cerebras/exome_bench

ExomeBench: A Benchmark Dataset for Clinical Variant Interpretation in Exome Regions 🧬

Dataset Files

Paper | Evaluation Code (GitHub)

ExomeBench consists of datasets and code. Datasets are licensed under Creative Commons Attribution–Non-Commercial 4.0 (CC BY NC 4.0). Code provided in ExomeBench is licensed under Apache 2.0. ExomeBench is a research benchmark. It is not a diagnostic tool and should not be used to make clinical decisions.

1. Project Overview

The ExomeBench dataset is derived from ClinVar (Nov 2024 release), 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, GRCh38).

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.

Code to fine-tune and evaluate models on this dataset using the Hugging Face Transformers library is available in ExomeBench GitHub Repository.

2. Dataset Details

Data Collection

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

Data Filtering

• Assertion Criteria: We include only variants with at least one submitter providing an interpretation and satisfying the assertion criteria for reliability.
• Variant Type: Only single-nucleotide variants (SNVs) are selected.
• Exome-Specific Regions: Filter the variants to include only those located in exome-specific regions (GENCODE v.38).

Sequence Generation

• Human Reference Genome (HRG, GRCh38): For each variant, generate input sequences from the HRG using the variants from the ClinVar database.
• Sequence Length: The length of the sequences is a parameter, typically set to 100 base pairs (bp).
• 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.

3. Tasks

ExomeBench includes five supervised tasks, each framed as a classification problem:

• Pathogenic 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.

• 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.

• 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
Pathogenic 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

4. SOTA Model Performances

Model	Task (MCC)
	PV	CPS	CP	BRCA	TFG
STRAND[1]	0.360	0.937	0.774	0.877	0.996
DNABERT-2-117M	0.162	0.876	0.549	0.552	0.996
HyenaDNA-Tiny-1k	0.135	0.816	0.445	0.700	0.994
NT-Multispecies-2.5B	0.306	0.624	0.293	0.422	0.991

Note: For some models and tasks, the seed settings in the STRAND paper were slightly different from those used in this repository, which may lead to minor variations in the reported results. Due to this, on an overly saturated tasks like TFG, you might observe a small discrepancy in the ordering of models based on MCC values compared to those reported in the paper.

5. 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']

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 collaboration 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={https://doi.org/10.1093/bib/bbaf618}, 
    title={Introducing a foundational sequence transformer for range adaptive nucleotide decoding (STRAND)}, 
    author={Ayanian, Shant et al.}, 
    year={2025},
    journal={Briefings in Bioinformatics, Volume 26, Issue 6}
} 

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

Corresponding email: exome-bench@cerebras.net

• Curated by: Cerebras Systems in collaboration with Mayo Clinic
• Language(s) (NLP): Python
• License: Creative Commons Attribution-NonCommercial 4.0 International: cc-by-nc-4.0