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Statistical approaches for mechanistic biological discovery with genomics

Julia Salzman – Professor, Stanford

Thu, 24-Feb-2022 / 4:00pm / Zoom: https://stanford.zoom.us/meeting/register/tJckfuCurzkvEtKKOBvDCrPv3McapgP6HygJ

Talk

• Video

Abstract

Sequencing of the human genome more than a decade ago was a transformative event in biology, and required mammoth resources: millions of dollars and thousands of people. Today, technology enables billions of DNA sequences from a single biological sample to be read out for minimal cost. This technology, and its widespread applications have transformed much of biology, genetics and medicine into a digital, data-driven science. It presents new opportunities for foundational biological discovery through development and formalizing them as statistical problems and designing approaches to solve them, including efficient computational solutions. The combination of digital biological data, statistical formalism and computational science promise to significantly advance many areas of biology ranging from organizing principles of cellular development to unraveling mechanisms of microbial growth and communication to biomarker applications. In this talk, I will describe some of the questions and work in these areas. I will give some basic biological background but much of the talk will assume familiarity with DNA and RNA as well as sequencing.

Bio

Julia Salzman is an Associate Professor in the Department of Biomedical Data Science, Biochemistry and Statistics (by Courtesy). She received her A.B. in Mathematics from Princeton University Magna Cum Laude and Ph.D. from Stanford University in the Department of Statistics supervised by Dr. Persi Diaconis. After a year on the faculty in the Department of Statistics at Columbia University, she returned to Stanford to study genomics. She joined the Stanford faculty in 2013. As a postdoctoral scholar in Dr. Patrick Brown’s lab, Dr. Salzman developed statistical algorithms that led to the discovery of a ubiquitous expression of circular RNA missed by other computational and experimental approaches for decades. Dr. Salzman’s research is funded by the NIH and NSF, and has been recognized by awards including an Alfred P. Sloan Fellowship in Computational Biology; a McCormick-Gabilan Fellowship, an NSF CAREER Award. Her research spans the interface of statistical methodology and genomics aiming to use data driven experiments to uncover organizing principles of biological regulation, historically focused on RNA processing.