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Datasets:
m42-health
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variant-benchmark

Tasks:
Text Classification
Modalities:
Tabular
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Formats:
parquet
Size:
1M - 10M
Tags:
gfm
genomics
dna
genomic foundation model
m42
BioFM
Libraries:
Datasets
Dask
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metadata
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
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      - name: vep
        dtype: string
    splits:
      - name: train
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    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
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      - name: alt_left
        dtype: string
      - name: alt_right
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      - 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<n<10M

Variant Benchmark

This benchmark is designed to evaluate how effectively models leverage variant information across diverse biological contexts. Unlike conventional genomic benchmarks that focus primarily on region classification, our approach extends to a broader range of variant-driven molecular processes.

Existing assessments, such as BEND and the Genomic Long-Range Benchmark (GLRB), provide valuable insights into specific tasks like noncoding pathogenicity and tissue-specific expression. However, they do not fully capture the complexity of variant-mediated effects across multiple biological mechanisms. This benchmark addresses that gap by incorporating a more comprehensive set of evaluations, enabling a deeper assessment of functional genomics models.

Quick Start 

from datasets import load_dataset

DATASET_SUBSET = "ancestry_prediction"

# Dataset subset should be from one of the available tasks:
# ['ancestry_prediction', 'non_coding_pathogenicity', 'expression',
#                         'common_vs_rare', 'coding_pathogenicity', 'meqtl', 'sqtl']

ds = load_dataset(
      "m42-health/variant-benchmark",
      DATASET_SUBSET,
)

print(ds)

# Example output:
# (Each dataset has a default 'train' split, but we recommend using k-fold cross-validation for better evaluation)
# DatasetDict({
#     train: Dataset({
#         features: ['sequence', 'id', 'start_idx', 'chromosome', 'variants', 'parents', 'label'],
#         num_rows: 14085
#     })
# })

Benchmark Tasks

  • Coding pathogenicity assessment: subset: coding_pathogenicity
    Accurate prediction of pathogenic coding variants is fundamental to precision medicine and clinical genomics. For this task, we use the 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, 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, 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 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, 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}
}