Data Analysis and Knowledge Management using BioXM in MeDALL, AirPROM and Synergy-COPD

Supported by

Prominent international speakers from

h"p://workshop.eisbm.eu1

WP#6#Epigene+cs#and#targeted#
proteomics#
Stephane#Ballereau#
Ste#
EISBM#Workshop## #

MeDALL aims

•  iden+fy#causes&for&allergy,#eg#asthma#and#atopic#derma++s#
•  in#par+cular#in#childhood#
•  to#improve#current#diagnos1c#and#preven1on&tools#
•  using#a#system&biology&approach#
•  combining&various&types#of&biomarker&proﬁles&or#
ﬁngerprints#into#a#handprints&

Classical&approach& Birth&cohorts& Novel&approach&
IgE&arrays&and&follow=up&

Integra1on&of&all&data&for&determina1on&of&biomarkers&for&early&diagnosis,&&
preven1on&and&iden1ﬁca1on&of&targets&for&therapy&of&allergy&


Analysis&of&
Classical#phenotypes#
risk&factors&
deﬁned#by#experts#
and&GxE&



Analysis&of&
Classical#phenotypes# Novel&phenotypes&&
risk&factors&
deﬁned#by#experts# deﬁned#using#sta+stal#methods#
and&GxE&



Analysis&of&
risk&factors&
and&GxE&

Selec+on#of#extreme#phenotypes#

Gene1cs&
Epigene1cs&
Transcriptomics&
Targeted&proteomics&
Ig&arrays&



Analysis&of&
risk&factors&
and&GxE&


Gene1cs&
Epigene1cs&
Karelia&cross=sec1onal&study&
Transcriptomics&
Finland&and&Russia&
Ig&arrays&



Analysis&of&
risk&factors&
and&GxE&


Gene1cs&
Epigene1cs&
Transcriptomics&
Finland&and&Russia&
Ig&arrays&

1)#Iden+ﬁca+on#of#ﬁngerprints#
2)#Valida+on#in#birth#cohorts#samples############
3)#Replica+on#in#birth#cohort#followKup###

Analysis&of&
risk&factors&
and&GxE&



Analysis&of&
risk&factors&
and&GxE&


Gene1cs&
Epigene1cs&
Transcriptomics&
Finland&and&Russia&
Ig&arrays&

1)#Iden+fica+on#of#fingerprints# Confirma1on:&
2)#Valida+on#in#birth#cohorts#samples############ &=&in&animal&models&
3)#Replica+on#in#birth#cohort#followKup### =&by&in#vitro#immunology&

Analysis&of&
risk&factors&
and&GxE&



Analysis&of&
risk&factors&
and&GxE&


Gene1cs&
Epigene1cs&
Transcriptomics&
Finland&and&Russia&
Ig&arrays&


Analysis&of&
risk&factors& Mathema1cal&modelling&
and&GxE&


Integrative knowledge management

Analysis&of&
risk&factors&
and&GxE&


Gene1cs&
Epigene1cs&
Transcriptomics&
Finland&and&Russia&
Ig&arrays&


Analysis&of&
risk&factors& Mathema1cal&modelling&
and&GxE&


From fingerprints to handprints

•  Develop#classiﬁers#and#predictors#using:#
–  univariate#and#mul+variate#sta+s+cal#analyses#
–  clustering#of#omics#data#
–  network#and#pathway#modelling#
–  simula+on#and#visualiza+on#with#graphical#interfaces##
•  Iden+fy#most#informa+ve#types#of#experiments#and#
analyses#
•  Reﬁne#handprints#predic+ve#of:#
–  disease#progression#and#exacerba+on#
–  response#to#treatment#in#allergic#pa+ents#

Data Analysis and Knowledge
Management using BioXM
MeDALL - AirPROM - Synergy-COPD

EISBM Workshop 13-15.6.12
Dr. Dieter Maier
Biomax Informatics AG

www.biomax.com

Biomax – Connecting unrelated
information for efficient decision
support
Biomax Vision Biomax Profile

Master scientific complexity Headquartered in Martinsried
Germany
Ensure ease of use
Increase speed of development In business for more than 12
years
Reduce cost and time
World wide customer base
BioXM is a configurable
knowledge management platform Enable centers of excellence for
to flexibly interconnect isolated personalized medicine
silos of information in biomedical Support for Systems Biology
research

Why „Knowledge Management“?

Knowledge: “the realisation and
understanding of patterns and their
implications existing in information”

Need to mine information for
patterns
A pattern often only emerge when
information from different silos is
combined
e.g. Expression with gene function,
SNPs with clinical history of
patients, ...

