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AnalyzeGenomes.com
A Federated In-Memory Database Platform for Digital Health
Dr.-Ing. Matthieu-P. Schapranow
BMBF All Hands Meeting, Karlsruhe
Oct 11, 2017
What is the Hasso Plattner Institute, Potsdam, Germany?
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
2
■ Founded as a public-private partnership
in 1998 in Potsdam near Berlin, Germany
■ Institute belongs to the
University of Potsdam
■ Ranked 1st in CHE since 2009
■ 500 B.Sc. and M.Sc. students
■ 12 professors/chairs, 150 PhD students
■ Apr 2017: Digital Engineering Faculty
■ Oct 2017: Opening of Digital Health Center
Hasso Plattner Institute
Key Facts
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
3
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
■  Can we enable clinicians to take their therapy decisions:
□  Incorporating all available patient specifics,
□  Referencing latest lab results and worldwide medical knowledge, and
□  In an interactive manner during their ward round?
Our Motivation
Turn Precision Medicine Into Clinical Routine
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
4
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
5
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
6
Our Vision
Medical Board Incorporating Latest Medical Knowledge
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
7
The Challenge
Distributed Heterogeneous Data Sources
8
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 Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
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
9
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
10
In-Memory Database
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
11
In-Memory Database
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Indexed
Sources
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
12
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
Federerated In-
Memory Database for
Digital Health
Indexed
Sources
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
13
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
Federerated In-
Memory Database for
Digital Health
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources
Reproducibility
Modeling of Data Analysis Pipelines
1.  Design time (researcher, process expert)
□  Definition of parameterized process model
□  Uses graphical editor and jobs from repository
2.  Configuration time (researcher, lab assistant)
□  Select model and specify parameters, e.g. aln opts
□  Results in model instance stored in repository
3.  Execution time (researcher)
□  Select model instance
□  Specify execution parameters, e.g. input files
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
14
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:
Systems Medicine Model of Heart Failure (SMART)
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
15
A R
T
+
T
RAM
S
+
S
M
■  Patient: 63 years, male, smoker, chronic heart insufficiency, stage III-IV
1.  Appointment I (pre-surgery): Acquire systemic patient details, e.g.
physiological and blood markers
2.  Predict outcome using clinical model with patient specifics
3.  Select adequate option and conduct valve replacement
4.  Equip patient with sensors to allow regular monitoring
5.  Appointment II 6 wks after surgery to validate outcome
Establish Systems Medicine Model for
Improved Treatment of Heart Failure
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
16
■  Joint process definition
■  Identification of long running steps
■  Aims
□  Sharing of data
□  Improved communication
□  Reproducible data processing
□  Analysis applications for interactive
hypothesis validation
Requirements Engineering for System Medicine
Computer-aided Systems Medicine Process
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
17
■  Structured data acquisition, e.g. IMDB as data integration platform
■  Improved communication, e.g. event-driven user notifications
■  Reproducible data processing, e.g. IMDB as processing platform for DNA
and RNA data
■  Enables real-time data analysis
Contributions
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
18
RNA Seq Analysis_V2
TopHat
Trimmomatic
FASTQC
STAR
featureCounts
Counts Matrix
BAM-File
Aligned Reads
FASTQC 2
FASTQ -
Trimmed Reads
Pre-Trimming
QC-Report
FASTQ - Reads
Post-Alignment
QC-Report
s
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
19
■  Interdisciplinary partners collaborate on enabling interactive health research
■  Current 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
Smart Analysis Health Research Access (SAHRA)
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
20
■  Analysis dashboard combining
functions per use case
■  Providing expert-facing entry
point to individual apps
■  Provides application-wide
authentication / single sign on
Interactive Analysis Dashboard
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
21
■  Stratification of patient cohorts using patient specifics
■  Automatic matching of similar patients and patient anamnesis
■  Interactive graphical exploration of longitudinal patient data
Stratification of Hypertension Patients
and Longitudinal Data Analysis
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
22
■  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
App Example:
Medical Knowledge Cockpit for Patients and Clinicians
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
23
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
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
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Unified access to multiple formerly
disjoint data sources
Pathway analysis of genetic
variants with graph engine
24
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
■  Interactively explore relevant publications, e.g. PDFs
■  Improved ease of exploration, e.g. by highlighted medical terms and relevant
concepts
Medical Knowledge Cockpit for Patients and Clinicians
Publications
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
25
■  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?
Learn more and test-drive it yourself: AnalyzeGenomes.com
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
26
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Keep in contact with us!
