DNA methylation, a type of chemical modification of DNA, is an important, heritable change that plays a key role in both normal and diseased cells without altering the sequence of the bases that DNA comprises. It is one of the most common molecular changes in cancer cells and affects gene expression levels. Therefore, it is important to study cancer DNA methylation, especially differential methylation patterns between two groups of samples (e.g., patients and normal individuals). With next generation sequencing (NGS) technologies, it is now possible to identify differential methylation patterns in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical approach using a Hidden Markov Model (HMM) to identify differentially methylated regions. In particular, we first use a hidden Markov chain to model the methylation signals to infer the methylation state and then use statistical tests to identify differentially methylated sites. The advantage of our method is that it can incorporate the methylation information in neighboring sites and thus reduce the impact of sequencing errors. In this presentation, we show our HMM-based method and compare it with other available methods using both publicly available data and simulation data.
Dr. Shuying Sun is an assistant professor at the Department of Mathematics, Texas State University. She received her Ph.D. in statistics from the University of Toronto and conducted postdoctoral research at the Mathematical Biosciences Institute, the Ohio State University. Currently, Dr. Sun teaches both undergraduate and graduate statistics courses at Texas State University. Dr. Sun’s research focuses on addressing challenging genetic and epigenetic questions using statistical and computational methods. In particular, Dr. Sun has been working on statistical genetics and bioinformatics with a focus on methylation microarray and sequencing data analysis, haplotype inference, mutation age estimation, and genetic variant identification. Dr. Sun has collaborated with biomedical researchers from different research groups in Canada and the United States on projects related to complex diseases (e.g., cancer and arthritis). She has also been developing statistical methodologies and software packages for genomic and epigenomic problems using Bayesian methods, Hidden Markov Models, Markov Chain Monte Carlo algorithms, and linear models.
Dr. Betancourt is an Assistant Professor in the Department of Chemistry and Biochemistry and a faculty member of the Materials Science, Engineering, and Commercialization Program at Texas State University-San Marcos. Dr. Betancourt’s research focuses on capturing the promise of nanomaterials and biomimetic materials for creating new biomedical technologies. Dr. Betancourt leads the research of the Biomaterials and Nanomedicine Laboratory at Texas State University, where research focuses on the development of functional nanostructures that can act as highly specific contrast agents for bioimaging, targeted and intracellular drug delivery systems, photoablation agents, and stimuli controlled delivery systems. Prior to joining Texas State University in 2011, Dr. Betancourt worked at InnoSense LLC, a technology company serving the aerospace, energy, defense, and health care market. During her three-year tenure at InnoSense, Dr. Betancourt held the positions of Research Scientist, Team Leader, and Deputy Director-R&D. At InnoSense, Dr. Betancourt was responsible for developing novel technologies in the areas of biosensors, biomaterials, therapeutics, theranostics, contrast agent, drug delivery, and specialty materials. Dr. Betancourt obtained her B.S. degree in chemical engineering from Texas A&M University, College Station, in 2002, followed by her M.S. and Ph.D. degrees in biomedical engineering from The University of Texas at Austin in 2005 and 2007, respectively. Dr. Betancourt is currently a recipient of a grant by the Research Corporation for Science Advancement (2012), and is co-PI in a NSF PREM grant (2012) and an internal Research Enhancement Program grant (2013). Dr. Betancourt’s work has been documented in four peer-reviewed publications, two review articles, two book chapters, and multiple professional presentations.
Climate, characterized by the seasonal variation of temperature and precipitation patterns, affect all aspects of plant function. Even though plants are well-adapted to coping with variability and periods of drought stress, they have tolerance limits which ultimately determine the distribution of species across climate zones. In this talk, I will give a brief overview of the research projects in my lab that address this issue at both experimental and theoretical levels. First, I will show how two common tree species of the Edwards Plateau – Ashe juniper and live oak – respond to wet and dry years. Second, I will discuss the importance of below-ground water storage capacity for tree survival during extended drought periods. Third, I will explain current efforts to parameterize a dynamic global vegetation model (DGVM) for Texas to predict past (2011) and future patterns of tree mortality and the possible direction of permanent vegetation change in Texas, if droughts of 2011 magnitude should become more common.
Susan Schwinning is an Associate Professor in Biology at Texas State University, where she conducts research in Ecohydrology and plant responses to drought. Prior to Texas State, Dr. Schwinning has served as a professor at the University of Arizona as well as a postdoctoral scholar for Columbia University's Biosphere 2 project. Dr. Schwinning received her B.S. in Biology from the University of Göttingen in Germany, her M.S. In Plant Physiology at the University of California, Davis, and her Ph.D. in Ecology from the University of Arizona. In addition to teaching and conducting research, Dr. Schwinning also serves as an Associate Editor for Oecologia and the Journal of Ecology. Dr. Scwinning has over 45 publications including ones in Nature, Functional Ecology, and Bioscience. She is the recipient of the John L. Harper Young Investigator Prize for a paper published in the Journal of Ecology, British Ecological Society.