Biostatistics & Bioinformatics
Statistics in biomedical research
Statistical methods have always been instrumental to the life sciences and biomedical research, and they have become even more critical with the advent of contemporary high-throughput techniques in the “omics” (genomics, meta-genomics, epigenomics, transcriptomics, proteomics, metabolomics, etc.), electronic medical records, and advanced medical imaging.
For the last two decades, Penn State has been home to many statisticians making pivotal contributions to these fields through collaborative projects. A collaboration between Drs. Francesca Chiaromonte, Marzia Angela Cremona, Di Chen, and Kateryna Makova with researchers from the Washington University School of Medicine, has developed further understanding of ‘jumping genes' effect on the genome. Unusual DNA folding increases the rates of mutations
Leveraging their expertise in various areas of Statistics, these faculty and their students develop novel statistical and computational methodology for the analysis of massive, high-dimensional, structured and complex data. Their work is supported by the National Institutes of Health and the National Science Foundation, as well as by the Huck Institutes for the Life Sciences of Penn State. Many belong to interdisciplinary centers at Penn State, such as the Center for Medical Genomics (CMG), the Center for Computational Biology and Bioinformatics (CCBB), and the Center for Infectious Disease Dynamics (CIDD).
Faculty and Student Research Collaborations
Unusual DNA folding increases the rates of mutations
DNA sequences that can fold into shapes other than the classic double helix tend to have higher mutation rates than other regions in the human genome. New research shows that the elevated mutation rate in these sequences plays a major role in determining regional variation in mutation rates across the genome. Deciphering the patterns and causes of regional variation in mutation rates is important both for understanding evolution and for predicting sites of new mutations that could lead to disease.
“Most of the time we think about DNA as the classic double helix; this basic form is referred to as ‘B-DNA,’” said Wilfried Guiblet, co-first author of the paper, a graduate student at Penn State at the time of research and now a postdoctoral scholar at the National Cancer Institute. “But, as much as 13% of the human genome can fold into different conformations called ‘non-B DNA.’ We wanted to explore what role, if any, this non-B DNA played in variation that we see in mutation rates among different regions of the genome.”
A paper describing the research by a team of Penn State scientists is available online in the journal Nucleic Acids Research.