Associate director, Gerstner Center for Cancer Diagnostics, Broad Institute “Ultrasensitive detection of minimal residual disease” Hosted by Jasmine Zhou

TITLE: "Genome-wide survey of mutations influencing protein abundances in yeast" ABSTRACT: Our understanding of how protein-level regulation is encoded in the genome and how protein abundances are affected by DNA sequence variation remains sparse. I will discuss our genetic screen based on large-scale pooled base editing that allows us to capture the effects of thousands […]

TITLE: "Unbiased analysis of single cell RNA sequencing data reveals previously uncharacterized heterogeneity in small cell lung cancer cell lines." ABSTRACT: Small Cell Lung Cancer (SCLC) accounts for approximately 13% […]

Associate Professor, Biostatistics & Bioinformatics, Center for Genomic and Computational Biology, Duke University “TBD” Hosted by Jason Ernst

TITLE: “Large uncertainty in individual PRS estimation impacts PRS-based risk stratification.” ABSTRACT: Large-scale genome-wide association studies have enabled polygenic risk scores (PRS), which estimate the genetic value of an individual […]

TITLE: "Constructing a Functional Interactome from Published Work to Identify Underrepresented and Integrative Effects of Neuroinflammatory Cytokines on Neural Excitability." ABSTRACT: Maintaining normal excitability is a key function of our […]

TITLE: "Metabolic dependencies of ecDNA and HSR focal amplification modes and plasticity." ABSTRACT: The focal amplification (FA) of genes that support the uncontrolled growth and proliferation of cells (i.e., oncogenes) […]

TITLE: "DoubletCollection: An R package that integrates cutting-edge computational doublet-detection methods." ABSTRACT: The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. There are several […]

TITLE: “Applications of generalized additive models and copulas to single-cell RNAseq computational method development: PseudotimeDE and scDesign2” ABSTRACT: Part 1: PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes […]

TITLE: “Integrating single cell omics and deep phenotype data to discover genes underlying cardiometabolic disorders” ABSTRACT: Obesity predisposes to cardiometabolic disorders (CMDs), such as type 2 diabetes, multiple dyslipidemias, and non-alcoholic fatty liver disease (NAFLD). We are interested in how cell-type level gene expression contributes to CMDs and impacts cross talk between cardiometabolic tissues. We […]