Title: "A hidden Markov random field based Bayesian method for the detection of long-range chromosomal interactions in Hi-C Data"
Speaker: Ming Hu, Department of Population Health; Divison of Biostatistics, New York University School of Medicine

Place: Materials and Electrical Engineering (MSEE) B012
Date: February 24, 2014; Tuesday
Time: 4:30pm


Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, the identification of non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfillment of this task has many important implications including identifying long-range interactions that assist in interpreting a sizable fraction of the results from genome-wide association studies (GWAS). The task - distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions - poses great challenges both statistically and computationally. Model-based methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration. We propose a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabilities in the two-dimensional space based on the contact frequency matrix. By borrowing information from neighboring loci pairs, our method demonstrates superior reproducibility and statistical power in both simulations and real data.

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