One of the most challenging problems in bioscience is data integration. From subcellular studiesto population simulations, we are faced with large volumes of difficult to integrate data. Presentation includes tips on getting started in big data bioscience.

1. Big Data, Bioscience
and the Cloud
Dan Sullivan
June 25, 2015
BioCatalyst: Cloud Computing in Bioscience
Oregon Bioscience Association

2. Overview
• Background
• Varieties of Big Data in Bioscience
• Continuous learning about Big Data & Cloud
• Making Connections

3. My Background
 Data Architect / Engineer
 NoSQL and relational data modeler
 Big data
 Analytics, machine learning and text mining
 Cloud computing
 Computational Biologist
 Author
 No SQL for Mere Mortals
 Contributor to TechTarget

4. Overview
• Background
• Varieties of Big Data in Bioscience
• Continuous learning about Big Data & Cloud
• Making Connections

5. Big Data Challenges in Bioscience
Volume
Velocity
Variety
Integration

6. Varieties of Big Data in
Bioscience
Subcellular – Genetics and
Proteomics
Cellular – Metabolic and
Signaling Pathways
Organism – Disease, Medicine,
Insurance
Populations – Epidemiology,
Social Networks

7. Genetics and Proteomics
• Genetic Sequencing
• Order of nucleotides in DNA
• Most DNA is common across species
• Many genes code proteins
• Some variants associated with disease
• Which ones?
• Proteomics
• Structure and function of proteins
• Variation in protein sequence and
structure associated with disease
• Which ones? In what context?
Images: http://www.masimo.it/hemoglobin/anemia.htm, https://en.wikipedia.org/wiki/DNA

8. Pathways
• Metabolic Pathways
• Series of chemical reactions
• Coordinated to produce
reactants
• Choreography of molecules
• Signaling Pathways
• Molecules on cell surface detect
changes in environment
• Cascade of reactions to change
state of cell
• Choreography of molecules
• How do they interact?

9. • Early 1950s
Korean War
autopsies
2012-2016 Genomic and Proteomic Studies
1985-1998 Pathology Studies - Pathodeterminants of
Atherosclerosis in Youth (PDAY) study
Disease - Atherosclerosis

10. Healthcare
• Genetics and Disease
• Post-Approval Drug Efficacy
• Discovering and Retrieving Medical
Information
• Comparative Quality

11. Populations
• Infectious Disease Spread
• How fast will disease spread?
• What countermeasures are
effective?
• What is the morbidity and
mortality?
• Simulation
– Synthetic population
– Model interactions
– Probabilistic

12. Why Cloud for Big Data in
BioScience?
• Scalability
• Access to compute and memory optimized
virtual machines
• Virtually unlimited storage
• Speed
• Many bioscience computations highly
parallel
• Minimize time to analyze, lower IT
overhead
• Cost
• AWS Spot Instances
• Google Pre-emptible VMs

13. Overview
• Background
• Varieties of Big Data in Bioscience
• Continuous learning about Big Data & Cloud
• Making Connections

14. Continuous Learning
• Coursera
• Cloud Computing Concepts
• Bioinformatics: Life Sciences on Your
Computer
• edX
• Introduction to Statistics
• Introduction to Biology
• Principles of Biochemistry
• Rackspace CloudU
• You Tube
• Big Data Vendors
• MapR
• Cloudera
• HortonWorks
• DataStax
• Data Bricks
• Trade Publications
– TechTarget
• SearchAWS
• SearchCloudComputing
• SearchCloudSecurity
– Health Data Management
– Harvard Business Review

15. Overview
• Background
• Varieties of Big Data in Bioscience
• Continuous learning about Big Data & Cloud
• Making Connections

16. LinkedIn Groups

18. Final Thoughts
• Great time to get into Biosciences and
Big Data
• Don’t be intimidated if it’s been a
while since you’ve studied biology –
we are all constantly learning in this
field
• Network online and in person
• Take advantage of free resources
• Courses
• Cloud
• AWS Free Tier
• MAPR Hadoop On Demand Training
• Connect with me on LinkedIn
• https://www.linkedin.com/in/dansull
ivanpdx
• Join me at a Meetup
• Dan.sullivan@cambiahealth.com