The given slide deck was presented on the 2017 Festival of Genomics in London, UK. It depicts how latest in-memory database technology supports clinicians in finding the best treatment options incorporating genetic data.

1. AnalyzeGenomes.com: When time matters…
Dr. –Ing. Matthieu-P. Schapranow
Festival of Genomics, London, UK
Jan 31, 2017

2. What is the Hasso Plattner Institute, Potsdam, Germany?
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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3. From Raw Genome Data to Analysis
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
When time matters...
■ DNA Sequencing: Transformation of
analogues DNA into digital format
■ Alignment: Reconstruction of complete
genome with snippets
■ Variant Calling: Identification of genetic
variants
■ Data Annotation: Linking genetic variants
with research findings
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4. ■ Purpose: Transformation of analogous DNA into digital format (A/D converter)
■ Input: Chunks of DNA
■ Output: DNA reads in digital form, e.g. in FASTQ format
1. DNA Sequencing
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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44peaks.app

5. ■ FASTQ format used for further processing
■ One read is a quart-tuple of:
1. Sequence identifier / description
2. Raw sequence
3. Strand / direction
4. Quality values per sequenced base
1. Output of Sequencing
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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6. ■ Purpose: Mapping of DNA reads to a reference
■ Input:
□ DNA reads := Sequence of nucleotides with a length of 100 bp up to some 1 kbp
□ Reference genome := Blueprint for alignment of DNA reads
■ Output: Mapped DNA reads
■ Bear in mind:
□ Less fraction in DNA reads, i.e. longer reads, allows more precise alignment
□ Reference from same origin improves mapping quality
2. Alignment
Overview
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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7. ■ Purpose: Variant Calling := Detect variations within a genome
■ Input:
□ Mapped DNA reads, i.e. output of alignment process
□ Reference genome
■ Output: List of variants
■ Bear in mind:
□ Read depth at posi:= Number of nucleotides storing information about pos i
3. Variant Calling
Overview
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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8. ■ Purpose:
□ Assess impact of genetic changes
□ Understand gene function and possible medical therapy options
■ Input: List of genetic variants
■ Output: Details about certain genetic locus
4. Genetic Annotations
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of Genomics, Jan 31,
2017
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CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE
chr7 140753336 rs113488022 T A 61 PASS NS=1 GT 0/1

9. ■ Manual, time-consuming Internet search, e.g. publications, annotations, guidelines
■ International consortiums provide fragmented information
■ Missing linkage across individual data sources
■ Annotations vary in completeness and correctness
4. Challenges
Today
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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10. ■ https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=rs113488022
4. Interpretation of Annotations: BRAF Gene
dbSNP
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of Genomics, Jan 31,
2017
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11. 4. Interpretation of Annotations: BRAF Gene
Kegg
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of Genomics, Jan 31,
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12. 4. Interpretation of Annotations: BRAF gene
GeneCards
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of Genomics, Jan 31,
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13. 4. Interpretation of Annotations: BRAF Gene
PubMed
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of Genomics, Jan 31,
2017
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14. Simplified Clinical Oncology Process (1/2)
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of Genomics, Jan 31,
2017
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Simplified Clinical Oncology Process (1/2)

15. Simplified Clinical Oncology Process (1/2)
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of Genomics, Jan 31,
2017
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Simplified Clinical Oncology Process (2/2)

16. ■ 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
When time matters...
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Dr. Schapranow, Festival
of Genomics, Jan 31,
2017

17. Use Case: Precision Oncology
Identification of Best Treatment Option for Cancer Patient
■ Patient: 48 years, female, non-smoker, smoke-free environment
■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV
■ Markers: KRAS, EGFR, BRAF, NRAS, (ERBB2)
1. Remove tumor through surgery
2. Send tumor sample to laboratory for DNA extraction
3. Sequence complete DNA of sample results in 750 GB of raw genome data
4. Process raw genome data, e.g. alignment, variant calling, and annotate
5. Identify relevant variants using international medical knowledge
6. Support decision making, e.g. link to de-identified historic cases
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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18. Dr. Schapranow, Festival
of Genomics, Jan 31,
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19. Our Vision
Medical Board Incorporating Latest Medical Knowledge
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of Genomics, Jan 31,
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20. The Challenge
Distributed Heterogeneous Data Sources
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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
When time matters...
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017

