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: 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 Albert Einstein College of Medicine in the Department of Systems and Computational Biology in the Bronx, New York. The focus of the Mar lab is to understand how variability in gene expression contributes to the regulation of cellular phenotypes. Around the topic of variability, her work involves applications in single cell genomics, stem cells, genetics and cancer biology. Jessica Mar received her Bachelor of Science degree in Mathematics at the University of Queensland in Brisbane, Australia and First Class Honors in Statistics in 2002. She got 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 UK. Since July 2016, Dr. Mar holds a joint appointment with the Australian Institute for Bioengineering and Nanotechnology at the University of Queensland, Australia as a Group Leader.

    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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