MAY 10, 2017 06:00 AM PDT
Developing bioinformatics approaches to understand the functional consequences of gene expression variability
Presented at the Genetics and Genomics 2017 Virtual Event
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: CME | P.A.C.E. CE | Florida CE
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Speakers:
  • Assistant Professor, Department of Systems & Computational Biology, Assistant Professor, Department of Epidemiology & Population Health, Albert Einstein College of Medicine
    Biography
      Jessica Mar is an Assistant Professor at the Albert Einstein College of Medicine in the Department of Systems and Computational Biology in the Bronx, New York. The Mar lab investigates how variability of gene expression informs our understanding of how genes and pathways are dysregulated in disease. Dr. Mar received her PhD in Biostatistics from Harvard University in 2008. Previously she was a postdoctoral research fellow at the Dana-Farber Cancer Institute in Boston and a visiting scientist at the European Bioinformatics Institute in the United Kingdom. She is a recipient of a University of Queensland medal and an American-Australian Fulbright award. She is currently an Associate Editor of Genomics.

    Abstract:

    When studying the transcriptome, most of our inferences revolve around changes in average expression. However, more recent examples have demonstrated that analysis of the variability of gene expression can also highlight important regulators too. In this talk, I outline some of the bioinformatics methods my lab has developed to investigate the functional consequences of gene expression variability to understand transcriptional regulation. I present a recently published method called pathVar, which provides functional interpretation of variability changes at the level of pathways and gene sets. Application of pathVar to cancer patient cohort data will be shown to demonstrate the utility of this method. I also describe a method based on the third statistical moment, skewness, to model heterogeneously expressed genes. Using skewness-based metrics, we can uncover new genes with regulatory roles in cancer, as well as those that vary with DNA methylated loci. Collectively, this series of related studies outline the value


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