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, ?-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. ?-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show ?-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. ?-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible.

Availability and implementation

?-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN).

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Jaroszewicz A 

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

REPOSITORIES: biostudies-literature