Dr. Peng Yu is an Assistant Professor in the Department of Electrical and Computer Engineering, Texas A&M University. He obtained his B.S. degree in physics from Peking University, P. R. China, and his M.S. degree in physics from the University of California, San Diego. He earned his Ph.D. degree in Computer Engineering from the University of Texas at Austin in 2009. He completed postdoctoral training in the Department of Molecular and Human Genetics at Baylor College of Medicine. He has published 20 papers in refereed journals including Science, Cell and Nature and conference proceedings. He has a U.S. patent. He has received a number of awards for his research contributions, including William H. Hildebrand Endowed Graduate Fellowship, Semiconductor Research Corporation Inventor Recognition Award.
- Statistical HTS data analysis
High-throughput sequencing technologies are widely used in biology experiments. These experiments typically have few biological replicates because of, for example, budget and time constraints. Our lab develops powerful and statistical rigorous methods to maximally extract information from these experiments that would otherwise not be possible.
- Regulation of gene expression
Eukaryote gene expression is tightly controlled to give arise to different cell types and tissues. To understand transcription regulation, we develop new computational methods by integrating ChIP-seq, DNA methylation, nucleosome positioning, CLIP-seq and RNA-seq data. We closely work with experimental biologists to validate our predictions.
- Genomics of complex diseases
Complex diseases, such as Autism Spectrum Disorders, are caused by a combination of genetic and environmental factors. The non-Mendelian inheritance pattern of these diseases has defeated attempts to predict the occurrence of these diseases based on genomic information. We address this challenge by developing novel computational methods using systems biology approaches such as molecular pathways and networks.
Metagenomics is the direct sequencing and analysis of microbial communities in environmental or host-constrained samples, which offers a viable way to study more than 99% of all microbes that are unculturable. But analyses of complex mixtures of organisms remain challenging. We are interested in developing new analysis methods to understand the composition and function of a microbial community and how microbiome is related to human health and environment.
- Structural genomics
Evolution-based methods are currently among the most successful ones in predicting protein structures. The central idea is that evolution information, derived based on protein sequences alone, can be used to guide protein folding. We are interested in developing new methods to predict protein structures using rapidly accumulating HTS data.