Title

Towards understanding of the three-dimensional chromosomal organization. A Bayesian model-based approach to analyze Hi-C data

 

Abstract

Understanding how chromosomes fold provides insights into transcription regulation and hence functional state of the cell. Recently, chromosomal conformation capture (3C)-related technologies have been developed to provide a genome-wide view of chromatin organization. Despite great technologies, multiple layers of noise and uncertainties stem from the sophisticated experiments, coupled with various sequencing-related artifacts, making the analysis of such data extremely challenging. Here using Hi-C as an example, we review the critical issues of analyzing this latest type of genomics data, including normalization, modeling and inference. We describe a novel Bayesian probabilistic approach, denoted “Bayesian 3D constructor for Hi-C data” (BACH), to infer chromosome three-dimensional (3D) structures from Hi-C data. We also discuss the observations we made when applying BACH to real Hi-C datasets. This is a collaboration with Ming Hu, Ke Deng, Jesse Dixon, Siddarth Selvaraj, Jennifer Fang, Bing Ren and Jun Liu (see http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002893).

 

About the presenter

Dr. Qin will be visiting the University for several days.  In particular, he will have free time Wednesday, the 2nd, before and after his talk at the seminar and Friday morning.  To schedule some time with him, please contact Dr. Qin (zhaohui.qin@emory.edu) or his host, Dr. Jukka Corander (jukka.corander@helsinki.fi).

 

Dr. Qin received his PhD in Statistics from University of Michigan in 2000. He underwent 3 years of postdoctoral training in Dr. Jun Liu’s lab at Harvard University. He was Assistant Professor at Center for Statistical Genetics, Department of Biostatistics at University of Michigan from 2003 to 2010. And he is now Associate Professor at Department of Biostatistics and Bioinformatics at Emory University.

 

Dr. Qin has more than ten years of experience in statistical modeling and computing with applications in statistical genetics and genomics. In the past five years, research work in his group has been focused on developing model-based methods to analyze data generated from applications of next generation sequencing technologies such as ChIP-seq, RNA-seq and Hi-C. He is also actively collaborating with biologists and clinicians on various projects that utilizing next generation sequencing technologies to study cancer genomics.