Challenges and Solutions for Genomics in the Age of Big Data

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Leading researchers in data science and genomics are recommending strategies to help genomic scientists better manage, share, analyze and archive massive research and clinical data sets in an effort to ensure that the big data explosion results in better health outcomes and faster research discoveries.

The recommendations are presented in the first white paper published by the National Consortium for Data Science (NCDS), a public-private partnership launched in North Carolina to push the development of data science, foster better data science education, and encourage the development of new data-related products and services.

The white paper, titled Data to Knowledge: Genomes to Health, resulted from the first NCDS Leadership Summit, held last April in Chapel Hill, NC. The summit brought together researchers in genomics and data science to discuss the greatest data challenges in genomic sciences and to brainstorm on possible solutions to those challenges. About 70 leaders in their fields attended the summit and identified the genomic science data challenges in the areas of data provenance, collection and management, delineation of phenotypes, adjudication of genomic variants, biostatistics and bioinformatics, data sharing, and ethics and the law.

Recommendations for dealing with those challenges included:

  • Fostering more interdisciplinary collaborations.
  • Pushing for widespread adoption of advanced analytical approaches, tools and data standards.
  • Incentivizing data sharing while keeping data safe and secure.
  • Developing automated, easy to use clinical decision support systems.
  • Launching new education and training in big data and information technology.
  • Clarifying legal and ethical issues, such as how to handle incidental findings and how to distinguish between fair use and misuse of genomic data.
The Genomic Data Landscape

The Genomic Data Landscape

Ten authors contributed to the paper: Stan Ahalt, PhD, Renaissance Computing Institute (RENCI) and the University of North Carolina (UNC) at Chapel Hill; Chris Bizon, PhD, RENCI; Jim Evans, MD, PhD, UNC School of Medicine; Yaniv Erlich, PhD, The Whitehead Institute; Geoffrey S. Ginsburg, MD, PhD, Duke University Institute for Genome Sciences & Policy; Ashok Krishnamurthy, PhD, RENCI; Leslie Lange, PhD, UNC-Chapel Hill; Dan Maltbie, Annai Systems; Dan Masys, University of Washington; Charles Schmitt, RENCI; and Kirk Wilhelmsen, UNC School of Medicine

The National Consortium for Data Science formed a year ago to provide a foundation for advancing data science research, educating the next generation of data scientists, and translating data innovations into economic opportunity. Membership is open to businesses, nonprofits, academic institutions and government agencies.


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