analyze genomes: in-memory apps for next-generation life sciences research
TRANSCRIPT
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Analyze Genomes: In-Memory Apps for Next Generation Life Sciences Research
Dr. Matthieu-P. Schapranow SAPPHIRE, Orlando, USA
May 18, 2016
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■ 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
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Our Methodology Design Thinking
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Our Methodology Design Thinking
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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
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Schapranow, SAPPHIRE, May 18, 2016
Our Approach Analyze Genomes: Real-time Analysis of 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
In-Memory Apps for Next Generation Life Sciences Research
Drug Response Analysis
Pathway Topology Analysis
Medical Knowledge Cockpit Oncolyzer
Clinical Trial Recruitment
Cohort Analysis
...
Indexed Sources
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In-Memory Database Technology Overview
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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 Partitioning Insert Only
A
Parallelization
+++
++
P
Active & Passive Data Stores
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In-Memory Database Technology Use Case: Analysis of Genomic Data
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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
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■ 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)
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Heart Failure
Sleeping disorder
Fibrosis
Blood pressure
Blood volume
Gene ex-pression
Hyper-trophy Calcium
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)
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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
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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
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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
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■ 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
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■ 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
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■ 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
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■ 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
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■ 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
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■ 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!
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Interdisciplinary Design Thinking
Teams
You?
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■ 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
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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
http://we.analyzegenomes.com/
Schapranow, SAPPHIRE, May 18, 2016
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