README.md

metadata

tags:
  - biology
  - medical
pretty_name: ExomeBench
size_categories:
  - 1K<n<10K

Dataset Card for the ExomeBench Dataset

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

Methods

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 ADD HERE.
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.

Tasks

There are 7 tasks created using the ClinVar data.

1. Exome Pathogenicity Prediction: Predict the pathogenicity of an exome variant sequence (pathogenic, likely pathogenic, likely benign, benign). Variants are stratified between train/test sets to ensure that variants from the same gene don't appear in both.
2. Exome Variants and Non-Variants: Predict whether an exome sequence represents a known SNV or a non-variant reference sequence.
3. Phenotype Prediction:
   1. Cancer-Predisposing Syndrome: Predict whether a variant is associated with the phenotype of Hereditary Cancer-Predisposing Syndrome.
   2. Cardiovascular Phenotypes: Predict whether a variant is associated with cardiovascular phenotypes.
4. Gene Prediction:
   1. BRCA: Predict whether a variant belongs to BRCA1, BRCA2, or neither (breast cancer genes).
   2. TTN: Predict whether a variant belongs to the TTN gene.
   3. Top Five Genes: Predict whether a variant belongs to one of five most common possible gene pools.

• Curated by: [More Information Needed]
• Language(s) (NLP): Python
• License: [More Information Needed]

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/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

[More Information Needed]