Bayesian Analysis of chromosomal interactions in Hi-C data using the hidden Markov random field model

Bayesian Analysis of chromosomal interations in Hi-C data using the hidden Markov random field model

There are different biological methods that have been developed over the years for analysis of the 3D structure of the DNA. Few computational and statistical methods have, however, been developed to analysis data generated using the Hi-C method.

We follow statistical methodology to explore the Hi-C data. The Hi-C data is well suited to be analysed using a finite mixture model. The Potts model, a hidden Markov random field model, was employed to analyse the hidden (latent) components. The hidden components through the Potts model can be categorised into k components (k = 2,3…,K).

Using the Metropolis-within-Gibbs approach to analyse the data, the proposed method was able to detect interactions (short and long range) and loops. A large part of the significant interactions that we detect are found within Topological Associated Domains, which is one of the 3D structures known to occur in Hi-C data.

Speaker

Godwin Osuntoki, University of Essex

How to attend

If not a member of the Dept. Mathematical Science at the University of Essex, you can register your interest in attending the seminar and request the Zoom’s meeting password by emailing Dr Osama Mahmoud (o.mahmoud@essex.ac.uk).