--- dataset_info: - config_name: ancestry_prediction features: - name: sequence dtype: string - name: id dtype: string - name: start_idx dtype: int64 - name: chromosome dtype: string - name: variants dtype: string - name: parents dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 914858176 num_examples: 14085 download_size: 298355655 dataset_size: 914858176 - config_name: coding_pathogenicity features: - name: alt_left dtype: string - name: alt_right dtype: string - name: ref_left dtype: string - name: ref_right dtype: string - name: label dtype: float64 - name: chrom dtype: string splits: - name: train num_bytes: 353452168 num_examples: 82872 download_size: 128733817 dataset_size: 353452168 - config_name: common_vs_rare features: - name: ref_forward_sequence dtype: string - name: alt_forward_sequence dtype: string - name: label dtype: int8 - name: chromosome dtype: string - name: position dtype: int32 - name: alt_left dtype: string - name: alt_right dtype: string - name: ref_left dtype: string - name: ref_right dtype: string - name: chrom dtype: string splits: - name: train num_bytes: 1239819384 num_examples: 200000 download_size: 542989051 dataset_size: 1239819384 - config_name: expression features: - name: alt_left dtype: string - name: alt_right dtype: string - name: ref_left dtype: string - name: ref_right dtype: string - name: label dtype: int64 - name: chrom dtype: string - name: deepsea_label dtype: string splits: - name: train num_bytes: 650249506 num_examples: 156162 download_size: 270672126 dataset_size: 650249506 - config_name: meqtl features: - name: label dtype: int8 - name: chromosome dtype: string - name: position dtype: int32 - name: uid dtype: string - name: alt_left dtype: string - name: alt_right dtype: string - name: ref_left dtype: string - name: ref_right dtype: string - name: chrom dtype: string splits: - name: train num_bytes: 318695785 num_examples: 76488 download_size: 134361097 dataset_size: 318695785 - config_name: non_coding_pathogenicity features: - name: alt_left dtype: string - name: alt_right dtype: string - name: ref_left dtype: string - name: ref_right dtype: string - name: label dtype: int64 - name: chrom dtype: string - name: vep dtype: string splits: - name: train num_bytes: 1265476277 num_examples: 295495 download_size: 442285117 dataset_size: 1265476277 - config_name: sqtl features: - name: geneId dtype: string - name: snpId dtype: string - name: pv dtype: float64 - name: label dtype: int64 - name: tissue dtype: string - name: __index_level_0__ dtype: int64 - name: chrom dtype: string - name: pos dtype: int64 - name: ref_allele dtype: string - name: alt_allele dtype: string - name: alt_left dtype: string - name: alt_right dtype: string - name: ref_left dtype: string - name: ref_right dtype: string splits: - name: train num_bytes: 4501372944 num_examples: 1055241 download_size: 1855898562 dataset_size: 4501372944 configs: - config_name: ancestry_prediction data_files: - split: train path: ancestry_prediction/train-* - config_name: coding_pathogenicity data_files: - split: train path: coding_pathogenicity/train-* - config_name: common_vs_rare data_files: - split: train path: common_vs_rare/train-* - config_name: expression data_files: - split: train path: expression/train-* - config_name: meqtl data_files: - split: train path: meqtl/train-* - config_name: non_coding_pathogenicity data_files: - split: train path: non_coding_pathogenicity/train-* - config_name: sqtl data_files: - split: train path: sqtl/train-* tags: - gfm - genomics - dna - genomic foundation model - m42 - BioFM - BioToken - Variant license: cc-by-nc-4.0 task_categories: - text-classification pretty_name: variant-benchmark size_categories: - 1M Accurate prediction of pathogenic coding variants is fundamental to precision medicine and clinical genomics. For this task, we use the [AlphaMissense](https://github.com/google-deepmind/alphamissense) dataset, which provides a comprehensive catalog of coding variants annotated for pathogenicity. - **Noncoding pathogenicity assessment:** `subset: non_coding_pathogenicity`
Pathogenic variants in noncoding regions significantly impact gene regulation, influencing many complex traits and diseases. We assess this using the [BEND dataset](https://github.com/frederikkemarin/BEND), which contains 295,000 annotated single-nucleotide variants (SNVs) in noncoding genomic regions. - **Expression effect prediction:** `subset: expression`
Variant-driven changes in gene expression contribute to phenotypic diversity and disease processes. To quantify these effects, we use gene expression data from [DeepSea](https://www.nature.com/articles/nmeth.3547), which provides variant-associated regulatory annotations. - **Alternative splicing:** `subset: sqtl`
Variant-induced alternative splicing contributes significantly to human proteomic diversity and affects biological processes and diseases. We evaluate splicing-related variant effects using an [sQTL dataset](https://www.nature.com/articles/s41467-020-20578-2) derived from sqtlSeeker2, containing over one million variant-tissue pairs. - **DNA methylation:** `subset: meqtl`
Genomic variants can influence DNA methylation patterns, affecting gene regulation and disease susceptibility. For this task, we utilize meQTL data from the [GRASP database](https://pubmed.ncbi.nlm.nih.gov/24931982/), which links genetic variants to methylation changes. - **Ancestry classification:** `subset: ancestry_prediction`
Genomic variation encodes population structure, informing studies in evolutionary biology and disease susceptibility. To evaluate this capability, we used genomic segments labeled by five major superpopulations from the 1000 Genomes Project. - **Common vs synthetic variants:** `subset: common_vs_rare`
This task evaluates the model’s ability to recognize biologically conserved genomic contexts characteristic of authentic common variants. To create this deataset, we randomly sampled 100K common variants (MAF > 0.05) from GnomAD~\citep{chen2024gnomad} and paired each with a synthetic control variant generated by randomly substituting a nucleotide within a ±20-nucleotide local context window. ## Citation ``` @article {Medvedev2025.03.27.645711, author = {Medvedev, Aleksandr and Viswanathan, Karthik and Kanithi, Praveenkumar and Vishniakov, Kirill and Munjal, Prateek and Christophe, Clement and Pimentel, Marco AF and Rajan, Ronnie and Khan, Shadab}, title = {BioToken and BioFM - Biologically-Informed Tokenization Enables Accurate and Efficient Genomic Foundation Models}, elocation-id = {2025.03.27.645711}, year = {2025}, doi = {10.1101/2025.03.27.645711}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2025/04/01/2025.03.27.645711}, eprint = {https://www.biorxiv.org/content/early/2025/04/01/2025.03.27.645711.full.pdf}, journal = {bioRxiv} } ```