Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet.
Coby VinerCharles A IshakJames JohnsonNicolas J WalkerHui ShiMarcela K Sjöberg-HerreraShu Yi ShenSantana M LardoDavid J AdamsAnne C Ferguson-SmithDaniel D De CarvalhoSarah J HainerTimothy L BaileyMichael M HoffmanPublished in: Genome biology (2024)
Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.
Keyphrases
- dna binding
- transcription factor
- genome wide
- dna methylation
- single molecule
- genome wide identification
- gene expression
- high resolution
- electronic health record
- high throughput
- copy number
- optical coherence tomography
- machine learning
- binding protein
- big data
- decision making
- artificial intelligence
- nucleic acid
- deep learning
- single cell