Keynote at the IEEE International Conference on Bioinformatics and Biomedicine, Washington DC, November 10, 2015. https://cci.drexel.edu/ieeebibm/bibm2015/

1. Big Data in Biomedicine – An NIH
Perspective
Philip E. Bourne Ph.D., FACMI
Associate Director for Data Science
National Institutes of Health
philip.bourne@nih.gov
IEEE BIBM Nov 10 2015, Washington DC
http://www.slideshare.net/pebourne

2. Perspective
 Structural bioinformatics researcher
 Former custodian of the PDB
 Obsessive about open science e.g., PLOS
 NIH-wide responsibility for developments in
data science – responding to the disruption

3. Bioinformatics 2015 31(1):146-50

4. Big Data in Biomedicine…
This speaks to something more
fundamental that more data …
It speaks to new methodologies, new
skills, new emphasis, new cultures,
new modes of discovery …

5. Consider this change from my own
career experience ….

6. The History of Computational
Biomedicine According to Bourne
1980s 1990s 2000s 2010s 2020
Discipline:
Unknown Expt. Driven Emergent Over-sold A Service A Partner A Driver
The Raw Material:
Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated
The People:
No name Technicians Industry recognition data scientists Academics
Searls (ed) The Roots in Bioinformatics Series PLOS Comp Biol

7. Premise:
We are entering a period of disruption
in biomedical research and we should
all be thinking about what this means
to bioinformatics & biomedicine
http://i1.wp.com/chisconsult.com/wp-
content/uploads/2013/05/disruption-is-a-
process.jpg
http://cdn2.hubspot.net/hubfs/418817/disruption1.jpg

8. We are at a Point of Deception …
 Evidence:
– Google car
– 3D printers
– Waze
– Robotics
– Sensors
From: The Second Machine Age: Work, Progress,
and Prosperity in a Time of Brilliant Technologies
by Erik Brynjolfsson & Andrew McAfee

9. Disruption: Example - Photography
Digitization
Deception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volume,Velocity,Variety
Digital camera invented by
Kodak but shelved
Megapixels & quality improve slowly;
Kodak slow to react
Film market collapses;
Kodak goes bankrupt
Phones replace
cameras
Instagram,
Flickr become the
value proposition
Digital media becomes bona fide
form of communication

10. Disruption: Biomedical Research
Digitization of Basic &
Clinical Research & EHR’s
Deception
We Are Here
Disruption
Demonetization
Dematerialization
Democratization
Open science
Patient centered health care

11. Disruptive Features: Sustainability
Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830

12. Disruptive Features:
Reproducibility
Changing Value of Scholarship (?)

13. “And that’s why we’re here today. Because something
called precision medicine … gives us one of the greatest
opportunities for new medical breakthroughs that we
have ever seen.”
President Barack Obama
January 30, 2015
Disruptive Features – New Science

14. An Example of That Promise:
Comorbidity Network for 6.2M Danes
Over 14.9 Years
Jensen et al 2014 Nat Comm 5:4022

15. What Are Some General Implications
of Such a Future?
 Open collaborative science becomes of increasing
importance
 The value of data and associated analytics becomes
of increasing value to scholarship
 Opportunities exist to improve the efficiency of the
research enterprise and hence fund more research
 Cooperation between funders will be needed to
sustain the emergent digital enterprise
 Current training content and modalities will not match
supply to demand
 Balancing accessibility vs security becomes more
important yet more complex

16. How Should We Respond?
 Funders: Encourage change and facilitate an orderly
transition
 Academic Leaders: Respond and facilitate a cultural
shift
 Developers: Develop working environments that are
more adaptive and capable of answers questions in a
more efficient and hopefully accurate way
 Users: Use the above environments
 Publishers: Move beyond papers

17. Take an Example That is Central to
What We Do
Molecular Graphics
Is It Optimal for Today’s Science?
http://upload.wikimedia.org/wikipedia/commons/2/2e/M
olecular-Graphics-GRIP-75-Console.jpg

