Matches in SemOpenAlex for { <https://semopenalex.org/work/W3043091519> ?p ?o ?g. }
- W3043091519 startingPage "737981" @default.
- W3043091519 abstract "Abstract Genes are regulated through enhancer sequences, in which transcription factor binding motifs and their specific arrangements (syntax) form a cis-regulatory code. To understand the relationship between motif syntax and transcription factor binding, we train a deep learning model that uses DNA sequence to predict base-resolution binding profiles of four pluripotency transcription factors Oct4, Sox2, Nanog, and Klf4. We interpret the model to accurately map hundreds of thousands of motifs in the genome, learn novel motif representations and identify rules by which motifs and syntax influence transcription factor binding. We find that instances of strict motif spacing are largely due to retrotransposons, but that soft motif syntax influences motif interactions at protein and nucleosome range. Most strikingly, Nanog binding is driven by motifs with a strong preference for ∼10.5 bp spacings corresponding to helical periodicity. Interpreting deep learning models applied to high-resolution binding data is a powerful and versatile approach to uncover the motifs and syntax of cis-regulatory sequences." @default.
- W3043091519 created "2020-07-23" @default.
- W3043091519 creator A5000244333 @default.
- W3043091519 creator A5006428391 @default.
- W3043091519 creator A5007801525 @default.
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- W3043091519 creator A5023637543 @default.
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- W3043091519 creator A5058539331 @default.
- W3043091519 creator A5058741660 @default.
- W3043091519 date "2019-08-21" @default.
- W3043091519 modified "2023-09-24" @default.
- W3043091519 title "Deep learning at base-resolution reveals motif syntax of the cis-regulatory code" @default.
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