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
27
Dr.-Ing. 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/

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AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital Health

  • 1. AnalyzeGenomes.com A Federated In-Memory Database Platform for Digital Health Dr.-Ing. Matthieu-P. Schapranow BMBF All Hands Meeting, Karlsruhe Oct 11, 2017
  • 2. What is the Hasso Plattner Institute, Potsdam, Germany? Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 2
  • 3. ■ Founded as a public-private partnership in 1998 in Potsdam near Berlin, Germany ■ Institute belongs to the University of Potsdam ■ Ranked 1st in CHE since 2009 ■ 500 B.Sc. and M.Sc. students ■ 12 professors/chairs, 150 PhD students ■ Apr 2017: Digital Engineering Faculty ■ Oct 2017: Opening of Digital Health Center Hasso Plattner Institute Key Facts Analyze Genomes: A Federerated In- Memory Database for Digital Health 3 Dr. Schapranow, BMBF All Hands, Oct 11, 2017
  • 4. ■  Can we enable clinicians to take their therapy decisions: □  Incorporating all available patient specifics, □  Referencing latest lab results and worldwide medical knowledge, and □  In an interactive manner during their ward round? Our Motivation Turn Precision Medicine Into Clinical Routine Analyze Genomes: A Federerated In- Memory Database for Digital Health 4 Dr. Schapranow, BMBF All Hands, Oct 11, 2017
  • 5. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 5
  • 6. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 6
  • 7. Our Vision Medical Board Incorporating Latest Medical Knowledge Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 7
  • 8. The Challenge Distributed Heterogeneous Data Sources 8 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 Analyze Genomes: A Federerated In- Memory Database for Digital Health Dr. Schapranow, BMBF All Hands, Oct 11, 2017
  • 9. 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 9 Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health
  • 10. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Our Approach: AnalyzeGenomes.com In-Memory Computing Platform for Big Medical Data 10 In-Memory Database Analyze Genomes: A Federerated In- Memory Database for Digital Health
  • 11. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Our Approach: AnalyzeGenomes.com In-Memory Computing Platform for Big Medical Data 11 In-Memory Database Combined and Linked Data Genome Data Cellular Pathways Genome Metadata Research Publications Pipeline and Analysis Models Drugs and Interactions Analyze Genomes: A Federerated In- Memory Database for Digital Health Indexed Sources
  • 12. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Our Approach: AnalyzeGenomes.com In-Memory Computing Platform for Big Medical Data 12 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 Federerated In- Memory Database for Digital Health Indexed Sources
  • 13. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Our Approach: AnalyzeGenomes.com In-Memory Computing Platform for Big Medical Data 13 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 Federerated In- Memory Database for Digital Health Drug Response Analysis Pathway Topology Analysis Medical Knowledge CockpitOncolyzer Clinical Trial Recruitment Cohort Analysis ... Indexed Sources
  • 14. Reproducibility Modeling of Data Analysis Pipelines 1.  Design time (researcher, process expert) □  Definition of parameterized process model □  Uses graphical editor and jobs from repository 2.  Configuration time (researcher, lab assistant) □  Select model and specify parameters, e.g. aln opts □  Results in model instance stored in repository 3.  Execution time (researcher) □  Select model instance □  Specify execution parameters, e.g. input files Analyze Genomes: A Federerated In- Memory Database for Digital Health Dr. Schapranow, BMBF All Hands, Oct 11, 2017 14
  • 15. 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: Systems Medicine Model of Heart Failure (SMART) Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 15 A R T + T RAM S + S M
  • 16. ■  Patient: 63 years, male, smoker, chronic heart insufficiency, stage III-IV 1.  Appointment I (pre-surgery): Acquire systemic patient details, e.g. physiological and blood markers 2.  Predict outcome using clinical model with patient specifics 3.  Select adequate option and conduct valve replacement 4.  Equip patient with sensors to allow regular monitoring 5.  Appointment II 6 wks after surgery to validate outcome Establish Systems Medicine Model for Improved Treatment of Heart Failure Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 16
  • 17. ■  Joint process definition ■  Identification of long running steps ■  Aims □  Sharing of data □  Improved communication □  Reproducible data processing □  Analysis applications for interactive hypothesis validation Requirements Engineering for System Medicine Computer-aided Systems Medicine Process Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 17
  • 18. ■  Structured data acquisition, e.g. IMDB as data integration platform ■  Improved communication, e.g. event-driven user notifications ■  Reproducible data processing, e.g. IMDB as processing platform for DNA and RNA data ■  Enables real-time data analysis Contributions Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 18 RNA Seq Analysis_V2 TopHat Trimmomatic FASTQC STAR featureCounts Counts Matrix BAM-File Aligned Reads FASTQC 2 FASTQ - Trimmed Reads Pre-Trimming QC-Report FASTQ - Reads Post-Alignment QC-Report
  • 19. s Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 19
  • 20. ■  Interdisciplinary partners collaborate on enabling interactive health research ■  Current 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 Smart Analysis Health Research Access (SAHRA) Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 20
  • 21. ■  Analysis dashboard combining functions per use case ■  Providing expert-facing entry point to individual apps ■  Provides application-wide authentication / single sign on Interactive Analysis Dashboard Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 21
  • 22. ■  Stratification of patient cohorts using patient specifics ■  Automatic matching of similar patients and patient anamnesis ■  Interactive graphical exploration of longitudinal patient data Stratification of Hypertension Patients and Longitudinal Data Analysis Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 22
  • 23. ■  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 App Example: Medical Knowledge Cockpit for Patients and Clinicians Analyze Genomes: A Federerated In- Memory Database for Digital Health 23 Dr. Schapranow, BMBF All Hands, Oct 11, 2017
  • 24. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 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 Analyze Genomes: A Federerated In- Memory Database for Digital Health Unified access to multiple formerly disjoint data sources Pathway analysis of genetic variants with graph engine 24
  • 25. Dr. Schapranow, BMBF All Hands, Oct 11, 2017 ■  Interactively explore relevant publications, e.g. PDFs ■  Improved ease of exploration, e.g. by highlighted medical terms and relevant concepts Medical Knowledge Cockpit for Patients and Clinicians Publications Analyze Genomes: A Federerated In- Memory Database for Digital Health 25
  • 26. ■  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? Learn more and test-drive it yourself: AnalyzeGenomes.com Dr. Schapranow, BMBF All Hands, Oct 11, 2017 26 Analyze Genomes: A Federerated In- Memory Database for Digital Health
  • 27. Keep in contact with us! Dr. Schapranow, BMBF All Hands, Oct 11, 2017 Analyze Genomes: A Federerated In- Memory Database for Digital Health 27 Dr.-Ing. 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/