Tara C. Matise, Ph.D.
I head the Laboratory of Computational Genetics, in the Department of Genetics at Rutgers University. The ultimate goal of our projects is to contribute to the identification of human disease genes.
"Computational Genetics" represents my joint interests in human genetics, computer science, statistical genetics, and bioinformatics. My research time is divided between basic research and developing tools and resources for use by the research community. Some of my research projects are related to specific diseases, while other projects involve development of genome-wide tools and are disease-independent. In addition, I currently run (2008-2012) the Coordinating Center for The PAGE Study (http://pagestudy.org).
The focus of much of my research has been developing and applying tools for genome mapping, focusing on the construction of linkage maps. The process of constructing linkage maps relies on statistical and heuristical methods, follows a stepwise algorithm, and is quite computationally intensive for large marker sets. I have written a computer program that is used for automated large-scale linkage and radiation hybrid mapping. MULTIMAP is freely available to the genetics community and has been used in several genome-wide mapping projects in humans and other organisms (mouse, rat, dog).
With completion of sequencing of the human genome, we have incorporated marker physical positions into a novel mapping approach to create the Rutgers combined physical-linkage maps. These maps contain over 28,000 markers and represent very comprehensive linkage maps.
Another research project in my group focuses on meta-analysis of schizophrenia genetic studies to identify and characterize candidate gene regions. We are working to identify the most likely candidate genes within several schizophrenia candidate regions, and to prioritize these for further study. (http://compgen1.rutgers.edu/schiz)
My newest area of research is to identify genes involved in female reproductive genetics - in other words, looking for genes that play a role in fertility. Ultimately I hope we will be able to use genetic markers to predict whether a woman is likely to proceed to the stage of infertility at a pace that is average, faster than average, or slower than average. This information could be useful for improved family planning.
PubMed link to list of my publications.This website provides current information about our research. It's home to a few web applications such as Map-O-Mat, Map Interpolator, and Rutgers Map Browser. You can also download some of our pre-compiled programs, source code, and mapping data files.