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

Osuntoki, Itunu G and Harrison, Andrew and Dai, Hongsheng and Bao, Yanchun and Zabet, Nicolae Radu (2022) 'ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data.' Bioinformatics, 38 (14). btac387-btac387. ISSN 1367-4803

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Abstract

Motivation Several computational and statistical methods have been developed to analyse 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 by using the ABC model. In addition to outperforming existing tools on both simulated and real data, our model also provides insights into different sources of biases that affects Hi-C data. We show that some datasets display higher biases from DNA accessibility or Transposable Elements content. Furthermore, our analysis in D. melanogaster showed that approximately half of the detected significant interactions connect promoters with other parts of the genome indicating a functional biological role. Finally, we found that the micro-C datasets display higher biases from DNA accessibility compared to a similar Hi-C experiment, but this can be corrected by ZipHiC.

Item Type: Article
Uncontrolled Keywords: Chromatin; Animals; Drosophila melanogaster; DNA; Bayes Theorem; Software; Bias
Divisions: Faculty of Science and Health
Faculty of Science and Health > Life Sciences, School of
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Jul 2022 11:55
Last Modified: 28 Nov 2022 15:45
URI: http://repository.essex.ac.uk/id/eprint/33147

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