Our lab is interested in how genome sequences specify organismal form and function. In particular, we are interested in understanding how the complex, coordinated patterns of gene expression that underlie animal development and the responses of microbes to changes in their environment are orchestrated by information in genome sequences. We address these questions using both experimental and computational genomic techniques.
Cracking the cis-regulatory code: Genome sequences are more than just a collection of coding regions strung together by junk DNA - they also contain information specifying when and where genes should be made. This regulatory information is critical to the proper functioning of virtually all biological processes. We are interested in precisely how this information is encoded in genome sequences. Our research involves the analysis of two systems: early embryonic development of the fruitflies and environmental stress responses in yeasts. We use diverse types of experimental data (some generated in our lab), including gene expression data, genome sequences, and characterizations of in vitro and in vivo transcription factor binding specificities to develop and apply computational tools to analyze cis-regulatory sequences and the transcriptional regulatory networks that read them out.
In the next few years, much of our work will focus on comparative genome sequence analysis. The genome sequence of a second Drosophila species will be completed in mid-2002, and species of 6 additional Saccharomycete yeasts have already been sequenced. We believe that this data will reveal critical aspects of the organization of cis-regulatory regions and how their architecture is related to their function.
Genomic approaches to studying ecology and evolution: In addition to studying how regulatory information is encoded in genome sequences, we are very interested in understanding the role that gene expression plays phenotypic variation within populations and in evolution. We are starting to use detailed experimental-genomics (i.e. microarray) techniques to study the natural ecology of fungi and the evolution of complex phenotypes related to their natural ecology, such as nutrient utilization and stress response. Our goals are to characterize natural gene expression variation is wild fungal populations, to identify aspects of gene expression patterns that are correlated with complex organismal phenotypes, and to link these to changes in the genome, using a variety of techniques to rapidly map the genetic basis of complex phenotypes.
Classification of tumors: In collaboration with physicians at Stanford, we are using human DNA microarrays to conduct genome-scale characterizations of gene expression in human tumors, with the goal of developing improved and higher resolution methods for classifying tumors. A fundamental difficulty in the understanding, prevention and treatment of cancer is that the currently recognized disease classes (e.g. breast cancer) are each really a collection of diseases having significant features in common (e.g. the organ where the tumor arose) but also many features that distinguish them. The diversity within most disease categories is reflected in a diversity of clinical outcomes and responses to specific therapeutic regimes. We have begun to use genome-wide gene expression measurements to build a higher resolution and more clinically relevant taxonomy of human tumors. In an analysis of tumors from patients with a type of non-Hodgkins lymphoma, we were able to identify two molecularly distinct subgroups of tumors that differed significantly in their response to standard chemotherapy regimes. We are expanding these studies to include more patients and more types of tumors, with the ultimate hope of being able to classify all human tumors in clinically homogenous groups.
Databases and Microarray Analysis Software: In support of our experimental projects, and to assist other researchers applying genomic tools or using their information, we develop, support and apply database and analysis software that helps transform the massive amounts of data generated by genome-scale experiments into meaningful biological insights. We are particularly interested in methods and software that help researchers identify and visualize coherent features in genomic data.