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 numbers. S2F models trained on custom or commercial data perform comparably in enhancer prediction, sequence explainability, and transcription factor footprinting. We demonstrate that integrating data from different scATAC-seq platforms enables large-scale, cost-efficient atlas construction for deep learning-based regulatory modeling. Generating high-quality training data for machine learning is costly. Here, authors include sequence-to-function modeling in benchmarking of custom and commercial droplet-based scATAC platforms, and release a new Drosophila embryo atlas along with a new mouse cortex atlas, assessed for model interpretability.
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