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Bolero data

Data behind Bolero, a sequence-to-function model that predicts cell-state-specific chromatin accessibility and gene expression from DNA sequence: the Bolero-10M single-cell ATAC/RNA collection, the inputs the released models need, and the evaluation sets used in the paper. Raw fragment files are not included.

Layout

Folder Contents
metadata/ Cell, sample and cluster metadata per dataset
metacell/ Metacell AnnData and 32 bp parquet stores (training corpus)
pseudobulk/ Pseudobulk records used as model inputs
reference_bigwig/ Reference coverage bigwigs
peaks/, peak_adata/, gene_adata/ Peak sets and peak / gene count matrices
gene_data/ Pseudobulk gene expression and highly variable genes
cell_embedding/, chromvar/, motif_scan/ Cell embeddings, chromVAR scores, JASPAR motif hits
score_data/ Motif score inputs for the +AP1 models
qtl/ caQTL / eQTL evaluation sets
diff_analysis/ Differential accessibility tables
reference_gtf/ Gene annotations
index/ Key-to-path lookup tables used by bolerodata

Usage

Address files by name through bolerodata, which downloads each file on first use:

pip install bolerodata
from bolerodata import DATASETS, metadata

metadata.DATASET_METADATA.head()            # list datasets (offline)
DATASETS["HumanBrainDev"].cell_metadata     # downloads and returns a DataFrame

Model checkpoints live in arcinstitute/bolero-models.

Citation

Liu H, Mao S, Gallent T, Chen Y, Yan Z, Vogel I, Traunmuller L, Zhang R, Buenrostro JD, Zhou J, Greenberg ME. Predicting cell-state-specific gene regulation from DNA across species. bioRxiv (2026). doi:10.64898/2026.10.19.760026

@article{liu2026bolero,
  title   = {Predicting cell-state-specific gene regulation from DNA across species},
  author  = {Liu, H. and Mao, S. and Gallent, T. and Chen, Y. and Yan, Z. and
             Vogel, I. and Traunmuller, L. and Zhang, R. and Buenrostro, J. D. and
             Zhou, J. and Greenberg, M. E.},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.10.19.760026}
}
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