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Analyze Genomes:
In-Memory Apps for Next Generation Life Sciences Research
Dr. Matthieu-P. Schapranow
SAPPHIRE, Orlando, U...
■  Online: Visit we.analyzegenomes.com for latest research
results, slides, videos, tools, and publications
■  Offline: High...
Our Methodology
Design Thinking
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Resear...
Our Methodology
Design Thinking
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences Resear...
Schapranow, SAPPHIRE,
May 18, 2016
Our Approach
Analyze Genomes: Real-time Analysis of Big Medical Data
5
In-Memory Databa...
In-Memory Database Technology
Overview
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Next Generation Life
Sciences...
In-Memory Database Technology
Use Case: Analysis of Genomic Data
Schapranow, SAPPHIRE,
May 18, 2016
In-Memory Apps for
Nex...
■  Interdisciplinary partners collaborate on enabling real-time healthcare research
■  Initial funding period: Aug 2015 – ...
Heart
Failure
Sleeping
disorder
Fibrosis
Blood
pressure
Blood
volume
Gene ex-
pression
Hyper-
trophyCalcium
meta-
bolism
E...
App Example:
Real-time Analysis of Event Data from Medical Sensors
■  Processing of sensor data, e.g. from Intensive Care
...
App Example:
Real-time Assessment of Clinical Trial Candidates
■  Supports trial design by statistical analysis of data se...
Schapranow, SAPPHIRE,
May 18, 2016
From University to Market
Oncolyzer
■  Research initiative for exchanging relevant
tumo...
■  Combines patient’s
longitudinal time series data
with individual analysis
results
■  Real-time analysis across
hospital...
■  Allows real-time analysis on
complete patient cohort
■  Supports identification of
clinical trial participants
based on ...
■  Shows all patients the logged-
in clinician is assigned for
■  Provides overview about most
recent results and treatmen...
■  Displays time series data, e.g.
temperature or BMI
■  Allows graphical exploration of
time series data
From University ...
■  Flexible combination of
medical data
■  Enables interactive and
graphical exploration
■  Easy to use even without
speci...
■  Markers for cardiovascular diseases to
assess treatment options (DHZB)
■  Combine health data to improve health care
re...
■  Online: Visit we.analyzegenomes.com for latest research
results, slides, videos, tools, and publications
■  Offline: High...
Keep in contact with us!
Dr. Matthieu-P. Schapranow
Program Manager E-Health & Life Sciences
Hasso Plattner Institute
Augu...
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Analyze Genomes: In-memory Apps for Next-generation Life Sciences Research

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

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Analyze Genomes: In-memory Apps for Next-generation Life Sciences Research

  1. 1. Analyze Genomes: In-Memory Apps for Next Generation Life Sciences Research Dr. Matthieu-P. Schapranow SAPPHIRE, Orlando, USA May 18, 2016
  2. 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. 3. Our Methodology Design Thinking Schapranow, SAPPHIRE, May 18, 2016 In-Memory Apps for Next Generation Life Sciences Research 3
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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