New technologies and approaches in drug discovery require new approaches to data management and analysis. High-content screening (HCS) generates much richer data than conventional biochemical assays with multiple measurements and endpoints at the whole-well and single-cell level. This multidimensional data requires different tools, such as those commonly used for microarray gene expression and flow cytometry analyses. New approaches to drug discovery such as RNA interference and phenotypic or 'black-box' screening also require different approaches to hit selection and data visualization from conventional high-throughput screening. This talk will describe novel data management and analysis tools that adress these needs using a combination of internally developed solutions, commercial and open source software. These tools enable researchers to analyze new types of experiments and to extract new information from existing data sets.
Donald G. Jackson, PhD
Sr. Research Investigator II,
Applied Genomics Department,
Bristol-Myers Squibb Research & Development
Dr. Jackson has used high-content screening (HCS) extensively to support target discovery, target validation, compound mechanism-of-action studies, and toxicology projects at BMS. He also led the development of HCS Road, an innovative management and analysis solution for HCS data. Previously he was a member of the bioinformatics group at BMS where he helped identify novel oncology targets using model organism genetic screens. He received his doctorate in Biochemistry, Cellular and Molecular Biology from The Johns Hopkins University and was a post-doctoral felow at the Massachusetts General Hospital where he worked on bioinformatics for the Zebrafish Genome Project.