The Risks and Promises of Cloud Computing for Genomics

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Gwendoline Farmer
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1 The Risks and Promises of Cloud Computing for Genomics Laura Lyman Rodriguez, Ph.D. National Human Genome Research Institute P3G Privacy Summit: Data Sharing and Cloud Computing May 3, 2013

2 Key Elements of the NIH Mission

3 NIH and Data Sharing The Cloud We believe that data sharing is essential for expedited translation of research results into knowledge, products, and procedures to improve human health. The NIH endorses the sharing of final research data to serve these and other important scientific goals. - NIH 2003 Data Sharing Policy

4 The Promise Speed Agility, flexibility Control of resources Levels the field Cost Co-location of tools and data

5 The Risks Cost Lack of control Too many models Security risks Policy requirements Inability to compute

6 Putting the Pieces Together Science Capacity to analyze and interrogate massive data sets Powerful, innovative methodology development Standards and interoperability Policy & Regulations Consistency and coordination Clear requirements for different systems/data Accountability Process Resources and vision Division of Responsibilities (researchers & providers) Role for government

Regulations Consistency and coordination Clear requirements for different systems/data

7 Aim: Iterative Dialog and Process Norms

8 What is NIH Doing?

9 Data and Informatics Working Group acd.od.nih.gov/diwg.htm

10 Overarching Themes At a pivotal point: Risk failing to capitalize on technology advances Bordering on institutional malpractice Cultural changes at NIH are essential Aim to develop new opportunities for: Data sharing Data integration Data analysis Long-term NIH commitment is required

changes at NIH are essential Aim to develop new opportunities for:

11 Among the Major Problems to Solve 1. Locating the data 2. Getting access to the data 3. Extending policies and practices for data sharing 4. Organizing, managing, and processing biomedical Big Data 5. Developing new methods for analyzing biomedical Big Data 6. Training researchers who can use biomedical Big Data effectively

Organizing, managing, and processing biomedical Big Data 5.

12 NIH is Tackling the Big Data Problem New NIH Leadership Position: Associate Director for Data Scientific Science Data (ADDS) for Data Science (ADDS) New Internal NIH Governing/Oversight Body: Scientific Data Council (SDC) New Trans-NIH Initiative: Big Data to Knowledge (BD2K) Council (SDC) Big Data to Knowledge (BD2K)

Internal NIH Governing/Oversight Body: Scientific Data Council (SDC) New

13 BD2K: Four Programmatic Areas I. Facilitating Broad Use of Biomedical Big Data II. Developing and Disseminating Analysis Methods and Software for Biomedical Big Data III. Enhancing Training for Biomedical Big Data IV. Establishing Centers of Excellence for Biomedical Big Data

Developing and Disseminating Analysis Methods and Software for

14 Policy and Regulatory Considerations What are the governing policies & regulations for sharing data in the cloud? Data security (grant or contract?) Privacy Genomic data How do these policies translate to the cloud? Are the ethics questions to be considered different? What do we know? What don t we know?

) Privacy Genomic data How do these policies translate to the cloud?

15 Experience Implementing Data Sharing Identify specific data types that are ripe for archiving and sharing Link policy development to resource development ( If you build it, they will come... ) Use carrots and sticks (mandates accompanied by incentives for sharing, reduced burden) Work with relevant communities to develop data standards Develop plans for monitoring and enforcement

.. ) Use carrots and sticks (mandates accompanied by incentives for sharing, reduced burden)

16 Getting to What We Don t Know Genomic Data Sharing Cloud Working Group launched 2012 Define the issues Develop use cases Collect experience from around NIH Connect to BD2K Group 2 Identify activities & pilots across NIH Assess for best practices & governance models Consider issues of scale and cost-savings potential Ask the Experts!!

to BD2K Group 2 Identify activities & pilots across NIH Assess for best practices &

17 Learning from the Community 8 questions, 9 external experts (plus 8 NIHers) Overview How is cloud computing being used? What are the advantages over traditional methods security, storage, and analysis Limitations Role for the government Including considerations in selecting providers

What are the advantages over traditional methods security, storage,

18 Learning from the Community (con t) System issues How sustain capacity for innovation in consumable model Compare & contrast models: cost, access, control, innovation Human matters What standards need for data security, oversight What methods should support or manage accountability among collaborations (liability)

control, innovation Human matters What standards need for data security,

19 Suggested Roles for the Government Evaluate solutions to ensure meet minimum requirements Encourage and incentivize cloud solutions Define the details to enable interoperability and cross-data query Maintain vision focus Solicit input from the community Consider methods to support community-based testing of potential solutions Promote data representation using standard formats & vocabularies to accelerate discovery Encourage new structures when needed

focus Solicit input from the community Consider methods to support community-based testing of potential solutions

20 Finding the balance assessing, learning, adjusting

21 Questions? Thank you!! genome.gov

NIH and Biomedical Big Data. Eric Green, M.D., Ph.D. Director, NHGRI Acting Associate Director for Data Science, NIH

NIH and Biomedical Big Data. Eric Green, M.D., Ph.D. Director, NHGRI Acting Associate Director for Data Science, NIH NIH and Biomedical Big Data Eric Green, M.D., Ph.D. Director, NHGRI Acting Associate Director for Data Science, NIH Nature 2013 Myriad Data Types Genomic Other Omic Imaging Phenotypic Exposure Clinical