Uncovering hidden genomic features using computational approaches

Modern genetic studies are heavily dependent on analyses of whole genome sequences that have only become available in the past decade. Technologies such as microarrays and next-generation sequencing can associate quantitive expression patterns of genes to their genomic sequences and allow the study of changes at the genome-wide level or the comparison of multiple genomes. Sequences plus expression information allow us to capture an extensive and realistic overview on any given genome. Novel mathematical and computational methods are essential for managing and mining information from these large-scale data sets. I have undertaken three projects that try to answer the following biological questions using computational approaches: (1) how do duplicate genes diverge in a co-expression network? (2) how many vertebrate genes are there with alternative open reading frames? (3) how can we delineate whole genome expression patterns using new sequencing technology? Within each project, I have developed computational methods and applied these to targeted data sets demonstrating the feasibility and power of these new bioinformatic approaches and addressing questions of biological significance.