Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer.

ABSTRACT: The quantitative characterization of the transcriptional control by histone modifications has been challenged by many computational studies, but most of them only focus on narrow and linear genomic regions around promoters, leaving a room for improvement. We present Chromoformer, a transformer-based, three-dimensional chromatin conformation-aware deep learning architecture that achieves the state-of-the-art performance in the quantitative deciphering of the histone codes in gene regulation. The core essence of Chromoformer architecture lies in the three variants of attention operation, each specialized to model individual hierarchy of transcriptional regulation involving from core promoters to distal elements in contact with promoters through three-dimensional chromatin interactions. In-de

SUBMITTER: Lee D 

PROVIDER: S-EPMC9637148 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature