Predicting the impact of sequence motifs on gene regulation using single-cell data

Genome Biology

Method

Siwat Ruangroengkulrith¹  Varodom Charoensawan²  Jacob Hepkema³  Benjamin J. Stewart⁴  Menna R. Clatworthy⁴  Nicholas Keone Lee⁵  Martin Hemberg⁶ 

[1] Department of Biochemistry, Faculty of Science, Mahidol University, 10400, Bangkok, Thailand;Department of Biochemistry, Faculty of Science, Mahidol University, 10400, Bangkok, Thailand;Integrative Computational BioScience (ICBS) Center, Mahidol University, 7310, Nakhon Pathom, Thailand;Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, 10400, Bangkok, Thailand;Wellcome Sanger Institute, Wellcome Genome Campus, CB10 1SA, Hinxton, UK;Wellcome Sanger Institute, Wellcome Genome Campus, CB10 1SA, Hinxton, UK;Molecular Immunity Unit, Department of Medicine, University of Cambridge, CB2 0QQ, Cambridge, UK;Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, CB2 0QQ, Cambridge, UK;Wellcome Sanger Institute, Wellcome Genome Campus, CB10 1SA, Hinxton, UK;The Gurdon Institute, University of Cambridge, Tennis Court Road, CB2 1QN, Cambridge, UK;Wellcome Sanger Institute, Wellcome Genome Campus, CB10 1SA, Hinxton, UK;The Gurdon Institute, University of Cambridge, Tennis Court Road, CB2 1QN, Cambridge, UK;Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Massachusetts General Hospital, and Harvard Medical School, 02115, Boston, MA, USA;

关键词: ;   

DOI  :  10.1186/s13059-023-03021-9

 received in 2022-05-02, accepted in 2023-07-21,  发布年份 2023

来源: Springer

【 摘 要 】

The 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

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