DHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility.

ABSTRACT: Histone modifications (HMs) are pivotal in various biological processes, including transcription, replication, and DNA repair, significantly impacting chromatin structure. These modifications underpin the molecular mechanisms of cell-type-specific gene expression and complex diseases. However, annotating HMs across different cell types solely using experimental approaches is impractical due to cost and time constraints. Herein, we present dHICA (deep histone imputation using chromatin accessibility), a novel deep learning framework that integrates DNA sequences and chromatin accessibility data to predict multiple HM tracks. Employing the transformer architecture alongside dilated convolutions, dHICA boasts an extensive receptive field and captures more cell-type-specific information. dHICA

SUBMITTER: Wen W 

PROVIDER: S-EPMC11421843 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature