Hepkema J.Lee N.K.Stewart B.J.Ruangroengkulrith S.Charoensawan V.Clatworthy M.R.Hemberg M.Mahidol University2023-08-282023-08-282023-12-01Genome Biology Vol.24 No.1 (2023)14747596https://repository.li.mahidol.ac.th/handle/20.500.14594/88815The binding of transcription factors at proximal promoters and distal enhancers is central to gene regulation. Identifying regulatory motifs and quantifying their impact on expression remains challenging. Using a convolutional neural network trained on single-cell data, we infer putative regulatory motifs and cell type-specific importance. Our model, scover, explains 29% of the variance in gene expression in multiple mouse tissues. Applying scover to distal enhancers identified using scATAC-seq from the developing human brain, we identify cell type-specific motif activities in distal enhancers. Scover can identify regulatory motifs and their importance from single-cell data where all parameters and outputs are easily interpretable.Agricultural and Biological SciencesArticleSCOPUS10.1186/s13059-023-03021-92-s2.0-851680671721474760X37582793