Need semantically integrated
information
e.g. Information about identical or
“equivalent” objects and “meaning”
becomes integrated requires
framework for integration
methods to find “equivalent”
“meaning”

Knowledge Management aspects

• Data integration
• Knowledge representation
• Knowledge extraction

• Collaboration and project management
• Multivariate data analysis

Clinical data harmonisation

Multi-scale Data

14 birth cohorts
Cross-sectional,
1 cross-sectional
1 cross-sectional
Longitudinal
2 interventional

Intervention
Studies

Traditional semantic mapping

KEGG

PubMed Gene Ontology UniProt

Working with semantic networks

• Connected data,
meta-data and
knowledge
• Query, view, report
• Integrate with
analysis

Knowledge Network Representation
Dynamic network representation in BioXM

Each node or edge of the network may serve
as entry point for further exploration!

Knowledge Network Expansion
Dynamic network representation in BioXM

Concept - Agile Solution Building

Step 1:
Specification
• Designing the
data model

Query the knowledge network, Define the domain-specific data
explore the graph and report query model
results
Step 3: Use Step 2:
• Query building Implementation
and information
retrieval
• Importing
information
Instantiate the
knowledge network
with data and
information from
external resources

Building Blocks

Experiment
Text mining Graphs
repository

Public databases R statistics Network search

Feature bundles

Clinical data access
Modell integration support
Collaboration net

Solution deployment
Step 1:
Specification
Designing the data
Web applications framework fueled
model by BioXM for quick access

Step 3: Use Step 2:
Query building and Implementation
information Importing
retrieval information

Step 4: WebApps
for Information,
Retrieval, Reporting
and Annotation

-

- 48 month, 1.12.10 - 30.11.14, HEALTH-2010-2.4.5-1
- Partners: 21
- http://medall-fp7.eu/
Supplementing classic epidemiology with molecular data integrated analysis

MeDALL knowledge

• Public resources
 80 sources + ontologies, 500k nodes, 3 m edges
• Literature review “phenotypes”
• Literature review “Allergy genes”
• Cohort variable harmonisation
 14 birth cohorts + 1 cross-sectional study
• Partners, tasks, documents
• “Omics” data (primary analysis results)

https://ssl.biomax.de/medall/

Registration requests: medall@biomax.com

Researchers, Organisations, WPs,
Tasks, Data

Edit your collaborations,
responsibilities, Skype, ….

Mining public resources

3 hierarchic levels
3 main categories: trigger, organ,
type
50 sub-categories
6522 manually validated terms

… projected onto public knowledge

Knowledge model: document
management specific adaptations

Review results:
Web Input form

Searches supporting
the review flow

Harmonisation knowledge sub-model

MeDALL “Phenotype database”

New: Geographic visualisation
Ozone-level 2010 average: metropolitan areas - rural areas

47

Airway Disease PRedicting Outcomes through Patient Specific
Computational Modelling (AirPROM)

- 50 month, 1.3.11-28.2.16
- Partners: 34
- call: ICT-2010.5.3 VPH call

Image analysis and omics based computational models of the airways to
unravel the pathophysiological mechaims in asthma and COPD

Multi-scale model integration
patient-specific integrated multi-scale models to predict the natural history
& response to therapy in airway disease

Genes & Proteins

Cell structure-
function:
Smooth muscle cells,
airway epithelial cells…
Tissue structure-
function:
Airway remodelling in
asthma & COPD

Organ structure-
function:
Mechanics, ventilation,
perfusion

Clinical medicine:
Asthma
COPD

AirPROM partner

Consortium Membership
•11 EU countries

•22 Academic partners
■
•3 SMEs
•2 Large industry partners
•European Respiratory Society
■ ■ •2 patient organisations ELF, EFA
■
■ ■
•WP Leads from 6 EU Countries
■
■ ■
■
European Approach Essential
■ •Breadth of expertise
•Clinical validation (14 clinical centres)
•Exploitation

Clinial partners

Multi-scale Data

Cross-sectional,
Longitudinal

Intervention
Studies

AirPROM automated data flows
WP1: clinical data

CT
WP2: omics data

morphology WP4: computational
patient anatomy tools

model, simulation result
WP7: KM

WP5: macro scale
large airway
WP3: micro scale
inform, constrain
model validate

WP6: macro scale WP8: patient specific
small airway multi scale model

AirPROM Knowledge
• Collaboration network (partners, tasks, data/models)
• 8 computational models I/O parameter semantic descriptions
 Cell model
 Tissue model
 Perfusion model …
• AirPROM clinical data
 15 control, 57 asthma
 Anthropometrics, Spirometry
• Link to image data
• Full text document search
• Public knowledge
 Gene function (EntrezGene, UniProt, MGI)
 Gene - disease association (OMIM, CTD, PubMed)
 Gene - compound association (CTD, PubChem, PubMed)
 Pathways (KEGG, Reactome)
 Protein-protein interactions (MINT, DIP, IntAct)
 ~100 data sources
 Network of ~2 million connections
• Omics data

