Events for April 19, 2021 - February 5, 2021

TITLE: "Clipper: p-value-free FDR control on high-throughput data from two conditions." ABSTRACT: High-throughput biological data analysis commonly involves identifying "interesting" features (e.g., genes, genomic regions, and proteins), whose values differ between two conditions, from numerous features measured simultaneously. The most widely-used criterion to ensure the analysis reliability is the false discovery rate (FDR). Existing bioinformatics […]

Associate Professor, UCLA Institute for Society and Genetics “TBD” Hosted by Paivi Pajukanta

TITLE: "Critical slowing down signals quasi-species extinction in an evolutionary model with environmental heterogeneity." ABSTRACT: Environmental heterogeneity can significantly affect the evolutionary dynamics of a population. A key parameter that determines the influence of heterogeneity is the migration rate--the rate at which organisms explore their heterogeneous environment. We study a model of a population which migrates between […]

TITLE: "Integrating metabolomics and fluxomics to study cancer metabolism in low glucose environments." ABSTRACT: Metabolomics and fluxomics are integral tools for quantitative metabolic analysis. By combining mass spectrometry, isotope tracing, and mathematical modeling, we can quantify metabolite concentrations and fluxes in cancer cells. Using these tools, we are investigating the coordination of bioenergetics, biosynthesis, and redox […]

Associate Professor, Department of Biochemistry and Biophysics, UCSF “TBD” Hosted by David Eisenberg

TITLE:“Longitudinal phenotyping of Severe Mental Illness from Electronic Health Records.” ABSTRACT:Electronic Health Records (EHRs) offer an exciting avenue for large-scale genetic studies of psychiatric disorders in globally diverse populations. As a potential source of inexpensive and longitudinal phenotypes, they are key for the study of severe mental illness trajectories. The Clinica San Juan de Dios […]

TITLE: "Population structure inference for biobank-scale data." ABSTRACT: Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. While a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and […]

Associate Professor of Genetics and of Computer Science, Yale School of Medicine “TBD” Hosted by Jason Ernst