Our research focuses on the computational approaches to problems in molecular biology. In particular, we are interested in exploring gene regulatory networks, mostly in metazoan genomes. We approach the subject through an integrated analysis of DNA sequence and gene expression data. We also strive to understand how sequences involved in gene regulation have evolved, and how such evolutionary dynamics may inform the discovery of novel regulatory sequences. Broadly speaking, our work may be said to fall in the areas of comparative and regulatory genomics.
FEATURED RESEARCH
Functional and Evolutionary Insights from the Genomes of Three Parasitoid Nasonia Species. The Nasonia Genome Working Group: Werren, J.H., Richards, S. , … Kim, J., … Sinha, S., … Gibbs, R.A. (157 authors)
Science (2010). Vol. 327. no. 5963, pp. 343 – 348 [IN THE NEWS] [SCIENCE PODCAST]
From Genome Web: “Nasonia Genome Working Group Publishes Results From Three Parasitoid Wasp Genomes: An international research team has sequenced and compared the genomes of three parasitoid wasp species belonging to the genus Nasonia, gaining new insights into the wasps’ biology and evolution. Those involved say genetic and genomic resources generated through the effort will help support the use of Nasonia wasps as model genetic organisms and biocontrol agents.”
Functional Characterization of Transcription Factor Motifs Using Cross-species Comparison across Large Evolutionary Distances. Jaebum Kim, Ryan Cunningham, Brian James, Stefan Wyder, Joshua D. Gibson, Oliver Niehuis, Evgeny M. Zdobnov, Hugh M. Robertson, Gene E. Robinson, John H. Werren, and Saurabh Sinha
PLoS Computational Biology, to appear.
This is our companion paper to the Nasonia genome paper published in Science magazine. We develop a computational pipeline for predicting the functions of transcription factor motifs, through DNA sequence analysis. The pipeline is applied to the newly sequenced genome of the jewel wasp, Nasonia vitripennis. It exploits the wealth of molecular data available in another insect species, the fruitfly Drosophila melanogaster, and uses cross-species comparison to its advantage. Our main contribution is to show how this can be done despite the large evolutionary divergence between the two species. The methodology presented here may be applied more generally to other scenarios (genomes) where comparative regulatory genomics must deal with large evolutionary divergences.