21. ■ Requirements
□ Managed services
□ Reproducibility
□ Real-time data analysis
■ Restrictions
□ Data privacy
□ Data locality
□ Volume of big medical data
Software Requirements in Life Sciences
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of Genomics, Jan 31,
2017
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http://stevedempsen.blogspot.de/2013/08/agile-software-requirements-comic.html

22. Project Time Line
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of Genomics, Jan 31,
2017
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2009 2010 2011 2012 2013 2014 2015
SAP HANA
launched Oncolyzer SORMAS
Drug Response
Analysis
Enterprise
Software
Medical
Knowledge
Cockpit
Analyze
Genomes
Platform
IMDB
Research
2016 2017
A R
T
+
T
RAM
S
+
S
M

23. Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
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In-Memory Database
When time matters...

24. Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
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In-Memory Database
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
When time matters...
Indexed
Sources

25. Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for 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
When time matters...
Indexed
Sources

26. Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
26
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
When time matters...
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

27. 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
Real-Time Data Analysis
In-Memory Database Technology
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
27
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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28. Managed Services provided by
Federated In-Memory Database System (FIMDB)
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of Genomics, Jan 31,
2017
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Node i
WorkerWorkerWorker
IMDB
Node j
WorkerWorkerWorker
IMDB
Node k
WorkerWorkerWorker
IMDB
Scheduler
Node m
WorkerWorkerWorker
IMDB
Relay
Node n
WorkerWorkerWorker
IMDB
...
Cloud Service Provider
(Shared Algorithms and Public Reference Data)
Hospital or Research Department
(Sensitive/Patient Data)
VPN
UDP
TCP
Shared File System (Pool) Shared File System (Pool)
...
Shared File System (Global)

29. From Raw Genome Data to Analysis
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
When time matters...
■ DNA Sequencing: Transformation of
analogues DNA into digital format
■ Alignment: Reconstruction of complete
genome with snippets
■ Variant Calling: Identification of genetic
variants
■ Data Annotation: Linking genetic variants
with research findings
29

30. 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
When time matters...
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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31. App Example:
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
When time matters...
Standardized Modeling and
runtime environment for
analysis pipelines
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Dr. Schapranow, Festival
of Genomics, Jan 31,
2017

32. Real-time Data Analysis and
Interactive Exploration
App Example:
Identification of Optimal Chemotherapy
Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
When time matters...
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
■ Honored by the 2015 PerMediCon Award

33. Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
When time matters...
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34. Showcase
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of Genomics, Jan 31,
2017
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Calculating Drug Response…Predict Drug Response

35. Dr. Schapranow, Festival
of Genomics, Jan 31,
2017
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cetuximab might be more
beneficial for the current case

36. ■ 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
When time matters...
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Dr. Schapranow, Festival
of Genomics, Jan 31,
2017

37. Dr. Schapranow, Festival
of Genomics, Jan 31,
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
When time matters...
Unified access to multiple formerly
disjoint data sources
Pathway analysis of genetic
variants with graph engine
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38. Dr. Schapranow, Festival
of Genomics, Jan 31,
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
When time matters...
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39. Real-time Assessment of Clinical Trial Candidates
■ Switch from trial-centric to patient-centric clinical trials
■ 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
When time matters...
Assessment of patients
preconditions for clinical trials
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Dr. Schapranow, Festival
of Genomics, Jan 31,
2017

40. ■ 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, Festival
of Genomics, Jan 31,
2017
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When time matters...

41. Keep in contact with us!
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of Genomics, Jan 31,
2017
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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/