Integrating large-scale genomics data to decipher the role of the noncoding genome in disease

September 22, 2022
3:00-4:15 pm ET
Cummings 270, Zoom
Speaker: Pawel Pryzytycki, Boston University
Host: Lenore Cowen

Abstract

Many people suffer from diseases with poorly understood and complex genetic underpinnings and no known causal mechanisms. These diseases are often driven by subtle changes in gene regulation rather than in the genes themselves and manifest only in a small subset of cells. While breakthroughs in high- throughput genetic sequencing technology are now making it possible to unravel the genetics of these diseases, these data are often sparse, high- dimensional, and heterogeneous across individuals and cells. Addressing these challenges requires algorithms for integrating and interpreting large-scale multi-modal genomics data. In order to resolve gene regulation to highly specific cell types, we developed a network-based integration of single-cell and bulk data measurements. This approach is robust to sparse annotations and noise and can be used to map diseases to specific cell types through their regulatory elements. We further show that the model can be easily extended to incorporate hierarchical cell type data. Overall, my talk will show how interpretable and intuitive methods for combining data often lead to the most biologically meaningful results.

Bio:

I am an Assistant Professor in the Faculty of Computing & Data Sciences at Boston University. My lab develops algorithms for the analysis and interpretation of large-scale genomics data, with a focus on the role of the non-coding genome in development and disease. Before starting my own lab I was a Bioinformatics Fellow in Dr. Katie Pollard's lab at the Gladstone Institutes at UCSF. The focus of my research was investigating the regulatory effects of noncoding variants in disease. Prior to that, I was a PhD student and NSF Graduate Research Fellow in Dr. Mona Singh's lab in the Department of Computer Science at Princeton University where my research focused on the use of algorithms and networks for cancer genomics.

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Meeting ID: 960 3825 1227

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Meeting ID: 960 3825 1227

Passcode: see colloquium email