Big Data from Biostatisticians'/Bioinformaticians' Perspective: From Epigenomics to Data Integration

• Activity Number: 271
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Guo-Cheng Yuan*+
• Companies: Harvard School of Public Health
• Keywords: hierarchical hidden Markov model ; chromatin ; gene regulatory network

Abstract

The genomic DNA provides a blueprint for gene regulation, but this blueprint is interpreted differently in different cell types. Epigenetics plays a critical role in the maintenance of cell-type specific gene expression programs. During the past few years, a large amount of epigenomic data have been generated, in part thanks to the rapid development of the next-generation sequencing technology. While these data have provided new biological insights, it has become increasingly clear that computational methods are still lacking both for analyzing these complex data and for effective integration with other data-types. Our group has been focused on developing computational approaches to overcome these challenges. In this talk, I will present our recent work on characterization of chromatin states, prediction of chromatin state variability patterns, and reconstruction of gene regulatory networks. These examples have demonstrated the utility of statistical models in extracting important biological information from large-scale datasets.