Using ontologies to do integrative systems biology

Using ontologies to do integrative systems biology

Chris EveloDepartment of Bioinformatics - BiGCaTMaastricht University @[email protected]

mailto:[email protected]

Faculty of Health, Medicine and Life Sciences

Typically we want to:

• Find studies.• Process data.• Integrate.• Evaluate.• Combine with yet

other data.


Systems Biology Issues:• Environment• Multi-compartment• Different levels of gene expression cascade

(multi-omics)

Needs:• Link information from different analysis

techniques• Combine many studies (store study design)


Using ISA to be able tofind studies

http://dx.doi.org/10.1038/ng.1054



Why a study capturing application?

New studies can be performed based on old data

Translational comparisons (mouse, human, rat etc)

Structured storage

Facilitate collaborations between groups- Data sharing on joined project- Start a collaboration

What do we need to accomplish this

Acceptance- Using standards (e.g. ISA-TAB & MAGE-TAB)- User friendly (interface via web browser)- Open source- Examples

Collaboration- Ontologies- Security of data (log-in and store data locally)- Open source (make own module)

Study capturing module

Transcriptomics module

Metabolomics moduleSimple assay module

Any new module

Feature layer

Web input Web output

Tool-

chain

Tool

-cha

in

dbXP: a total study capturing solution


GSCF

TemplatesTemplatesTemplatesTemplatesTemplatesTemplates

GroupsGroups

Protocols

Protocols

Subjects

Subjects

Samples

Samples

EventsEvents

Query module

Structured querying

Structured querying

Full-text queryingFull-text querying

Profile-based analysis

Profile-based analysis

Study comparison

Study comparison

Pa

thw

ays, G

O, m

eta

bo

lite p

rofile

s

Simple Assay moduleBody weight, BMI, etc.

Epigenetics module

Transcriptomics module

Raw data Nimblegen Illumina

Resulting Genome Feature data

Clean CPG island

data

Raw data cell files

Result datap-valuesz-values

Clean datagene

expression

Web user interface

dbNP Architecture

AssaysAssays

customprogramscustom

programscustom

dbscustom

dbs

GSCF

TemplatesTemplatesTemplatesTemplatesTemplatesTemplates

GroupsGroups

Protocols

Protocols

SubjectsSubjects

SamplesSamples

EventsEvents

AssaysAssays

NCBOOntologies

Data im

portxls, cvs, text

Molgenis EBIrepository

customdbs

Output xls ISAtab

API

customprograms

web interface

Generic Study Capture FrameworkData input / output

Used in European Projects

Food4me (Dublin)

NU-AGE (UNIBO, Bologna)

Bioclaims (UIB, Palma)

Nutritech (TNO, Zeist)

EuroDish (WUR, Wageningen)

ITFoM (proposed for metabolic syndrome studies)


Process the data…

Epigenetics DNA Methylation Pipeline

RQC, processing

RQC, processing

RQC, processing

SequenceQC, processing

Statistical analysis

Raw dataNimblegen

Raw sequencing data

MeDIP, BIS-Seq

Raw dataIllumina

CleanDNA

methylation

data(GenomeFeatureFormat)

Result datawith

p-values(GFF)


Connecting to Pathways:

1) Prepare data for pathway analysis

2) Connect processing pipelinesPathVisioRPC used from arrayanalysis.org see: http://pathvisiorpc.wordpress.com

3) Store Pathway profiles as vectors,Using pathways themselves as a vocabulary C Evelo, K van Bochove & J Saito. Genes Nutr (2011) 6: 81-87Answering biological questions - querying a systems biology database for nutrigenomics

4) Allow queries for studies with same outcome

http://pathvisiorpc.wordpress.com/

http://dx.doi.org/10.1007/s12263-010-0190-x

http://dx.doi.org/10.1007/s12263-010-0190-x

http://dx.doi.org/10.1007/s12263-010-0190-x


Integrate

ExampleWikiPathway Pathway

Pathway on glycolysis. Using modern systems iology annotation.

