RT Journal Article
SR Electronic
T1 ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.19.463680
DO 10.1101/2021.10.19.463680
A1 Itunu G. Osuntoki
A1 Andrew Harrison
A1 Hongsheng Dai
A1 Yanchun Bao
A1 Nicolae Radu Zabet
YR 2021
UL http://biorxiv.org/content/early/2021/10/20/2021.10.19.463680.abstract
AB Several computational and statistical methods have been developed to analyse data generated through the 3C-based methods, especially the Hi-C. Most of existing methods do not account for dependency in Hi-C data. Here, we present ZipHiC, a novel statistical method to explore Hi-C data focusing on 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 Hi-C contact frequency matrix. ZipHiC uses data on the sources of biases related to 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, 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.Competing Interest StatementThe authors have declared no competing interest.