Predicting the impact of sequence motifs on gene regulation using single-cell data
Issued Date
2023-12-01
Resource Type
ISSN
14747596
eISSN
1474760X
Scopus ID
2-s2.0-85168067172
Pubmed ID
37582793
Journal Title
Genome Biology
Volume
24
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Genome Biology Vol.24 No.1 (2023)
Suggested Citation
Hepkema J., Lee N.K., Stewart B.J., Ruangroengkulrith S., Charoensawan V., Clatworthy M.R., Hemberg M. Predicting the impact of sequence motifs on gene regulation using single-cell data. Genome Biology Vol.24 No.1 (2023). doi:10.1186/s13059-023-03021-9 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/88815
Title
Predicting the impact of sequence motifs on gene regulation using single-cell data
Other Contributor(s)
Abstract
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.