And genes and metabolites connected to major databases.

Find the pathways:Biological processes in duodenal mucosa affected by glutamine administration

number of genes

Pathway Changed Up Down Measured Total Z Score

Hs_Mitochondrial_fatty_acid_betaoxidation 6 6 0 16 16 4.456

Hs_Electron_Transport_Chain 17 17 0 85 105 4.278

Hs_Fatty_Acid_Synthesis 5 5 0 21 22 2.757

Hs_Fatty_Acid_Beta-Oxidation 6 6 0 31 32 2.424

Hs_mRNA_processing_Reactome 16 6 10 118 127 2.402

Hs_Unsaturated_Fatty_Acid_Beta_Oxidation 2 2 0 6 6 2.342

Hs_HSP70_and_Apoptosis 4 4 0 18 18 2.299

Hs_Oxidative_Stress 5 5 0 27 28 2.097

Hs_Fatty_Acid_Omega_Oxidation 3 3 0 14 15 1.915

Hs_Proteasome_Degradation 8 8 0 60 61 1.629

Hs_RNA_transcription_Reactome 5 5 0 38 40 1.25

Hs_Irinotecan_pathway_PharmGKB 2 1 1 12 12 1.154

Hs_Synthesis_and_Degradation_of_Ketone_Bodi

es_KEGG 1 1 0 5 5 1.023


Connecting to other data

We both need Study Capturing


If the mountain will not come to Mahomet, Mahomet must go to the mountain.

Other repositories (like dbXP!) have better study descriptions.Integrate in Sage Synapse.

Pathway visualisation missing: integrate PathVisio in Synapse (started).

PathVisio

• Data modeling and visualization on biological pathways• Uses gene expression, proteomics and metabolomics data • Can identify significantly changed processes

www.pathvisio.org

Martijn P van Iersel, Thomas Kelder, Alexander R Pico, Kristina Hanspers, Susan Coort, Bruce R Conklin, Chris Evelo (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9: 399


Understandinggenomics

ExampleWikiPathways Pathway

Pathway on glycolysis. Using modern systems biology (MIM) annotation.

And genes and metabolites connected to major databases.


adding data =adding colour

ExamplePathVisio result

Showing proteomics and transcriptomics results on the glycolysis pathway in mice liverafter starvation.

[Data from Kaatje Lenaerts and Milka Sokolovic, analysis by Martijn van Iersel]

Download PathwaysWeb services

SPARQL endpoint

How to do data visualization?

Connect to Genome Databases


Backpages link to databases

BridgeDbhttp://dx.doi.org/10.1186/1471-2105-11-5

Martijn van IerselBiGCaT Maastricht

http://dx.doi.org/10.1186/1471-2105-11-5

Problem: Identifier Mapping

?Agilent probeset

A65_P12450

Entrez Gene 3643

Solution: Built-in Mapping

• Generic bioinformatics platforms should have identifier mapping built-in.

BioConductorPathVisio Cytoscape...

Batteries Included

• Ensembl Biomart• Synergizer• CRONOS• DAVID• AliasServer• MatchMiner• OntoTranslate

or• Local database

Problem: Which mapping service?

BridgeDB: Abstraction Layer

interfaceIDMapper

classIDMapperRdb

relational database

class IDMapperFile

tab-delimited text

classIDMapperBiomart

web service

The BridgeDb Framework: Standardized Access to Gene, Protein and Metabolite Identifier Mapping Services. Martijn P van Iersel, Alexander R Pico, Thomas Kelder, Jianjiong Gao, Isaac Ho, Kristina Hanspers, Bruce R Conklin, Chris T Evelo. BMC Bioinformatics 2010, 11: 5.

