报告人：
郑杰教授
Bioinformatics Research Centre, School of Computer Engineering,Nanyang Technological University, Singapore
时间：6月21日上午9:30
地点：生物电子学国家重点实验室会议室
报告题目：
Computational Discovery of Epigenetic Regulation of Genetic Recombination and Transcription
Abstract
Epigenetics is a young and exciting field that studies the molecular mechanisms of cellular phenotypes beyond DNA sequences. In this talk, I will present computational approaches and discoveries that shed light on epigenetic mechanisms of two fundamental cellular processes: genetic recombination and transcription.
The regulatory mechanism of recombination is a fundamental problem in genomics, with wide applications in genome wide association studies, birth-defect diseases, molecular evolution, cancer research, etc. In most species, recombination events cluster into short regions called "recombination hotspots". Recently, a zinc finger protein, PRDM9, was discovered to be a trans-acting regulator of recombination hotspots. We proposed an approach to predicting additional candidate trans-regulators from transcription factors by assessing their preference of binding to hotspots. Applying this approach on newly mapped mouse hotspots genome-wide, we confirmed that PRDM9 is a major trans-regulator of hotspots. In addition, a list of top candidate trans-regulators of mouse hotspots was reported. Using GO analysis we observed that the top genes are significantly enriched with function of histone modification, highlighting the epigenetic regulatory mechanisms of recombination hotspots. Moreover, we encoded genomic and epigenomic features into a support vector machine (SVM) to predict hotspots. Trained on known hotspots and coldspots in human and mouse genomes, the model is able to predict hotspots based on the features with good performance across chromosomes and species. The model reports a ranking of feature importance, in which histone modifications are at the top of the list. To our best knowledge, this is the first result of cross-species study of recombination hotspots considering both genetic and epigenetic features.
The reverse engineering of gene regulatory network (GRN) is an important problem in systems biology. We investigate how epigenetic data can be incorporated into reconstruction of GRN. We encoded the histone modification data as prior for Bayesian network inference of GRN. Applying to the transcription data of yeast cell cycle, we demonstrated that integration of epigenetic data can improve the accuracy of GRN inference significantly. Furthermore, fusion of gene expression and epigenetic data is promising to uncover interactions between genetic and epigenetic regulations of gene expression.
Biography
Dr. Jie Zheng received his Ph.D. in 2006 from the University of California, Riverside and his B. Eng (first class honors) in 2000 from Zhejiang University in China, both in Computer Science. Before joining Nanyang Technological University as a tenure-track assistant professor in 2011, he was a research scientist at the National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH), USA. His research goal is to develop novel Bioinformatics algorithms and computational models to help answer Biomedical questions (such as the mechanisms of cancer) and improve public health. While trained as a Computer Scientist, Dr. Zheng keeps active and long-standing collaborations with Life Scientists. His current research directions include computational epigenetics, cancer systems biology and algorithm design for next-generation sequencing.