We are interested in deciphering the evolutionary history of life by comparative analysis of genome sequences. Our research currently focuses on the development of computational methods for RNA, protein, and genome sequence analysis. We use probabilistic modeling approaches to build statistical models of interesting biological features, and use such models for Bayesian or likelihood-based statistical inference in large scale genome analysis and annotation.

We are currently working in three general areas. One area is RNA structure and function. We are developing new methods for RNA homology detection, RNA structure prediction, and identification of conserved RNA structures by comparative genome sequence analysis (including both noncoding RNA genes and cis-regulatory RNA structures). A second area is the improvement of methods for remote protein homology detection, implemented in our HMMER software package. Finally, more recently we have begun collaborating with neuroscientists on the analysis of neural cell-type-specific genomic data, aiming to learn more about the evolution and development specification of neuronal cell types.