CyThe-saurus Wiki

PathwaysPathVisio

NetworkMerge

BridgeDb

Internet webservices

BioMartBridgeDb

-REST

LocalDatabas

e

Tab-delimitedtext files

Tools

MappingServices

PICR

Cytoscape Plugins

BridgeDb interface

1: JAVA interface 2: REST interface

API Overview

BridgeDb.connect(...)IDMapper.mapID(...)

Xref.getUrl()DataSource.getUrl()

Easy & Flexible Code

REST API

ILMN_1713029 Illumina3255967 AffyNP_001025186 RefSeqIPI00005930 IPIGO:0042752 GeneOntologyNM_033282 RefSeq3255968 Affy94233 Entrez GeneENSG00000122375Ensembl Human234226_at AffyA6NEB4 Uniprot/TrEMBL0001780601 IlluminaGO:0008020 GeneOntology606665 OMIMA_23_P24234 Agilent14449 HUGO

http://webservice.bridgedb.org/Human/xrefs/L/1234

REST API

http://webservice.bridgedb.org/Human/xrefs/L/1234http://webservice.bridgedb.org/Human/search/ENSG00000122375http://webservice.bridgedb.org/Human/attributeSethttp://webservice.bridgedb.org/Human/propertieshttp://webservice.bridgedb.org/Human/targetDataSources http://webservice.bridgedb.org/Human/attributes/L/3643http://localhost:8183/Human/xrefs/L/3643

http://<Base URL>/<Species>/<function> [ /<argument> ... ]\

R Example

Problem: Custom Microarrays

Custom probe #QXZCY!34

?

EnsMartCustom

table

Solution: Stacking

CyThesaurus

MIRIAM and Identifiers.org

Regular expression for autodetection Pattern for

generating URLs

Link to documentation

Availibility

www.bridgedb.org

BMC Bioinformatics. 2010 Jan 4;11(1):5.

[email protected]/blog

Visualizing fluxes on metabolic pathways 46

Data

Metabolite

Flux

Innovate using BridgeDB

Integrating it allVisualizing fluxes, data and annotation


Extending pathways, how to do it?

Network approaches to extend pathwaysE.g. most pathways don’t have miRNA’s

Adding miRNA’s


Pathway Loom, weaving pathways

Adding miRNA’s clutters

PathVisio RI plugin provides backpage info

microRNAs in pathway analysis. The Regulatory Interaction plugin offers a suitable middle-ground between not including any miRNAs in pathways, which misses this regulatory information, and including all validated miRNA-target interactions, which clutters the pathway. After loading interaction file(s), selecting a pathway element shows the interaction partners of this element and their expressions in a side panel. This allows for the detection of potential active regulatory mechanisms in the study at hand.

http://www.bigcat.unimaas.nl/wiki/images/f/f6/VanHelden-poster-nbic2012.pdf

http://www.bigcat.unimaas.nl/wiki/images/f/f6/VanHelden-poster-nbic2012.pdf


Or consider pathway as a network

GPML Cytoscape Pluginhttp://www.pathvisio.org/wiki/Cytoscape_plugin

PPS1Liver

All pathways

Pathways with high z-score grouped together.

Explains why there are relatively few significant genes, but many pathways with high z-score.

Cytoscape visualization used to group

Robert Caesar et al (2010) A combined transcriptomics and lipidomics analysis of subcutaneous, epididymal and mesenteric adipose tissue reveals marked functional differences. PLoS One 5: 7. e11525http://dx.doi.org/doi:10.1371/journal.pone.0011525

http://dx.doi.org/doi:10.1371/journal.pone.0011525

Explore pathway interactions

Thomas Kelder, Lars Eijssen, Robert Kleemann, Marjan van Erk, Teake Kooistra, Chris Evelo (2011) Exploring pathway interactions in insulin resistant mouse liver BMC Systems Biology 5: 127 Aug. http://dx.doi.org/doi:10.1186/1752-0509-5-127

http://dx.doi.org/doi:10.1186/1752-0509-5-127

What we used

Non-redundant shortest paths in a weighted graph.

