Slides of the 2015 Bio Data World Congress show how our analyzegenomes.com services are combined to support precision medicine in the context of modern oncology treatment.

1. Big Data in Genomics: Opportunities and Challenges
Dr. Matthieu-P. Schapranow
Bio Data World Congress, Cambridge, UK
Oct 22, 2015

2. ■ Online: Visit we.analyzegenomes.com for latest research results, tools, and news
■ Offline: Read more about it, e.g. High-Performance In-Memory Genome Data Analysis:
How In-Memory Database Technology Accelerates Personalized Medicine, In-Memory
Data Management Research, Springer, ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for “Festival of Genomics” Jan 19-21, 2016 in London, UK
Important things first:
Where do you find additional information?
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
2

3. What is the Hasso Plattner Institute, Potsdam, Germany?
Schapranow, HPI, Oct
13, 2015
Analyze Genomes: A
Federated In-Memory
Database Computing
Platform
3

4. Prof. Dr. h.c. Hasso Plattner
■ Research focuses on the technical aspects of enterprise
software and design of complex applications
□ In-Memory Data Management for Enterprise
Applications
□ Enterprise Application Programming Model
□ Scientific Data Management
□ Human-Centered Software Design and Engineering
■ Industry cooperations, e.g. SAP, Siemens, Audi, and EADS
■ Research cooperations, e.g. Stanford, MIT, and Berkeley
Hasso Plattner Institute
Enterprise Platform and Integration Concepts Group
Schapranow, HPI, Oct
13, 2015
Analyze Genomes: A
Federated In-Memory
Database Computing
Platform
4
Partner of Stanford
Center for Design
Research
Partner of MIT in
Supply Chain
Innovation and
CSAIL
Partner at
UC Berkeley
RAD / AMP Lab
Partner of SAP
AG

5. ■ Since 2009 Program Manager E-Health & Life Sciences
■ 2006-2014 Strategic Projects SAP HANA
■ Visiting Scientist at V.A., Boston, MA and Charité, Berlin
■ Software Engineer by training (PhD, M.Sc., B.Sc.)
With whom are you dealing?
Schapranow, HPI, Oct
13, 2015
Analyze Genomes: A
Federated In-Memory
Database Computing
Platform
5

6. ■ Patients
□ Individual anamnesis, family history, and background
□ Require fast access to individualized therapy
■ Clinicians
□ Identify root and extent of disease using laboratory tests
□ Evaluate therapy alternatives, adapt existing therapy
■ Researchers
□ Conduct laboratory work, e.g. analyze patient samples
□ Create new research findings and come-up with treatment alternatives
The Setting
Actors in Oncology
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
6
Big Data in Genomics:
Opportunities and
Challenges

7. IT Challenges
Distributed Heterogeneous Data Sources
7
Human genome/biological data
600GB per full genome
15PB+ in databases of leading institutes
Prescription data
1.5B records from 10,000 doctors and
10M Patients (100 GB)
Clinical trials
Currently more than 30k
recruiting on ClinicalTrials.gov
Human proteome
160M data points (2.4GB) per sample
>3TB raw proteome data in ProteomicsDB
PubMed database
>23M articles
Hospital information systems
Often more than 50GB
Medical sensor data
Scan of a single organ in 1s
creates 10GB of raw dataCancer patient records
>160k records at NCT
Big Data in Genomics:
Opportunities and
Challenges
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015

8. Schapranow, HPI, Oct
13, 2015
Our Approach
Analyze Genomes: Real-time Analysis of Big Medical Data
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In-Memory Database
Extensions for Life Sciences
Data Exchange,
App Store
Access Control,
Data Protection
Fair Use
Statistical
Tools
Real-time
Analysis
App-spanning
User Profiles
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
Analyze Genomes: A
Federated In-Memory
Database Computing
Platform
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

9. Case Vignette
■ Patient: 48 years, female, non-smoker, smoke-free environment
■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV
■ Markers: KRAS, EGFR, BRAF, NRAS, (ERBB2)
■ Initial treatment: Surgery
■ Therapy: Palliative chemotherapy
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
9

10. Cloud-based Services for Processing of DNA Data
■ Control center for processing of raw DNA data, such as
FASTQ, SAM, and VCF
■ Personal user profile guarantees privacy of uploaded
and processed data
■ Supports reproducible research process by storing all
relevant process parameters
■ Implements prioritized data processing and fair use, e.g.
per department or per institute
■ Supports additional service, such as data annotations,
billing, and sharing for all Analyze Genomes services
■ Honored by the 2014 European Life Science Award
Big Data in Genomics:
Opportunities and
Challenges
Standardized Modeling and
runtime environment for
analysis pipelines
10
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015

