Data Science Meetup at CU Health Campus (Aurora)
Sequencing the Human Genome & Personalized Medicine & Bioinformatics
Tuesday, December 10, 2013
6:00 PM to 9:00 PM
Colorado School of Public Health - University of ColoradoPrevious Slide 1/4 Next
Sequencing the Human Genome & Personalized Medicine
High-throughput technologies have produced enormous data sets with potential revolutionize the way we think about health and disease. This talk will examine how sequencing the human genome drove the emergence of BIG DATA in biomedical research and discuss how next-generation (next-gen) sequencing is helping to understand personalized medicine. Given that billions of microbes inhabit our bodies and represent the most intimate connection humans have to their environment, this talk will also provide a specific example of how next-gen sequencing is helping to understand how maternal-infant microbial exposures impact obesity during early life events.
Dr. Lemas completed a BS in biology at the University of Vermont (UVM) and later completed his PhD in the Department of Biochemistry at the University of Alaska Fairbanks (UAF). Dominick’s doctoral research in molecular epidemiology was focused on elucidating genetic and dietary factors that influence obesity and fasting lipid profiles in Yup’ik Eskimos. Currently, Dr. Lemas is a postdoctoral research fellow at the University of Colorado Denver (UCD) in the Department of Pediatrics where his research interests are devoted to understanding how early exposures to the maternal-fetal environment impact infant adiposity, with an emphasis on the development of the maternal-fetal microbiome.
One of the challenges of bioinformatics is integrating big(ish) genome-wide data from a variety of sources. Our lab is focused on the study of lung diseases like asthma, pulmonary fibrosis, and emphysema. We measure gene expression at 10's of thousands of sites, methylation at millions of sites, thousands of microbes from unmentionable sites, and millions of genetic changes across hundreds to thousands of samples. I will discuss my work on attempting to integrate these diverse measurements with the idea that the aggregate information will tell us more about the disease than each "bit" of information would on it's own. I will also cover some very simple things I do to develop software that works on large data.
Dr. Brent Pedersen is a computational biologist at the University of Colorado medical campus. He develops methods and writes software in python, R, awk, bash, C, lua and perl to analyze genomic data (at: https://github.com/brentp/ ). Like all bioinformaticians, he spends a lot of time converting data from one format to another. Brent is perennially suspicious of p-values (and q-values). He earned a Ph.D. at the University of California at Berkeley.