AUG 22, 2013 02:00 PM PDT

The Road to Genomic Medicine is Paved with Data and Information

Speakers
  • Professor of Computational Biology and Bioinformatics, Chair of the Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute
    Biography
      John Quackenbush is Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics at the Harvard TH Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. John's PhD was in Theoretical Physics, in 1992 he received a fellowship from the National Institutes of Health to work on the Human Genome Project, which led him from the Salk Institute to Stanford University to The Institute for Genomic Research (TIGR) before moving to Harvard in 2005. He currently directs the Computational Biology and Quantitative Genetics MS program and is PI of the BD2K Training Grant at HSPH. John's research uses massive data from DNA sequencing and other assays to model functional networks in human cells. By comparing networks between groups of individuals, he has found new drug targets, explored chemotherapy resistance, and investigated differences between the sexes. He has received numerous awards for his work, including recognition in 2013 as a White House Open Science Champion of Change. He is also the co-founder of Genospace, a precision medicine software company that was purchased by the Hospital Corporation of America in 2017.

    Abstract:
    Since the introduction of second-generation DNS sequencing technologies in 2007, the cost of genome sequencing has been consistently by 33% per quarter, with the $1000 genome arriving in 2012 and the $100 genome not far off. As DNA sequencing increasingly becomes a commodity, biomedical research is rapidly evolving from a purely laboratory science to an information science in which the winners in the race to cure disease are likely to be those best able to collect, manage, analyze, and interpret data. Here I will provide an overview of the approach we have been developing to deal with the challenge of personal genomic data, including integrative approaches to data analysis and the creation of data portals focused on addressing the most common use cases presented by different user constituencies. By effectively collecting genomic and clinical data and linking information available in the public domain, we have made significant advances in uncovering the cellular networks and pathways that underlie human disease and building predictive models of those networks that may help to direct therapies.

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