TITLE: How chromosome fold? What sequencing and Bayesian modeling can tell us about the three-dimensional organization of mammalian genomes
SPEAKER: Dr. Zhaohui Steve Qin
Understanding how chromosomes fold provides insights into transcription regulation 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.
Contact: Zhaohui Steve Qin <email@example.com>
BIO: Dr. Qin is currently an Associate Professor in the Department of Biostatistics and Bioinformatics at Rollins School of Public Health, Emory University. He is also a faculty member at the Department of Biomedical Informatics, Emory University School of Medicine and Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute. Dr. Qin received his B.S. degree in Probability and Statistics from Peking University in 1994 and Ph.D. degree in Statistics from University of Michigan in 2000. He was a postdoctoral fellow in Dr. Jun Liu's group in Department of Statistics at Harvard University from 2000 to 2003. He joined the Department of Biostatistics at University of Michigan in 2003. In 2010, he moved to his current position in Emory University. Dr. Qin has more than ten years of experience in statistical modeling and statistical computing with applications in statistical genetics and genomics. Recently, his research is focused on developing Bayesian model-based methods to analyze data generated from applications of next generation sequencing technologies such as ChIP-seq, RNA-seq and resequencing. Dr. Qin also actively collaborates with biomedical scientists and clinicians on various projects that utilizing next generation sequencing technologies to study cancer genomics. Dr. Qin has published more than 50 peer-reviewed research papers covering statistics, bioinformatics, statistical genetics and computational biology. He has severed multiple times as ad hoc member on various NIH study sections and has supervised more than 10 graduate students and postdoctoral fellows.