This work will develop a computational framework to advance the understanding of epigenetic gene regulation. One of the most important tasks in modern molecular biology is to understand the role of epigenome in gene regulation and in phenotype development. With the current unprecedented availability of genome-scale epigenetic modification data, this project will develop computational algorithms and tools to model histone modification and discover patterns that are of interest to gene regulation. The discovered histone modification patterns will provide us deep insights into multiple epigenetic modification interaction and genome-epigenome interaction. The probabilistic model of gene expression regulation constructed from large-scale genomics and epigenomic data integration will greatly advance understanding of gene regulation at different levels. The objectives of this research are to: (1) create novel algorithms to model epigenetic modifications from high-throughput sequencing data; (2) create computational and statistical algorithms to mine epigenetic modification patterns; and (3) model gene regulation through genomic and epigenomic data integration.
Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence. This work will develop tools for modeling epigenetic changes. All of the proposed research will be converted into software tools that will be released as open-source and freely available software tools to the scientific community. All the associated source code will be freely available to the public through the project website. The proposed research will have great impact on education at all levels. The research will be incorporated into the graduate, undergraduate and K12 education, and will be disseminated to the research community, informal science education, and the public to enhance scientific understanding. The education and outreach component also includes developing outreach activities for women and girls in interdisciplinary science.