18. Good News/Bad News
 Good News:
– It is harder to think of a
more powerful way to
comprehend complex
data
– It has excited
generations to the
promise of science
– It has adapted to
changing technologies
 Bad News:
– It is not an
adaptive/extensible
environment
– It is not a collaborative
environment
– It is not an integrative
environment
– It is the curse of the
ribbon
BMC Bioinformatics 2005, 6:21

19. 1. A link brings up figures
from the paper
0. Full text of PLoS papers stored
in a database
2. Clicking the paper figure retrieves
data from the PDB which is
analyzed
3. A composite view of
journal and database
content results
Is a database
really different
than a
biological
journal?
PloS Comp Biol
2005 1(3) e34
4. The composite view has
links to pertinent blocks
of literature text and back to the PDB
1.
2.
3.
4.
The Knowledge and Data Cycle

20. Take Another Example:
The Raw Material of Structural
Bioinformatics
Is this the optimal starting point anymore?

21. Do data resources including the PDB
best serve the needs of the user at
this point?

22. Good News/Bad News for the PDB in
this Changing Landscape
 Bad News:
– Interface complex and
uni-data oriented
– Data accessible;
methods accessible (sort
of); but not together
– Significant redundancy in
services offered
 Good News:
– Annotation!
– Demand is increasing
– Integrated with other
data types
– Restful services

23. General Problem Statement:
How to insure a high quality
annotated data source that provides
the optimal environment for
accessibility, integration and analysis
by a broad community of diverse
users?

24.  The Commons is a shared virtual space which is
FAIR:
– Find
– Access (use effectively)
– Interoperate
– Reuse
 An environment to find and catalyze the use of
shared digital research objects
The Commons
Concept

25. The Developer or User Defines the
Environment from the Appropriate
Building Blocks

26. Public Beacons
Host Content
AMPLab 1000 Genomes Project
Broad Institute ExAC
Curoverse PGP, GA4GH Example Data
EBI
1000 Genomes Project, UK10K, GoNL, EVS,
GEUVADIS, UMCG Cardio GenePanel
Google
1000 Genomes Project, Phase III, Illumina Platinum
Genomes
ISB Known VARiants
NCBI NHLBI Exome Sequence Project
OICR 55 cancer datasets
SolveBio 56 public datasets
UCSC ClinVar, LOVD, UniProt
University of Leicester Cafe CardioKit, Cafe Variome Central
WTSI IBD, Native American, Egyptian, UK10K
Over ?? public datasets beaconized across 21 institutions
10s thousands of individuals

28. The Commons
Components
 Computing environment
– cloud or HPC (High Performance Computing)
– supports access, utilization, sharing and storage of
digital objects.
 Methods for Interoperability
– enables connectivity, shareability and interoperability
between digital objects.
 Digital object compliance model
– describes the properties of digital objects that
enables them to be discoverable and shareable.

29. The Commons
Components

30. BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
DDICC
Software
Standard
s
Infrastructure - The
Commons
Labs
Labs
Labs
Labs

31. The ability to store and share and
compute on digital research objects
 Especially useful for large data sets that
are not easily computed locally
Scalable and Elastic
Pay per use - Cost effective
An environment that fosters
collaboration
The Commons
Computing Environment: Cloud

32. Commons - Pilots
 The Cloud Credits - business model
 BD2K Centers
 MODs (Model Organism Databases)
 HMP Data and tools available in the cloud
 NCI Cloud Pilots & Genomic Data
Commons

33. The PDB in the Commons
 Components:
– Annotated collection of data files
– API’s to access these data files
– Example methods using these APIs
 Potential outcomes
– Nothing happens?
– A new breed of developer starts to use PDB data in new
ways ?
– The casual user has a broader set of services that
previously?
– Quality declines/increases?

34. I not only use all the brains
I have, but all I can borrow.
– Woodrow Wilson

35. ADDS Team
BD2K Representatives

36. NIHNIH……
Turning Discovery Into HealthTurning Discovery Into Health
philip.bourne@nih.gov
https://datascience.nih.gov/
http://www.ncbi.nlm.nih.gov/research/staff/bourne/