WHAT WE DO
Modern technologies have led to an explosion of biomedical transcriptomic data and revolutionized how biologists think about and study biology. Transcriptomics is the study of all of the RNA molecules in living cells. Expression of RNA is at the core of every process and behavior of every cell of every organism on the planet.
We focus on utilizing modern tools like high-throughput sequencing to understand how pathogens cause disease and how our bodies react and protect against virus, bacteria, or parasites. This takes the form of a blend of biological understanding, computer science, and statistics. Work in our stream is a mix of analysis of raw data, understanding in biological context, and the development of statistical and machine learning approaches to better utilize the resulting data.
WHY IT MATTERS
Between the development of antibiotics and vaccinations, infectious disease is far less prevalent than in most of human history. Due to increasing population density and development of antibiotic resistance, untreatable infectious diseases are reappearing in developed countries. Transcriptomic analysis is essential in our ongoing search for new antibiotics and treatments. In addition, high-throughput sequencing technologies are more than just essential tools for modern biomedical research. Similar approaches are rapidly becoming integrated with clinical diagnosis and treatment in the field of personalized medicine.
WHAT YOU LEARN
Students all learn how high-throughput sequencing and transcriptomic experiments are performed and analyzed. In the process students learn basic scripting or programming skills (Python or R) to utilize state-of-the-art analysis toolkits for data processing, alignment, and analysis. Some students analyze genuine experimental data sets developing an understanding of how these large-scale experiments connect back to core biological concepts and conclusions and how to communicate those conclusions visually and verbally. Other students may choose to develop new analytical approaches using large-scale databases, statistics, or machine learning to understand how pathogens behave and evolve.