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

Analyze Genomes: In-Memory Apps for Next Generation Life Sciences Research

Dr. Matthieu-P. Schapranow SAPPHIRE, Orlando, USA

May 18, 2016

■  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


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


Drug Response Analysis

Pathway Topology Analysis

Medical Knowledge Cockpit Oncolyzer

Clinical Trial Recruitment

Cohort Analysis

...

Indexed Sources

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

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Parallelization

+++

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Active & Passive Data Stores

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

■  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


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


Assessment of patients preconditions for clinical trials

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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


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?

■  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?



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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

[email protected]

http://we.analyzegenomes.com/



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Date post:	16-Apr-2017
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Analyze Genomes: In-memory Apps for Next-generation Life Sciences Research

Health & Medicine