1. A set of pathways2. An interaction network3. Weight value for all edges

= experimental expression of connected genes.

Pathway interactions and what causes them

An indirect interaction between the Axon Guidance and Insulin Signaling pathways in the network for the comparison between HF and LF diet at t = 0. Left: Network representation of the identified path between the two pathways, consisting of three proteins Gsk3b, Sgk3 and Tsc1. Right: The location of these proteins in the KEGG pathway diagrams. The newly found indirect interactions have been added in red.

Pathway interactions and detailed network visualization for the interactions with three apoptosis related pathways for the comparison between HF and LF diet at t = 0. A: Subgraph of the pathway interaction network, based on incoming interactions to three stress response and apoptosis pathways with the highest in-degree. Pathway nodes with a thick border are significantly enriched (p < 0.05) with differentially expressed genes. B: The protein interactions that compose the interactions between the three apoptosis related pathways and their neighbors in the subgraph as shown in box A (see inset, included interactions are colored orange). Protein nodes have a thick border when their encoding genes are significantly differentially expressed (q < 0.05).

We tried to make it easier with

The CyTargetLinker Cytoscape PluginExtending pathways on the fly.

Provided databases with the plugin: • miRNAs with targets• Transciption Factors with targets• Drug – Target Interactions• ENCODE derived databases

Extend with your own.

MiRNAs of Interest

miRNA target information from mirTarBase

Collection of miRNA-target gene interactions in the miRTarBase database with 1,715 genes,286 miRNAs and 2,817 interactions.

miRTarBase as a target interaction network

miRNAs associated with colorectal cancer extended with validated target genes

human ErbB signaling pathway extended with validated microRNA regulation

RDF 3

Nanopub

Data 1

RDF 1

Descriptor

Data 2

RDF 2

Descriptor

Data 3

Descriptor

RDF 4

Nanopub

Data 4

Descriptor

RDF Data Cache

Semantic Data Workflow Engine

SparqlWeb Service API WebServices

OPS GUIOPS FrameworkArchitecture. Dec 2011

App Framework

Identity & Vocabulary

Management

Note: Things may change!

Public Vocabularies

Chemistry Normalisation &

Registration

OPS Data Model

Feed in WikiPathwaysrelationships, use BioPAXto create the RDF

And then we have linked data?

Data 1

RDF 1

Descriptor

Data 2

RDF 2

Descriptor

Public Vocabularies

Well yes, for Open PHACTS we do…

Semantic Data Workflow Engine

Identity & Vocabulary

Management

OPS Data Model

Chemistry Normalisation &

Registration

Data 1

RDF 1

Descriptor

Data 2

RDF 2

Descriptor

Public Vocabularies

But really…, what about federated SPARQL queries?

OtherPublic

Vocabularies

Data 1

RDF 1

Descriptor

Data 2

RDF 2

Descriptor

Public Vocabularies

Most often partly…

OtherPublic

Vocabularies

IdentityMapping

If the vocabularies used are different linking just database IDs not good enough.

We need full mappings of ontologies. Identification of overlapping modules.

And maybe… Suggestions for ontologies to use in specific field.

Thanks!

Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant and NIH.

WikiPathways team:• Martijn van Iersel (PathVisio,

BridgeDB)• Thomas Kelder (WikiPathways,

networks)• Alex Pico (US team leader)• Brice Conklin (former US team leader)• Kristina Hanspers (US curation)• Martina Kutmon (CyTargetLinker)• Susan Coort (Regulatory plugins)• Lars Eijssen (Data pipelines)• Anwesha Dutta (Flux visualisation)• Andra Waagmeester (LOOM)• Egon Willighagen (Open Phacts)

Thanks!

Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant.

