The document discusses the role of big data in life sciences, particularly in genomics and systems biology, highlighting its significance in understanding complex biological systems and improving disease treatment through precision medicine. It addresses the challenges associated with big data, including storage, organization, and analysis, while presenting various use cases in genomics and healthcare. Additionally, it explores solutions for effective data management, visualization, and dissemination within the field.

1. BigData in Life Sciences, Genomics and
Systems Biology
Harsha Rajasimha
9th September 2015

2. BigData in Life Sciences, Genomics and
Systems Biology
What is Bigdata
Life sciences, Genomics and systems biology
BigData in life sciences – where is it coming from?
Genomics and Systems Biology – BigData challenges.
Making sense of BigData
Future of BigData in genomics/SB

3. What is BigData?
http://www.ibmbigdatahub.com/infographic/four-vs-big-data

4. Medicine, Ag, Food Safety, Forensics, Epidemiology
concern the study of living organisms, including biology, botany,
zoology, microbiology, physiology, biochemistry, and related
subjects

5. Gen“omics”
Before 2000: One Gene at a time based on prior knowledge
Now: All ~25,000 genes at once – no prior knowledge necessary
5
 Genomics is a discipline in genetics that applies recombinant
DNA, DNA sequencing methods, and bioinformatics to
sequence, assemble, and analyze the function and structure of
genomes (the complete set of DNA within a single cell of an
organism).
 OMICS Characteristics
Comprehensiveness
Scale
High-throughput and low-cost technology development
Rapid data release
Social and ethical implications

6. Central Dogma of Molecular Biology
DNA RNA PROTEIN
Transcription
Reverse
Transcription
RNAi
Gene silencing
FUNCTION
Molecular biology is a branch of science concerning biological
activity at the molecular level. The field of molecular biology
overlaps with biology and chemistry and in particular, genetics and
biochemistry.

7. Systems biology is the study of systems of biological components,
which may be molecules, cells, organisms or entire species.
Living systems are dynamic and complex, and their behavior may
be hard to predict from the properties of individual parts.

8. Life Sciences BigData Examples
Measuring Instruments: LIMS, ELNs
Imaging: Molecular and cellular, pathology
Genomics: personal genomes, aggregate databases, gene
expression
Electronic Health Records: variety of information, phenotypes
Literature evidence: Pubmed, ISI web of science, Clinical trials,
WWW
Curated content: biochemical pathways, drug
response/resistance

9. Precision medicine is an emerging approach for disease
treatment and prevention that takes into account individual
variability in genes, environment, and lifestyle for each person.

10. • Biological
Mechanism
Example:
• Metabolic
Pathways

11. • Systems Biology Graphical Notation

12. ~4800
BigData Use Case 1: Disease Genes Discovery

13. Obama’s PMI: Need for Large Cohorts

15. 15
Genome / DNA Sequencing
•Game Changer 1: First
human genome sequenced
(2001)
•Game Changer 2: Human
genome costs <1K (2014)
Cost is decreasing at the square of Moore’s law:
Flatley’s Law
ability to digitize humans through
genomics and genotyping will
overturn the practice of medicine.
only a small fraction of 700,000
medical practitioners in US are upto
speed with genomics...

16. 16
Personal Genomes, Cancer Genomes, and other
genomes

17. Other BigData Use Cases
• Insurance: Cost benefit analysis of tests
• Health record- guided drug development
• Patient Stratification – drug response based on DNA
• Measuring Instruments
• FDA Office of Regulatory affairs 14 labs, 1000+ instruments,
data
• 1000genomes, 100K genomes UK, PMI million cohort
– genomes + phenomes
• Biochemical pathways: Reactome, KEGG, etc.

18. Lot of BigData – not enough analysis
http://searchhealthit.techtarget.com/tip/Big-data-in-health-care-
Lots-of-data-but-not-enough-analysis

19. Solutions to BigData
Data Storage
Data Organization
Data Analytics
Data Movement
Data Exchange
Data Visualization
BD2K: BigData is worthless
Data Dissemination: Open data, Free data, Open Govt

20. Data Storage
http://blog.zoolz.com
Cloud, Enterprise
storage, Planning for
tomorrow

21. Data Management, Retrieval
• Relational databases
• No-SQL databases
• Data use cases
http://www.tomsitpro.com/articles/rdbms-sql-cassandra-dba-
developer,2-547-2.html

22. Data Organization and DBs
http://www.enaxisconsulting.com/images/userfiles/image
s/MDM-Chart640Final(1).jpg
Business Cases, Continuity,
Infrastructure, Governance
E.g., NIH public data repositories

23. Data Analytics

24. Data Movement / Transfer
• How is the data expected to move within and outside
the infrastructure?
• Bring data to analysis tools or tools to data?
• From Archives to compute storage, From local to cloud,
• Network bandwidth considerations
• DAS, NAS, SAN, Tapes, RAM, Cache

25. Data Integration and Exchange
• APIs: Application programming
interfaces for on-demand
access
• XML: SBML
• EMRs
• RDF/OWL: BioPAX
• FastQ
• DICOM
• Commons: genomics, cancer,
etc.

26. Data Visualization
Circos plot
Health InfoScape: 7+ million EMRs, SENSEable city lab at
MIT and GE HealthyMagination. Freq of co-occurrence
of medical conditions.
Alignment of 8 yersinia whole bacterial
genomes

27. BD2K: BigData is worthless

28. Data Dissemination
Dissemination

29. Discussion!
Lets do it with BigData!
harshakarur@gmail.com