HicGAN infers super resolution Hi-C data with generative adversarial networks.

ABSTRACT:

Motivation

Hi-C is a genome-wide technology for investigating 3D chromatin conformation by measuring physical contacts between pairs of genomic regions. The resolution of Hi-C data directly impacts the effectiveness and accuracy of downstream analysis such as identifying topologically associating domains (TADs) and meaningful chromatin loops. High resolution Hi-C data are valuable resources which implicate the relationship between 3D genome conformation and function, especially linking distal regulatory elements to their target genes. However, high resolution Hi-C data across various tissues and cell types are not always available due to the high sequencing cost. It is therefore indispensable to develop computational approaches for enhancing the resolution of Hi-C data.

Results<

SUBMITTER: Liu Q 

PROVIDER: S-EPMC6612845 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature