Evaluating single-cell ATAC-seq atlasing technologies using sequence-to-function modeling.

ABSTRACT: Deciphering the cis-regulatory logic underlying cell type identity remains a key challenge in biology. Single-cell chromatin accessibility (scATAC-seq) atlases enable training of sequence-to-function (S2F) deep learning models to decode enhancer logic. Yet, optimal criteria for constructing training datasets, i.e., the number of cells and ATAC fragments, remain unclear. Moreover, the suitability of different scATAC-seq platforms for such models has not been systematically tested. We introduce HyDrop v2, an improved custom droplet scATAC-seq method, and perform the first benchmark of scATAC-seq platforms focusing on its capacity to train S2F models and its capacity to yield TF footprints in different species. We show that lower fragment counts can be compensated for by increased cell number

SUBMITTER: Dickmanken H 

PROVIDER: S-EPMC12929571 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature