AUG 22, 2013 02:00 PM PDT

The Road to Genomic Medicine is Paved with Data and Information

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  • Professor of Computational Biology and Bioinformatics, Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute
      John Quackenbush received his PhD in theoretical physics from UCLA in 1990. Following a physics postdoc, he received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project, spending two years at the Salk Institute and two years at Stanford University working in genomics and computational biology. In 1997 he moved to The Institute for Genomic Research (TIGR), pioneering expression analysis. He joined the Dana-Farber Cancer Institute and the Harvard School of Public Health in 2005, and works reconstruction of gene networks that drive the development of diseases. In 2012 he and Mick Correll co-Founded GenoSpace, a company that develops software tools to enable precision medicine applications.
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    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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