The given slide deck was presented on May 18, 2016 at Intel Tech Talks hosted by SAPPHIRE 2016 in Orlando, FL

1. Analyze Genomes:
In-Memory Apps for Next Generation Life Sciences Research
Dr. Matthieu-P. Schapranow
SAPPHIRE, Orlando, USA
May 18, 2016

2. ■ Online: Visit we.analyzegenomes.com for latest research
results, slides, videos, tools, and publications
■ Offline: High-Performance In-Memory Genome Data Analysis:
In-Memory Data Management Research, Springer,
ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for Intel Tech Talks at SAPPHIRE booth 625 daily!
□ May 17 12.30pm: A Federated In-Memory Database Computing Platform Enabling
Real-time Analysis of Big Medical Data
□ May 18 12.30pm: In-Memory Apps for Next Generation Life Sciences Research
□ May 19 11.30am: In-Memory Apps Supporting Precision Medicine
Where to find additional information?
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
2

3. Our Methodology
Design Thinking
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
3

4. Our Methodology
Design Thinking
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
4
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

5. Schapranow, SAPPHIRE,
May 18, 2016
Our Approach
Analyze Genomes: Real-time Analysis of Big Medical Data
5
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
In-Memory Apps for
Next Generation Life
Sciences Research
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

6. In-Memory Database Technology
Overview
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
6
Advances in Hardware
64 bit address space –
4 TB in current server boards
4 MB/ms/core data throughput
Cost-performance ratio rapidly
declining
Multi-core architecture
(6 x 12 core CPU per blade)
Parallel scaling across blades
1 blade ≈50k USD =
1 enterprise class server
Advances in Software
Row and
Column Store Compression PartitioningInsert Only
A
Parallelization
+
++
+
+
P
Active & Passive
Data Stores

7. In-Memory Database Technology
Use Case: Analysis of Genomic Data
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
7
Analysis of
Genomic Data
Alignment and Variant Calling
Analysis of Annotations in World-
wide DBs
Bound To CPU Performance Memory Capacity
Duration Hours – Days Weeks
HPI Minutes Real-time
In-Memory
Technology
Multi-Core Partitioning & Compression

8. ■ Interdisciplinary partners collaborate on enabling real-time healthcare research
■ Initial funding period: Aug 2015 – July 2018
■ Funded consortium partners:
□ AOK
German healthcare insurance company
□ data experts group
Technology operations
□ Hasso Plattner Institute
Real-time data analysis, in-memory database technology
□ Technology, Methods, and Infrastructure for Networked Medical Research
Legal and data protection
App Example:
Smart Analysis Health Research Access (SAHRA)
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
8

9. Heart
Failure
Sleeping
disorder
Fibrosis
Blood
pressure
Blood
volume
Gene ex-
pression
Hyper-
trophyCalcium
meta-
bolism
Energy
meta-
bolism
Iron
deficiency
Vitamin-D
deficiency
Gender
Epi-
genetics
■ Integrated systems medicine based on
real-time analysis of healthcare data
■ Initial funding period: Mar ‘15 – Feb ‘18
■ Funded consortium partners:
App Example:
Systems Medicine Model of Heart Failure (SMART)
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
9

10. App Example:
Real-time Analysis of Event Data from Medical Sensors
■ Processing of sensor data, e.g. from Intensive Care
Units (ICUs) or wearable sensor devices (quantify self)
■ Multi-modal real-time analysis to detect indicators for
severe events, such as heart attacks or strokes
■ Incorporates machine-learning algorithms to detect
severe events and to
inform clinical
personnel in time
■ Successfully tested
with 100 Hz event
rate, i.e. sufficient
for ICU use
In-Memory Apps for
Next Generation Life
Sciences Research
Comparison of waveform data
with history of similar patients
10
Schapranow, SAPPHIRE,
May 18, 2016
t

11. App Example:
Real-time Assessment of Clinical Trial Candidates
■ Supports trial design by statistical analysis of data sets
■ Real-time matching and clustering of patients and
clinical trial inclusion/exclusion criteria
■ No manual pre-screening of patients for months:
In-memory technology enables interactive pre-
screening process
■ Reassessment of already screened or already
participating patient reduces recruitment costs
In-Memory Apps for
Next Generation Life
Sciences Research
Assessment of patients
preconditions for clinical trials
11
Schapranow, SAPPHIRE,
May 18, 2016

12. Schapranow, SAPPHIRE,
May 18, 2016
From University to Market
Oncolyzer
■ Research initiative for exchanging relevant
tumor data to improve personalized treatment
■ Real-time analysis of tumor data in seconds
instead of hours
■ Information available at your fingertips: In-
memory technology on mobile devices, e.g. iPad
■ Interdisciplinary cooperation between clinicians,
clinical researchers, and software engineers
■ Honored with the 2012 Innovation Award of the
German Capitol Region
In-Memory Apps for
Next Generation Life
Sciences Research
Unified access to formerly disjoint
oncological data sources
Flexible analysis on patient’s
longitudinal data
12
t

13. ■ Combines patient’s
longitudinal time series data
with individual analysis
results
■ Real-time analysis across
hospital-wide data using
always latest data when
details screen is accessed
■ http://analyzegenomes.com/
apps/oncolyzer-mobile-app/
From University to Market
Oncolyzer: Patient Details Screen
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
13

14. ■ Allows real-time analysis on
complete patient cohort
■ Supports identification of
clinical trial participants
based on their individual
anamnesis
■ Flexible filters and various
chart types allow graphical
exploration of data on
mobile devices
From University to Market
Oncolyzer: Patient Analysis Screen
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
14

15. ■ Shows all patients the logged-
in clinician is assigned for
■ Provides overview about most
recent results and treatments
for each patient
■ http://global.sap.com/
germany/solutions/
technology/enterprise-
mobility/healthcare-apps/
mobile-patient-record-app.epx
From University to Market
SAP EMR: Patient Overview Screen
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
15

16. ■ Displays time series data, e.g.
temperature or BMI
■ Allows graphical exploration of
time series data
From University to Market
SAP EMR: Patient Detail Screen
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
16

17. ■ Flexible combination of
medical data
■ Enables interactive and
graphical exploration
■ Easy to use even without
specific IT background
From University to Market
SAP Medical Research Insights
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
17

18. ■ Markers for cardiovascular diseases to
assess treatment options (DHZB)
■ Combine health data to improve health care
research (AOK)
■ Generously supported by
Join us for current projects!
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
18
Interdisciplinary
Design Thinking
Teams
You?

19. ■ Online: Visit we.analyzegenomes.com for latest research
results, slides, videos, tools, and publications
■ Offline: High-Performance In-Memory Genome Data Analysis:
In-Memory Data Management Research, Springer,
ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for Intel Tech Talks at SAPPHIRE booth 625 daily!
□ May 17 12.30pm: A Federated In-Memory Database Computing Platform Enabling
Real-time Analysis of Big Medical Data
□ May 18 12.30pm: In-Memory Apps for Next Generation Life Sciences Research
□ May 19 11.30am: In-Memory Apps Supporting Precision Medicine
Where to find additional information?
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
19

20. Keep in contact with us!
Dr. Matthieu-P. Schapranow
Program Manager E-Health & Life Sciences
Hasso Plattner Institute
August-Bebel-Str. 88
14482 Potsdam, Germany
schapranow@hpi.de
http://we.analyzegenomes.com/
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Research
20