ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data.

ABSTRACT:

Motivation

Several computational and statistical methods have been developed to analyze data generated through the 3C-based methods, especially the Hi-C. Most of the existing methods do not account for dependency in Hi-C data.

Results

Here, we present ZipHiC, a novel statistical method to explore Hi-C data focusing on the detection of enriched contacts. ZipHiC implements a Bayesian method based on a hidden Markov random field (HMRF) model and the Approximate Bayesian Computation (ABC) to detect interactions in two-dimensional space based on a Hi-C contact frequency matrix. ZipHiC uses data on the sources of biases related to the contact frequency matrix, allows borrowing information from neighbours using the Potts model and improves computation speed using the ABC model. In

SUBMITTER: Osuntoki IG 

PROVIDER: S-EPMC9272800 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature