Publication Date

2-17-2023

Journal

iScience

DOI

10.1016/j.isci.2023.105945

PMID

36866046

PMCID

PMC9971889

PubMedCentral® Posted Date

1-7-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Biological sciences, Biochemistry, Genetics

Abstract

The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies such as Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still remains. Here we introduce DeepBend, a convolutional neural network model with convolutions designed to directly capture the motifs underlying DNA bendability and their periodic occurrences or relative arrangements that modulate bendability. DeepBend consistently performs on par with alternative models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBend's genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling the bendability of topologically associated domains and their boundaries.

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