UC San Diego

Epigenetic modifications play a pivotal role in gene regulations and thus heavily influence phenotypic outcomes. Comparative epigenomics, which incorporates both epigenome and genome into interspecies comparison, has become a powerful tool for revealing regulatory features of the genome and evolutionary properties of the epigenome. With the rapid growth of the high-throughput sequencing technologies, a tremendous amount of epigenomic datasets has been generated in various species, urging the development of systematic and quantitative approaches to the integrative comparison of the genome and the epigenome.

In this dissertation, I first presented a likelihood approach to testing hypotheses on the co-evolution of genome and epigenome. By converting evolutionary biology hypotheses into explicit probabilistic forms, I was able to establish a class of evolutionary models to quantify the dependence of interspecies epigenomic variations on underlying sequence variations between orthologous regions. To better facilitate functional annotation of the genome, I further developed an algorithm named EpiAlignment based on the evolutionary models, which incorporates both sequence and epigenomic data into alignment. A web service was also implemented to make the algorithm accessible to the research community at large. Using EpiAlignment, I searched for genomic region with similarities in both sequences and epigenomic modification patterns between human and mouse, revealing thousands of pairs of cis-regulatory elements with potential functional correspondence. In summary, this dissertation work brings mathematical rigor to comparative epigenomic studies, laying foundation to functional genomic studies with evolution-based approaches.