AirPROM KM tasks

• Ensure data flow: provide a secure federated data retrieval, exchange, processing
and warehousing infrastructure
• Semantically integrate the clinical, biobanking physiological, genetic, experimental
and imaging data
• Enable data analysis by providing data matrices and integration with algorithms and
tools for network inference
• Formats to support e.g. ANSYS, ISA-TAB, CGNS, MAGE, SBML, CDISC
• Ontologies to support SNOMED, FMA anatomy ontology, Bio-Physical Ontology
• MIBBI meta-information definitions
• Expected data volume: lower Terabyte region

Simple data mapping

Set rules
for import

Data to be imported
(e.g. from an Excel
spreadsheet)

Example:
Tabular Data Import Define import script or
select existing script

Study cohort variable
harmonisation
Aim:
To provide a template to facilitate
harmonization between pre-existing
cohorts and support the design of
emerging ones.
Bank 2

Bank 3
Data
Bank
1 common Pool
Bank 4

Bank 5

Data Schema Structure:
a nested hierarchical structure

Modules M1

Data Schema

• Factor analysis carried out on BTS severe
asthma data set to determine underlying
structure/characteristics of the dataset

• The underlying structure/ factors were then
used to inform the domains and themes to order
the data.

Integrated computational
models

Linked, secure image data
access

High-performance storage system

Tera- to Petabyte data storage for image and image analysis data
• AN1-PZ1.storage.pionier.net.pl
• Certificate based
• sFTP, SSHFS, GridFTP, WebDAV
• access with e.g.

• CT-image data for 35 subjects
• Initial image analysis data

Public Knowledge, disease
centred

Semantically integrated
knowledge

Immune response associated
genes

Pathway - compound
centred knowledge

R interactive view item

79

R interactive view item

80

Synergy-COPD
“Modelling and simulation environment for systems medicine
(Chronic obstructive pulmonary disease -COPD- as a use case)”

- Start: 1.2.11
- Duration: 3 years
- Partners: 9
- call: ICT-2010.5.3 VPH call
- see: www.Synergy-COPD.org

Integration of models at metabolic (muscle TCA, Respiratory chain, ATP diffusion)
cellular (immune system) and organ (lung biophysics, gas diffusion blood flow) level
Clinical decision support
Software with translation into clinical praxis

SYNERGY, KM tasks
 Clinical data from BioBridge, PAC-COPD + ECLIPSE
 Experimental methods:
– Phenotypes: (respiratory symptoms (wheezing, asthma), rhinitis, dermatitis, IgE? to
common inhaled allergens, and their longitudinal changes)
– transcriptome
– proteomics (targeted)
– metabolomics
 Data matrices for and integration with algorithms and tools for network inference
 Integration of models (SBML, CellML)

Knowledge model
for semantic mapping

Aims:
 Find model and experimental data parameters which
are similarily described
 Use experimental data to validate theoretical models
 Connect Models which share similar Model
Parameters

Model and data parameter
concept
Data parameter
Model parameter • instantiates a certain
 instantiates a certain parameter in the Life Science
parameter in a model World
 has ontological • occurs as descriptor or
description measurable in experimental or
anthropometric data
• has ontological description

Parameter semantic annotation

Context specific
Generic • context specfic parameter
 general parameter information, true for a given
information, true in any model/study only
context • shared assignment to
 assigned to parameter parameter + model/study
only • e.g. unit, Input/Output
 e.g. semantic description

Parameter semantic annotation

Molecular level model (SBML import)
 MIRIAM mapping based reference entity association
 check for identical MIRIAM mapping before re-using existing
model parameter by name
 create model specific name “parameter_model” if non-identical
Supra-molecular level model (manual
parameter generation)
 create semantic annotation based on search result for free text -
ontology mapping
 search for existing parameter with same semantics i.e. on-the-fly
network similarity search between search result + Model
parameter context

Mapping concept
Use experimental data to validate theoretical models
 Connect Element:Model Parameter:Instances
with
Element:Parameter:Instances

Connect models which share similar Model
Parameters
 Connect Element:Model Parameter:Instances
with
Element:Model Parameter:Instances

