Integrative and multiscale analysis of epigenomic sequencing data

点击翻译按钮获取中文摘要

The `epigenome' consists of a multitude of chemical instructions for the process of building proteins from DNA. These instructions govern how and when genes are turned on and off. Different cell types, developmental stages, and environmental exposures can each have their own set of instructions. Decoding these instructions is vital to understanding the fundamental molecular processes of gene regulation and their variation. While new technologies can now probe the epigenome in more dimensions and at higher resolution than ever before, our ability to interpret these measurements lags far behind our ability to generate them. The bottleneck in extracting meaningful biological information from epigenomic experiments lies in data analysis and interpretation. My research program focuses on developing and applying tailored statistical methodology to characterize epigenetic variation and its effects on gene expression. In particular, a critical gap lies in the difficulty in integrating epigenomic information from different sample and measurement sources. Part of this challenge results from the presence of uncontrolled technical artifacts that can mask biological differences. Another major obstacle stems from the fact that epigenetic changes can take place at many different scales, involving anywhere from a single nucleotide to millions, but existing analytical approaches are hardwired to examine one scale at a time. To address these shortcomings, my research objectives focus on developing new quantitative frameworks for normalization, multiscale inference, and integrative modeling of epigenomic sequencing data. Specifically, we will develop a statistical approach for the computational correction of recently identified sequencing biases in methylation data that can confound the interpretation of biological signals. To infer epigenetic changes at multiple scales of resolution, we will develop a statistical approach that infers the scale from the data. In addition, we will carry out a systematic in silico evaluation of the relationship between epigenetic modification and gene expression that accounts for spatial relationships. The proposed research facilitates the integration of epigenomic information, which will lead to greater understanding of the molecular processes governing gene regulation and their variation with respect to factors such as environment or age. Key advances in analytic procedures will allow for unbiased interpretation that is also agnostic to the scale at which the signal predominates. Systematic exploration of the regulatory potential of inter- and intra-genetic modifications will yield insight into how epigenomic signals interact. Further, the methodology developed will be disseminated through publicly available tools, which will enable other investigators in the community to extract meaningful biological insights from sequencing data.