Computational approaches to studying patient mutations in transcriptional activation domains
Max Staller, Assistant Researcher
Center for Computational Biology
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
Our group has developed simple and highly predictive models for transcriptional activation domains. The goal of the project is to identify human activation domains enriched for patient mutations.
Role: Learn to work with databases of human genetic variation. Identify activation domains with excess variation. You will practice posing a hypothesis, testing it with the available data and revising the hypothesis. There will be an opportunity for explorative, self-directed data analysis.
Qualifications: Coursework in introductory biology. Computer programming in python is required. Enthusiasm. Curiosity. Any coursework in statistics will be helpful but is not required. If you have any experience working with Savio or another cluster, please describe it.
Day-to-day supervisor for this project: Sanjana R Kotha
Hours: 12 or more hours
Biological & Health Sciences