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

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Our Methodology Design Thinking

Schapranow, SAPPHIRE, May 18, 2016

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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

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

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

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

In-Memory Apps for Next Generation Life Sciences Research

Comparison of waveform data with history of similar patients

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Schapranow, SAPPHIRE, May 18, 2016

t

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

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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t

■  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!

Schapranow, SAPPHIRE, May 18, 2016

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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?

Schapranow, SAPPHIRE, May 18, 2016

In-Memory Apps for Next Generation Life Sciences Research

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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

schapranow@hpi.de

http://we.analyzegenomes.com/

Schapranow, SAPPHIRE, May 18, 2016

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