Overcoming artificial structures in resolution-enhanced Hi-C data by signal decomposition and multi-scale attention.

ABSTRACT: Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures in the enhanced data due to the need to divide the large contact matrix into small patches. In addition, previous deep learning methods largely focus on local patterns, which cannot fully capture the complexity of Hi-C data. Here we propose Smooth, High-resolution, and Accurate Reconstruction of Patterns (SHARP) for enhancing Hi-C data. It uses the novel approach of decomposing the data into three types of signals, due to one-dimensional proximity, contiguous domains, and other fine struc

SUBMITTER: Li Q 

PROVIDER: S-EPMC11526948 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature