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SAP HANA: Re-Thinking Information Processing for Genomic and Medical Data
1. SAP
HANA:
Re-‐Thinking
Informa7on
Processing
for
Genomic
and
Medical
Data
Prof.
Dr.
Hasso
Pla,ner
Chairman
of
the
Supervisory
Board,
SAP
AG
Professor,
Hasso
Pla,ner
Ins?tute
2. Real-Time Personalized Medicine is
Within Our Reach
Informa?on
and
Feedback
within
the
Window
of
Opportunity
Pa?ents
Doctors
Insurers
Researchers
Real-‐Time
Data
Capture
and
Analysis
SAP
HANA
Healthcare
PlaPorm
Electronic
Genomics
Medical
Records
Annota?ons
...
All
Relevant
Medical
Informa?on
Our
Can
we
Analyze
and
Interpret
all
Pa?ent
Data
Challenge:
on
a
Mobile
Device
During
a
Pa?ent’s
Visit?
2
3. Innovation in Personalized Medicine can be
Driven Using a “Design Thinking” Approach
Human
Factors
Business Technical
Factors Factors
3
4. What
Professionals
Desire
is
Simple
Use
Case
1:
Clinician
Iden?fy
Clinically
Ac?onable
Gene?c
Variants
(e.g.
Causing
Tumor
Forma?on)
in
Order
to
Deliver
Personalized
Medical
Treatment
Needs:
• Real-‐Time
Comparison
of
Variants
to
Assess
Causal
Ones
• Access
to
all
Pa?ent-‐Specific
Data
Any?me
and
Anywhere
Desirability Viability Feasibility
4
5. What
Professionals
Desire
is
Simple
Use
Case
2:
Researcher
Iden?fy
Causal
Variants
or
Muta?ons
in
Cohorts
(>
10,000
Individuals)
Suffering
from
Diseases
of
Interest,
e.g.
Au?sm
Needs:
• Comparison
of
Variants
in
Diseased
and
Healthy
Cohorts
• Flexible
Queries
to
Verify
Hypotheses
in
Real-‐Time
Desirability Viability Feasibility
5
6. Only
a
Deeply
Collabora7ve
Effort
can
be
Viable
From
a
Business
Perspec7ve
Patients Customers
SAP HANA
Universities Partners
"We
have
been
thrilled
to
work
with
SAP
and
HPI
on
a
collabora?on
to
accelerate
DNA
sequence
analysis.
In
our
pilot
projects,
we
are
seeing
drama?c
speedups
in
compu?ng
on
human
genome
varia?on
data
from
many
samples.
We
are
dreaming
of
what
will
soon
be
possible
as
we
integrate
phenotype,
genomics,
proteomics,
and
exposome
data
to
empower
complex
trait
mapping
using
millions
of
health
records.”
-‐
Professor
Carlos
D.
Bustamante
at
the
Stanford
University
School
of
Medicine
Desirability Viability Feasibility
6
7. SAP
HANA
is
the
Technology
Enabler
for
This
Vision
Advances
in
Hardware
• Mul?-‐core
Architectures,
• 64
bit
Address
Space
–
e.g.
4
CPUs
x
10
Cores
on
4TB
in
Current
Servers
A
Each
Node
• 25GB/s
Data
Throughput
• Scaling
Across
Servers,
• Costs
per
Enterprise
Class
e.g.
100
Nodes
x
40
Cores
Server
Node
(40
Cores)
approx.
29,000
USD
Advances
in
SoQware
T
+
+
+
+
Text Retrieval Insert Only Compression Partitioning Multi-Core Dynamic
and Extraction Parallelization Multithreading
7
8. SAP
HANA
is
the
Technology
Enabler
for
This
Vision
Due
to
the
Power
of
Mathema?cs
and
Distributed
Compu?ng,
SAP
HANA
can
Predictably
Complete
any
Informa?on
Processing
Task,
However
Complex,
Within
a
Given
Time-‐Window.
It
is
Only
a
Ma,er
of
Scaling
the
Hardware
–
There
are
no
Other
Variables
or
Unknowns
Scanning
3MB/msec/core
Inser?ng
1.5M
Records/sec
Aggrega?ng
12.5M
Records/sec/core
Desirability Viability Feasibility
8
9. More
Than
Just
a
Faster
Database,
SAP
HANA
is
a
Revolu7onary
Compu7ng
PlaTorm
+
Desirability Viability Feasibility
9
10. SAP
HANA
Customers
Have
Already
Demonstrated
Amazing
Results
Enterprise Applications
YODOBASHI
NONGFU
SPRING
LEADING
AIRLINE
100,000x
Faster
128,000x
Faster
43,000x
Faster
Sales
Data
Op?miza?on
of
Real-‐Time
Analysis
for
Transporta?on
Pricing
of
Campaign
Mailing
Routes
Tickets
Speed-‐Up
From
Speed-‐Up
From
Speed-‐Up
3
Days
To
2.5
sec
25
Hours
To
0.7
sec
From
12
Hours
To
1
sec
Desirability Viability Feasibility
10
11. SAP
HANA
Customers
Have
Already
Demonstrated
Amazing
Results
Healthcare Industry
MEDTRONIC
MITSUI
KNOWLEDGE
INDUSTRY
CHARITÉ
60x
Faster
408,000x
Faster
Than
1,000x
Processing
Queries
Tradi?onal
Disk-‐Based
Faster
Tumor
Systems
in
Technical
PoC
Data
10x
Data
Analyzed
in
Compression
From
216x
Faster
DNA
Analysis
Seconds
1.5
TB
To
150
GB
Results
-‐
From
2-‐3
Days
To
20
Instead
of
Minutes
Hours
250x
Be,er
Complaint
Analysis
2-‐10
sec
(Long
Text
Data)
For
Report
Desirability Viability Feasibility
Execu?on
11
12. We
Can
Drama7cally
Accelerate
Each
Step
of
the
DNA
Analysis
Pipeline
Mobile Real-time Analysis
Sequencing Service/Lab Computational Pipeline
e.g. Clinicians AND
e.g. Biologist e.g. Bioinformatician
Researchers
Sequencing Alignment Variant Calling Annotation and Analysis
Follow-up
Patient Raw DNA Mapped Discovered
and
Samples ReadS Genome Variants
Validation
12
13. First
Results
in
Alignment
Are
Promising
Mobile Real-time Analysis
Sequencing Service/Lab Computational Pipeline
e.g. Clinicians AND
e.g. Biologist e.g. Bioinformatician
Researchers
Sequencing Alignment Variant Calling Annotation and Analysis
Follow-up
Patient Raw DNA Mapped Discovered
and
Samples ReadS Genome Variants
Validation
SAP
HANA
Improves
Alignment
Performance
at
Higher
Accuracy!*
Faster
BWA-‐SW
28.3h
|
SAP
HANA
3.6h
Higher
Accuracy
BWA-‐SW
0.53%
Misaligned
|
SAP
HANA
0.35%
Misaligned
BWA-‐SW
0.34%
Unaligned
|
SAP
HANA
0.14%
Unaligned
13
*
Comparisons
done
with
simulated
full
genome,
30x
coverage,
100
bases
per
read,
single
ended
14. First
Results
in
Annota7on
and
Analysis
Are
Promising
Mobile Real-time Analysis
Sequencing Service/Lab Computational Pipeline
e.g. Clinicians AND
e.g. Biologist e.g. Bioinformatician
Researchers
Sequencing Alignment Variant Calling Annotation and Analysis
Follow-up
Patient Raw DNA Mapped Discovered
and
Samples ReadS Genome Variants
Validation
Annota7on
• Report
SNPs
(Single
Nucleo?de
Polymorphisms)
Failing
Quality
Control
82x
faster
UCSC
102.47
sec
|
SAP
HANA
1.25
sec
Analysis
• Compute
the
Alterna?ve
Allele
Frequency
for
Each
Variant
in
a
Genomic
Region
600x
faster
(Chromosome
1,
Posi?ons
100,000-‐200,000)
VCFtools
259
sec
|
SAP
HANA
0.43
sec
• Compute
the
Total
Number
of
Missing
Genotypes
for
Each
Individual
270x
faster
VCFtools
548
sec
|
SAP
HANA
2
sec
Supported
By:
Carlos
Bustamante
lab
14
15. Example
Solu7on:
Molecular
Health
Assessing
Therapy
Effec7veness
• Proac?vely
Analyze
Therapy
Alterna?ves
and
Provide
Decision
Support
When
Clinician
Talking
to
Pa?ent
• Combine
Genomic
Data
With
Electronic
Medical
Records
to
Iden?fy
Best
Therapy
for
Pa?ent
15
16. Example
Solu7on:
HANA
Oncolyzer
Real-‐7me
Access
to
Pa7ent
Data
• Mobile
Access
to
Complete
History
of
Pa?ent-‐Specific
Events
• Combined
Search
in
Structured
and
Unstructured
Clinical
Data
Sources
• Interac?ve
Analysis
and
Explora?on
of
Pa?ent
Records
on-‐the-‐fly
on
Doctor’s
Mobile
Devices
16
17. We
Have
the
Building
Blocks
to
Take
the
Next
Big
Step
in
Personalized
Medicine
• Mobile
and
Flexible
Informa?on
and
Feedback
within
the
Window
of
Opportunity
Access
to
any
Pa?ent-‐
Related
Data
Pa?ents
Doctors
Insurers
Researchers
• Real-‐Time
Analysis
Using
In-‐Memory
Real-‐Time
Data
Capture
and
Analysis
Technology
SAP
HANA
Healthcare
PlaPorm
• Data
Integra?on
from
Electronic
Heterogeneous
Genomics
Medical
Records
Annota?ons
...
Sources
All
Relevant
Medical
Informa?on
17
18. The
Future:
Redefining
the
Possible
with
Real-‐Time
Informa7on
Enable
Clinicians
to:
Enable
Researchers
to:
• Make
Evidence-‐Based
• Inves?gate
the
Genomes
of
Therapy
Decisions
at
the
Millions
of
High-‐Risk
Pa?ents
Pa?ent’s
Bed
on
a
Cluster
<
10M
USD
• Supervise
High-‐Risk
• Analyze
the
Results
in
Pa?ents
to
Prevent
Real-‐Time
Emergencies
18
19. The
Power
of
Mul7disciplinary
Teams
Only
Strong
Partners
Build
Strong
Co-‐Opera?ve
Success
Stories
SAP:
Global
Sowware
Vendor
and
Expert
for
Enterprise
Technologies
World-‐Wide
+
Design
Hasso
Plabner
Ins7tute:
Academic
Research
Thinking
Teams
Ins?tute
for
IT
Systems
Engineering
+
You
Carlos
Bustamante
Lab:
Leading
Stanford
Lab
On
Human
Popula?on
Genomics
and
Global
Health
Join our partnership!
19
20. New
Ways
of
Real-‐Time
Collabora7ve
Personal
Medicine
20
21. Thank
you!
Join
us:
hana-‐healthcare-‐plaPorm@sap.com
You
are
Invited
to
Visit
our
Booth
and
A,end
our
Partner
Presenta?ons.
21