AI Article Synopsis
- The binding of transcription factors to gene regions like promoters and enhancers is key for regulating genes, but figuring out how these elements work is tough.
- A new tool called scover uses convolutional neural networks trained on single-cell data to identify regulatory motifs and assess their importance for gene expression.
- Scover reveals that it accounts for 29% of gene expression variation in mouse tissues and can identify specific regulatory activities in distal enhancers of the developing human brain, making the results more understandable.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10426127 | PMC |
| http://dx.doi.org/10.1186/s13059-023-03021-9 | DOI Listing |
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