Mapping method
Context:Parameter Description:Instance_B

Ontology:A:54645 Element:Parameter:Instance_B

Ontology:A:5461

Ontology:B:987723

Ontology:C:21365
Ontology:A:54632

Element:Compound:Oxygen

Element:Model Parameter:Instance_A

Context:Model Parameter Description:Instance_A

Parameter semantic description to
annotation mapping

Synergy-COPD Knowledge Portal

https://synergy.linkcare.es/

Semantically integrated information - Types

 semantically described deterministic models
 probabilistic networks
 existing knowledge (PPI, Pathways, ..)
 experimental data
 clinical data
 primary data analysis results (differential expression, overrepresented pathways,
..)

95

Semantically integrated information -
Results
 All clinical and experimental data from the BioBridge 8-week study
 Differential expression analysis
– 6 analyses, 5422 mapped proteins in total
 Probabilistic network
– 1 network, 4989 mapped proteins, 14 physiologic parameters
=> 1895 common proteins
 5 deterministic models
– Electron chain 18/9/1 (proteins/common proteins to PN/Diff expression)
– TCA 16/11/5
– Central metabolism 11/8
– Gas exchange 13/2 PaO2, VO2max
– Spatial heterogeneities 13/3 PaO2, VO2max, Ventilation

96

Public knowledge sources

 80 000 genes
 112 000 proteins
 2 826 Pathways (677 KEGG, 1 276 biochemical, 202 SBML, 55 COPD,
8 user defined, 608 Reactome)
 26 Ontologies (1.3 M concepts)
 > 80 public databases ( > 500 M objects including > 20 000 diseases,
> 2 M compounds)

97

Resulting knowledge network

 1.5 M protein-protein interactions (80k experimental)
 330k gene - compound associations
 225k gene - function associations
 120k gene - disease associations
 36k gene - pathway associations
 250 semantic model and clinical parameter descriptions

98

Interpreting semantically integrated
information
 generate new probabilistic networks from the KB
 explore the connection between probabilistic network(s) and deterministic
models based on concepts (genes, physiology) with direct but especially indirect
connections e.g. via Pathways, PPI, ..
 explore the connections between data analysis results to nitroso-redox related
knowledge

99

Concept for details see WP4 & 5 presentations

Glycolysis
NAD Glc
Clinical ADP Resulting connecting network
data mechanic Myofibrils Glycolysis
work
ATP TCA cycle Cit NAD Glc
NADH Pyr AcCoA
NAD ADP
OAA NADH Succ mechanic
work
ADP ATP
O2 NAD Lac NADH Pyr
transport Electron chain CrP
ATP
diffusion
CrP ROS NAD Lac
Deterministic models
COPD knowledge base ATP

Data clinical/ CrP
experimental

Selection of hubs
Oxidative
phosphorylation

TCA COPD KB
Cycle
network
Glycolysis search
Probabilistic network Physiological
measurments

Selected candidates to explore connections

TNFRSF25
HDAC7
IL11RA

PaO2
TCA cycle
TP63 IL17D
SIRT3
MEF2D
HDAC9
VO2maxkg

Glycolysis
SIRT5

Complex1 IL1R1
MEF2D Complex3 TNFRSF21
Complex5 CXCR4
MYC
FGFR1
Complex 4 IGF1
Protein
ITGB11
Carbonylation
IL1
SIRT4 VO2max IL22
VE IL17A
HDAC1 HDAC4
FOXO4 SMAD1 IL1RAPL1
SIRT2 HIF1A
SMAD4
FOXO1

Electron chain

Searching connecting networks by PPI

102

PPI based networks

Glycolysis candidates

Glycolysis model

103

Searching connecting networks by PPI

 Glycolysis 11 model proteins, 27 candidates
- PPI with good experimental evidence (78 456)
- 428 protein net, 7 Glycolysis model - 10 candidates
- PPI Two Hybrid (3 743), 55 protein net
- all PPI (>1.5 Mio), 757 protein net

 Electron Chain 18 model proteins, 23 candidates, 193 protein
net

 TCA cycle 16 model proteins, 21 candidates, 199 protein net

104

Overrepresentation in connecting network
Central metabolism model - Glycolysis candidates: 167 pathways

105

Summary

• Flexible knowledge modelling
• Different levels of access
• Exchange within, between and outside of projects
• Knowledge network “background” for data
analysis and mining

Data Analysis and Knowledge Management using BioXM in MeDALL, AirPROM and Synergy-COPD

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Data Analysis and Knowledge Management using BioXM in MeDALL, AirPROM and Synergy-COPD