We meet weekly and work closely with the laboratory of Dr. Leah Kottyan. Altogether, we are a team of faculty, staff scientists, computational system administrators, post-doctoral fellows, PhD candidates, technicians, and undergraduate students. We come from backgrounds of computer science, bioinformatics, transcription factor research, immunology, developmental biology, and genetic analysis to study the intersection of gene regulation and human disease genetics.

Recent advances in genome sequencing technologies have resulted in a considerable increase in our ability to associate genomic regions with human diseases. One major result of these studies is that a substantial proportion of genetic risk is likely attributable to sequences not located within genes. Such non-coding regions frequently harbor binding sites for transcription factors, which control the degree, timing, and magnitude of gene expression. Recent studies have linked disruptions in transcription factor binding to a variety of human diseases. In our lab, we apply our advances in knowledge of transcription factor binding preferences (see project 1) and how to accurately model them (see project 2) to discover disruptions in transcription factor binding that contribute to human diseases. In particular, we focus on autoimmune diseases, with emphasis on Systemic Lupus Erythematosus (SLE, or Lupus).

Proper control of gene expression is governed by the complex interplay of many transcription factors. One major component governing where and how a transcription factor binds to genomic DNA is its inherent sequence binding preferences. In our lab, we evaluate models of transcription factor binding preferences, with the end goal of accurate prediction of transcription factor occupancy in vivo. In addition to accurate sequence preference models, we develop methods integrating multiple sources of information, including DNA accessibility, protein interactions, DNA looping, epigenetic modifications, and gene expression.

In collaboration with Tim Hughes at the University of Toronto, I have developed a joint computational and experimental method for accurately determining the sequence binding preferences of transcription factors. Using this approach, we have characterized the binding preferences of thousands of transcription factors, covering most of the branches of the eukaryotic tree and the majority of transcription factor families. The availability of this resource, and its continued development, will open new opportunities for a wide variety of genomic and functional genomic analyses. Further, it provides the groundwork for future studies aimed at understanding how transcription factors interact with DNA, both in vitro and in vivo.