11. ■ Query-oriented search interface
■ Seamless integration of patient specifics, e.g. from EMR
■ Parallel search in international knowledge bases, e.g. for biomarkers, literature,
cellular pathway, and clinical trials
Medical Knowledge Cockpit for Patients and Clinicians
Linking Patient Specifics with International Knowledge
Big Data in Genomics:
Opportunities and
Challenges
11
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015

12. Medical Knowledge Cockpit for Patients and Clinicians
■ Search for affected genes in distributed and
heterogeneous data sources
■ Immediate exploration of relevant information, such as
□ Gene descriptions,
□ Molecular impact and related pathways,
□ Scientific publications, and
□ Suitable clinical trials.
■ No manual searching for hours or days:
In-memory technology translates searching into
interactive finding!
Big Data in Genomics:
Opportunities and
Challenges
Automatic clinical trial
matching build on text
analysis features
Unified access to structured
and un-structured data
sources
12
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015

13. Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Medical Knowledge Cockpit for Patients and Clinicians
Pathway Topology Analysis
■ Search in pathways is limited to “is a certain
element contained” today
■ Integrated >1,5k pathways from international
sources, e.g. KEGG, HumanCyc, and WikiPathways,
into HANA
■ Implemented graph-based topology exploration and
ranking based on patient specifics
■ Enables interactive identification of possible
dysfunctions affecting the course of a therapy
before its start
Big Data in Genomics:
Opportunities and
Challenges
Unified access to multiple formerly
disjoint data sources
Pathway analysis of genetic
variants with graph engine
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14. Real-time Data Analysis and
Interactive Exploration
Drug Response Analysis
Data Sources
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
Smoking status,
tumor classification
and age
(1MB - 100MB)
Raw DNA data
and genetic variants
(100MB - 1TB)
Medication efficiency
and wet lab results
(10MB - 1GB)
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Patient-specific
Data
Tumor-specific
Data
Compound
Interaction Data

15. Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
15

16. Showcase
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
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Calculating Drug Response…Predict Drug Response

17. Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
17
cetuximab might be more
beneficial for the current case

18. Our Methodology
Design Thinking
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
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19. Our Methodology
Design Thinking
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
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Desirability
■ Portfolio of integrated services for clinicians, researchers, and patients
■ Include latest treatment option, e.g. most effective therapies
Viability
■ Enable precision medicine also in far-off
regions and developing countries
■ Involve word-wide experts (cost-saving)
■ Combine latest international data
(publications, annotations, genome data)
Feasibility
■ HiSeq 2500 enables high-coverage
whole genome sequencing in 20h
■ IMDB enables allele frequency
determination of 12B records within <1s
■ Cloud-based data processing services
reduce TCO

20. Combined column
and row store
Map/Reduce Single and
multi-tenancy
Lightweight
compression
Insert only
for time travel
Real-time
replication
Working on
integers
SQL interface on
columns and rows
Active/passive
data store
Minimal
projections
Group key Reduction of
software layers
Dynamic multi-
threading
Bulk load
of data
Object-
relational
mapping
Text retrieval
and extraction engine
No aggregate
tables
Data partitioning Any attribute
as index
No disk
On-the-fly
extensibility
Analytics on
historical data
Multi-core/
parallelization
Our Technology
In-Memory Database Technology
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
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Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges

21. ■ For patients
□ Identify relevant clinical trials and medical experts
□ Become an informed patient
■ For clinicians
□ Identify pharmacokinetic correlations
□ Scan for similar patient cases, e.g. to evaluate therapy efficiency
■ For researchers
□ Enable real-time analysis of medical data, e.g. assess pathways
to identify impact of detected variants
□ Combined mining in structured and unstructured data, e.g. publications,
diagnosis, and EMR data
What to Take Home?
Test it Yourself: AnalyzeGenomes.com
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
21
Big Data in Genomics:
Opportunities and
Challenges

22. Keep in contact with us!
Hasso Plattner Institute
Enterprise Platform & Integration Concepts (EPIC)
Program Manager E-Health
Dr. Matthieu-P. Schapranow
August-Bebel-Str. 88
14482 Potsdam, Germany
Dr. Matthieu-P. Schapranow
schapranow@hpi.de
http://we.analyzegenomes.com/
Schapranow, Bio Data,
Cambridge, UK, Oct 22,
2015
Big Data in Genomics:
Opportunities and
Challenges
22