AUG 22, 2013 9:00 AM PDT

Management and Analysis of Pre-implantation Genetic Diagnosis Data

C.E. Credits: CE
Speaker
  • Director Bioinformatics, Children's Hospital of Philadelphia, University of Pennsylvania
    Biography
      Deanne Taylor joined DBHI in 2014 as the Bioinformatics Director. Deanne oversees the design, development, and implementation of high performance innovative bioinformatics workflows and provide technical leadership and supervision of a multidisciplinary bioinformatics team at CHOP. Previously, she was Director of Bioinformatics with Reproductive Medicine Associates and Assistant Professor with the Department of Obstetrics, Gynecology and Reproductive Medicine at Rutgers Robert Wood Johnson Medical School. Prior to that she worked for Harvard School of Public Health as a Research Scientist and served several years as the Program Director of the Graduate Program in Bioinformatics at Brandeis University. She also has experience working in the pharmaceutical industry. Deanne obtained her PhD in Biophysics from the University of Michigan, Ann Arbor, and completed a postdoctoral fellowship at Pfizer. Her background is in biophysics, bioinformatics, computational biology and structural biology with emphasis on human genetics and translational medicine.

      Deanne's main areas of research are in the development of mathematical and computational methods to better understand biological variation and the genetic contribution to disease, coupling clinical information with high-dimensional biomedical data from next-gen sequencing, microarray, PCR, and proteomics experiments.

    Abstract
    During IVF procedures pre-implantation genetic diagnosis (PGD) allows for genetic profiling of embryos prior to replacement. Most PGD procedures during IVF are performed to select chromosomally normal embryos as comprehensive chromosome screening (CCS), and in a smaller number of cases PGD is used for embryo selection against known genetic disease variants carried by the parents. Genomic technologies utilizing PCR, microarray, and second-generation sequencing methods are currently being used for PGD data analysis in the laboratory. With decreasing costs in second-generation sequencing technologies coupled with increased demand for PGD, adoption of "PGD-seq" methods for CCS and genetic selection will face significant future challenges in data management, analysis and information delivery to clinicians. One of our goals is to provide secure, integrated environments for clinicians to use genetic information to inform treatment strategies. I discuss challenges and strategies for storing and integrating biomedical data with other private and public data repositories. I discuss process and warehouse design, analysis methods and implementation strategies for PGD and genetics data. I discuss how we deliver and visualize PGD data for clinicians and to researchers in clinical and basic science.

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