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LOL-EVE Ultra Rare Variants Dataset
Dataset Description
This dataset contains ultra rare variants from gnomAD for evaluating variant effect prediction models. The dataset includes variants with minor allele frequency (MAF) < 0.001 in promoter regions across diverse genes.
Dataset Structure
- Total Variants: ~549,000
- Species: Primates (Homo sapiens)
- Variant Types: Insertions, deletions, and substitutions in promoter regions
- Sequence Length: Up to 1,000bp promoter regions
- MAF Range: < 0.001 (ultra rare)
Features
Basic Variant Information
gene: Gene symbolspecies: Species nameclade: Evolutionary cladechromosome: Chromosomeposition: Genomic positionref: Reference allelealt: Alternative allelevariant_type: Type of variant (insertion/deletion/substitution)
Sequences
wt_sequence: Wild-type sequencevar_sequence: Variant sequencesequence_length: Length of sequencewt_sequence_start: Start position of sequence
Variant Annotations
filter: Variant filtering statusmaf: Minor allele frequency
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("cshearer/LOL-EVE-UltraRare")
# Access the data
print(f"Dataset size: {len(dataset['train'])}")
print(f"Features: {dataset['train'].features}")
# Example: Get variants by MAF threshold
rare_variants = dataset['train'].filter(lambda x: x['maf'] < 0.0001)
print(f"Ultra rare variants (MAF < 0.0001): {len(rare_variants)}")
# Example: Get variants by type
insertions = dataset['train'].filter(lambda x: x['variant_type'] == 'insertion')
deletions = dataset['train'].filter(lambda x: x['variant_type'] == 'deletion')
substitutions = dataset['train'].filter(lambda x: x['variant_type'] == 'substitution')
Citation
If you use this dataset in your research, please cite:
@article{loleve2024,
title={LOL-EVE: Language of Life - Evolutionary Variant Effects},
author={Your Name and Collaborators},
journal={Nature},
year={2024}
}
License
This dataset is released under the MIT License.
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