Analyzing GO representation in pathways using an independent library for ontology analysis

Combining efforts and information to increase biological understanding

Structuring biological data

• Gene Ontology (GO) – Protein function or localization– Hierarchically structured terms– 3 topics (namespaces)

• Biological process• Molecular function• Cellular component

– Disadvantage• No information on interactions

Structuring biological data

• Pathways– Network of interactions– Structural overview of elements in the

pathway

– Disadvantages:• Missing structure

of interacting pathways

• Overlap and abundance in pathways

Analysis based on structures

• Uses:– Better overview of the data– Increased biological understanding

• Challenges in the field:– Difficulty comparing algorithms– Good work may be overlooked– Redundant efforts– Out-of-date algorithms used– Comparison extremely difficult

Goals:

• Develop an independent library for ontology analysis in which efforts can be combined

• Increase biological understanding by combining knowledge on pathways and gene ontology.

Independent library for ontology analysis

• Open source:– Collaboration– Clear view of the algorithm– Free use– Minimalizing redundant efforts

• Usable for multiple ontology's and identifiers

Combining Pathways and GO

• Display information on the function of the pathway

• Make a comparison between pathways• Quality control

– Single pathway– List of pathways

Materials

• PathVisio– Open source Tool for visualizing and analyzing

pathway data• BridgeDb

– id mapping framework for bioinformatics• WikiPathways

– Community curated pathway data source

Independent Library• Manager input:

1. Ontology Terms (File)2. Map of term with

identifier3. Method Selection

Methods

• Simple OverRepresentation method– Percentage of identifiers in GO Term

• Basic Fisher’s exact test– 2x2 contingency table Id’s linked to

GOGenes not linked to GO

Id’s in pathway a b a + bId’s not in pathway c d c + d

a + c b + d n

Plug-in

• Panel for the analysis of a single pathway– Display GO terms in a table with score– Highlight matches– Save results

• Menu Item for analyzing a list of pathways– Select a folder containing pathway files– Individual result files– File containing all results with extra info

Single Pathway analysis

Single Pathway analysis

• Regulation of blood pressure• Angiogenesis• Others:

– G-protein coupled receptor– proteolysis

name scoreG-protein coupled receptor signaling pathway 35%regulation of cell proliferation 29%proteolysis 29%regulation of blood pressure 29%response to drug 29%regulation of vasoconstriction 29%positive regulation of apoptotic process 29%negative regulation of cell growth 23%kidney development 23%elevation of cytosolic calcium ion concentration 23%

Homo sapiens: Mus musculus:name scorekidney development 50%G-protein coupled receptor signaling pathway 50%

response to drug 37%

negative regulation of cell proliferation 37%

positive regulation of apoptotic process 37%

regulation of blood pressure 37%

response to salt stress 25%regulation of systemic arterial blood pressure by circulatory renin-angiotensin 25%

arachidonic acid secretion 25%

blood vessel development 25%

Multiple Pathway analysis

Multiple Pathway analysissignal transduction

xenobiotic metabolic processoxidation-reduction process

metabolic processG-protein coupled receptor signaling pathway

gene expressionnerve growth factor receptor signaling pathway

apoptotic processsynaptic transmission

DNA repairmitotic cell cycle

innate immune response

0 2 4 6 8 10 12 14 16 18Biological Process12 of 105 terms

cytoplasmcytosolnucleus

plasma membranemembrane

integral to membranemitochondrion

nucleoplasmendoplasmic reticulum membrane

extracellular regionendoplasmic reticulum

integral to plasma membranemicrosome

extracellular space

0 10 20 30 40 50 60 70 80Cellular Compontent12 of 26 terms

Goals:

• Develop an independent library for ontology analysis in which efforts can be combined

• Increase biological understanding by combining knowledge on pathways and gene ontology.

Independent library

• Reads GO terms from file• Mapping from term to identifier• Analysis on sample data• Framework enables more methods to be

added

Combining Pathways and GO

• Single Pathway:– More information on pathway– Quality control possible

• Pathway List:– Separate results for every pathway– Enables structuring possibility’s– Quality control possible

Using ontologies to do integrative systems biology

Education

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