Pathohenic Variant Prediction (PV)
   Classify exome variants into four clinical significance categories: pathogenic, likely pathogenic, likely benign, or benign. Variants from the same gene are split across train/test to prevent leakage.

2. Phenotype Association

   • Cancer-Predisposing Syndrome (CPS): Determine if a variant is linked to Hereditary Cancer-Predisposing Syndrome.
   • Cardiovascular Phenotype (CP): Predict whether a variant is associated with cardiovascular conditions.

3. Gene Localization

   • BRCA Classification (BRCA): Identify whether a variant belongs to BRCA1, BRCA2, or neither.
   • Top 5 Genes Prediction (TFG): Classify a variant into one of the five most frequently represented genes in the dataset.

Task	# Classes	# Samples
		Train	Dev	Test
Pathohenic Variant Prediction (PV)	4	85503	9340	12000
Cancer-Predisposing Syndrome (CPS)	2	49327	8456	12685
Cardiovascular Phenotype (CP)	2	26756	4587	6881
BRCA Classification (BRCA)	3	18506	3172	4760
Top 5 Genes Prediction (TFG)	5	30552	5238	7857

Usage

from datasets import load_dataset
# One of:
# [
#  'brca',
#  'cancer_predisposing_syndrome',
#  'cardiovascular_phenotype',
#  'pathogenic_variant',
#  'top_five_genes'
# ]
task_name = "brca"
dataset = load_dataset("cerebras/exome_bench", data_dir=task_name)
# dataset['train'], dataset['validation'], dataset['test']

SOTA Model Evaluations

Model	Task Acc/Precision/Recall/F1/ROCAUC
	PV	CPS	CP	BRCA	TFG
NT-Multispecies-2.5B	0, 0, 0, 0, 0	0, 0, 0, 0, 0	0, 0, 0, 0, 0	0, 0, 0, 0, 0	0, 0, 0, 0, 0

Citation

This benchmark was developed as part of the efforts supporting the paper: Introducing STRAND: A Foundational Sequence Transformer for Range Adaptive Nucleotide Decoding in collabration with Mayo Clinic. If you find our work valuable, please consider giving the project a star and citing it in your research:

@article{ExomeBench, 
    DOI={10.2139/ssrn.5183178}, 
    title={Introducing strand: A foundational sequence transformer for range adaptive nucleotide decoding}, 
    author={Ayanian, Shant and Osborne, Andrew and Xu, Clark and Molnar, Carl and Das, Pravat and Perez, Xoab and Natalia, Vassilieva and Pondenkandath, Vinay and Kanakiya, Bhargav and Venkatesh, Ganesh and et al.}, 
    year={2025},
    journal={Research Square Pre-print}
} 

Thank you for your support!

Uses

This dataset is intended for models that aim to test their ability on exome-specific data. With more genomic models now focusing on exome regions for training, there is a need for benchmarks that verify whether these models truly learn exome-related features—particularly since many existing benchmarks overlook exome data, even though most disease-associated variants lie in protein-coding regions.

Direct Use

These tasks are split into train/dev/test sets and are designed to fine-tune larger models, which can then be evaluated using metrics such as AUC. The dataset encompasses a range of tasks—from assessing general exome-specific changes to identifying gene-specific variants and predicting pathogenicity—providing a broad overview of model performance on clinically relevant exome data.

Out-of-Scope Use

The dataset is for research and benchmarking only; it should not be used as a standalone diagnostic tool. More so, ClinVar may still contain some noise or inconsistencies in variant annotations.

Dataset Card Contact

Correspanding email: exome-bench@cerebras.net

• Curated by: Cerebras Systems in collabration with Mayo Clinics
• Language(s) (NLP): Python
• License: Creative Commons Attribution 4.0: cc-by-4.0