Research

Making “high-end” computational biology more accessible

A major focus of the lab is in the development of methods and infrastructure to support the translation of this raw experimental data into biologically meaningful discoveries. High-throughput data production technologies are revolutionizing modern biology, but progress is frequently impeded by computational details completely unrelated to the scientific questions being investigated. It is our goal to remove these impediments and make complex computational analysis more accessible. Along with the Nekrutenko Lab at Penn State, we develop Galaxy, which allows computational tools to be trivially integrated into an analysis environment in which experimental biologists can construct complex analyses. We are broadly interested in novel computing infrastructure, user interface, and visualization approaches for facilitating biological discovery, both within the context of the Galaxy project and beyond it.

Understanding how complex function is encoded in the genome

Evolution of the regions that control gene expression appears to contribute substantially more to phenotypic diversity than evolution of genes themselves. However, we currently understand very little about how regulatory information is encoded in vertebrate genomes, and how this encoding allows for precise reproducible complex expression patterns, yet is harnessed by evolution to generate remarkable diversity. Our lab seeks to understand regulatory element structure in its evolutionary and functional context through the development of data mining and integration methods. We are particularly focused on the development of data driven predictive models for identifying candidate functional regions for experimental verification. We have developed effective approaches for learning characteristic predictive patterns between-species genome comparisons, and current work seeks to extend these methods to incorporate within-species comparisons as well as high-throughput experimental data to learn fundamental features of regulatory elements.