An integrative approach for fine-mapping chromatin interactions.

ABSTRACT:

Motivation

Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions.

Results

To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution sour

SUBMITTER: Jaroszewicz A 

PROVIDER: S-EPMC7425030 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature