The Role of Automated Function Prediction in the Era of Big Data and Small Budgets

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The Role of Automated Function Prediction in the Era of Big Data and Small Budgets Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health

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Keynote presentation at the Automated Functional Prediction Special Interest Group, ISMB Conference, July 11, 2014 Boston, MA USA.

Transcript of The Role of Automated Function Prediction in the Era of Big Data and Small Budgets

  • The Role of Automated Function Prediction in the Era of Big Data and Small Budgets Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health
  • A View from the Funding Agencies
  • It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair
  • Roughly translated A time of great (unprecedented?) scientific development but limited funding A time of upheaval in the way we do science
  • From a funders perspective A time to squeeze every cent/penny to maximize the amount of research that can be done A time for when top down approaches meet bottom up approaches
  • Top Down vs Bottom Up Top Down Regulations e.g. US: Common Rule, FISMA, HIPPA Data sharing policies GWAS Genome data Clinical trials Digital enablement Moves towards reproducibility Bottom Up Communities emerge and crowdsource Collaboration Data shared Open source software Common principles Standards
  • A Time for New Models Source Michael Bell
  • And This May Just be the Beginning Evidence: Google car 3D printers Waze Robotics From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
  • Consider This an Opportunity Look at the value of data Derive new business models Look for new efficiencies Foster best practices Foster collaboration .
  • It is the age when functional annotation is in the greatest demand for science.. It is the age when the rewards outside academia are greater than the rewards inside
  • Associate Director for Data Science Commons Training Center BD2K Modified Review Sustainability* Education* Innovation* Process Cloud Data & Compute Search Security Reproducibility Standards App Store Coordinate Hands-on Syllabus MOOCs Community Centers Training Grants Catalogs Standards Analysis Data Resource Support Metrics Best Practices Evaluation Portfolio Analysis The Biomedical Research Digital Enterprise Communication Collaboration rogrammatic Theme Deliverable Example Features ICs Researchers Federal Agencies International Partners Computer Scientists Scientific Data Council External Advisory Board * Hires made
  • Innovation Big Data to Knowledge BD2K Centers of excellence Software catalog Data catalog Software initiatives Standards Training
  • Sustainability and Sharing: The Commons Data The Long Tail Core Facilities/HS Centers Clinical /Patient The Why: Data Sharing Plans The Commons Government The How: Data Discovery Index Sustainable Storage Quality Scientific Discovery Usability Security/ Privacy Commons == Extramural NCBI == Research Object Sandbox == Collaborative Environment The End Game: KnowledgeNIH Awardees Private Sector Metrics/ Standards Rest of Academia Software Standards Index BD2K Centers Cloud, Research Objects,
  • What The Commons Is and Is Not Is Not: A database Confined to one physical location A new large infrastructure Owned by any one group Is: A conceptual framework Analogous to the Internet A collaboratory A few shared rules All research objects have unique identifiers All research objects have limited provenance
  • What Does the Commons Enable? Dropbox like storage The opportunity to apply quality metrics Bring compute to the data A place to collaborate A place to discover 1024x825.png
  • [Adapted from George Komatsoulis] One Possible Commons Business Model HPC, Institution
  • What Are the Benefits to Those Doing Functional Annotation? Open environment in which to test new ideas better for crowdsourcing Opportunity to gain resources to run annotation pipelines Opportunity to collaborate through provision of open APIs Better characterization and accessibility to annotation methods
  • Commons Pilots Define a set of use cases emphasizing: Openness of the system Support for basic statistical analysis Embedding of existing applications API support into existing resources Evaluate against the use cases Review results & business model with NIH leadership Design a pilot phase with various groups Conduct pilot for 6-12 months Evaluate outcomes and determine whether a wider deployment makes sense Report to NIH leadership summer 2015
  • Some Acknowledgements Eric Green & Mark Guyer (NHGRI) Jennie Larkin (NHLBI) Leigh Finnegan (NHGRI) Vivien Bonazzi (NHGRI) Michelle Dunn (NCI) Mike Huerta (NLM) David Lipman (NLM) Jim Ostell (NLM) Andrea Norris (CIT) Peter Lyster (NIGMS) All the over 100 folks on the BD2K team
  • NIHNIH Turning Discovery Into HealthTurning Discovery Into Health