using ontologies to do integrative systems biology
DESCRIPTION
To really get ahead with complex health problems like cancer and diabetes we need to become better at combining different types of studies, including large scale genomics and genetics studies and we need to learn to better combine such studies with biological knowledge we already. Typically that leads to questions like “I did this study with high-fat low fat diet comparison in mice and looked at the transcriptomics results in liver, fat and muscle. Did somebody else maybe do a study like that and publish the data, maybe for proteomics? Could I find that in one of these open data repositories?”. Or, “I did that, can I find which biological pathways are affected most and whether any of the proteins in that pathway is a known target for an existing drug?”. Or even “I did that study, could I find another study that yielded the same kind of biological results even if it was from a different research field with a completely different result?”. To answer this kind of questions we need to describe studies and study results, structure knowledge allow mapping of “equal” things with different identifier schemes and essentially do a lot of mapping to and between ontologies. More and more of this is getting real and I will try to describe some of that. Homepage for this webinar is here: http://www.bioontology.org/ontologies-in-integrative-systems-biology It is part of this series: http://www.bioontology.org/webinar-seriesTRANSCRIPT
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Using ontologies to do integrative systems biology
Chris EveloDepartment of Bioinformatics - BiGCaTMaastricht University @[email protected]
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Faculty of Health, Medicine and Life Sciences
Typically we want to:
• Find studies.• Process data.• Integrate.• Evaluate.• Combine with yet
other data.
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Faculty of Health, Medicine and Life Sciences
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)
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Faculty of Health, Medicine and Life Sciences
Using ISA to be able tofind studies
http://dx.doi.org/10.1038/ng.1054
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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
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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)
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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
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Faculty of Health, Medicine and Life Sciences
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
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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
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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)
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Faculty of Health, Medicine and Life Sciences
Process the data…
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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)
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Faculty of Health, Medicine and Life Sciences
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
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Faculty of Health, Medicine and Life Sciences
Integrate
ExampleWikiPathway Pathway
Pathway on glycolysis. Using modern systems iology annotation.
And genes and metabolites connected to major databases.
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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
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Faculty of Health, Medicine and Life Sciences
Connecting to other data
We both need Study Capturing
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Faculty of Health, Medicine and Life Sciences
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).
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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
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Faculty of Health, Medicine and Life Sciences
Understandinggenomics
ExampleWikiPathways Pathway
Pathway on glycolysis. Using modern systems biology (MIM) annotation.
And genes and metabolites connected to major databases.
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Faculty of Health, Medicine and Life Sciences
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Faculty of Health, Medicine and Life Sciences
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]
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Download PathwaysWeb services
SPARQL endpoint
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How to do data visualization?
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Connect to Genome Databases
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Faculty of Health, Medicine and Life Sciences
Backpages link to databases
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BridgeDbhttp://dx.doi.org/10.1186/1471-2105-11-5
Martijn van IerselBiGCaT Maastricht
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Problem: Identifier Mapping
?Agilent probeset
A65_P12450
Entrez Gene 3643
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Solution: Built-in Mapping
• Generic bioinformatics platforms should have identifier mapping built-in.
BioConductorPathVisio Cytoscape...
Batteries Included
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• Ensembl Biomart• Synergizer• CRONOS• DAVID• AliasServer• MatchMiner• OntoTranslate
or• Local database
Problem: Which mapping service?
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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.
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CyThe-saurus Wiki
PathwaysPathVisio
NetworkMerge
BridgeDb
Internet webservices
BioMartBridgeDb
-REST
LocalDatabas
e
Tab-delimitedtext files
Tools
MappingServices
PICR
Cytoscape Plugins
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BridgeDb interface
1: JAVA interface 2: REST interface
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API Overview
BridgeDb.connect(...)IDMapper.mapID(...)
Xref.getUrl()DataSource.getUrl()
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Easy & Flexible Code
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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
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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> ... ]\
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R Example
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Problem: Custom Microarrays
Custom probe #QXZCY!34
?
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EnsMartCustom
table
Solution: Stacking
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CyThesaurus
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MIRIAM and Identifiers.org
Regular expression for autodetection Pattern for
generating URLs
Link to documentation
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Visualizing fluxes on metabolic pathways 46
Data
Metabolite
Flux
Innovate using BridgeDB
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Integrating it allVisualizing fluxes, data and annotation
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Faculty of Health, Medicine and Life Sciences
Extending pathways, how to do it?
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Network approaches to extend pathwaysE.g. most pathways don’t have miRNA’s
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Adding miRNA’s
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Faculty of Health, Medicine and Life Sciences
Pathway Loom, weaving pathways
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Faculty of Health, Medicine and Life Sciences
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Adding miRNA’s clutters
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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
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Faculty of Health, Medicine and Life Sciences
Or consider pathway as a network
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GPML Cytoscape Pluginhttp://www.pathvisio.org/wiki/Cytoscape_plugin
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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
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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
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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.
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Pathway interactions and what causes them
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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.
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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).
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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.
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MiRNAs of Interest
miRNA target information from mirTarBase
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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
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miRNAs associated with colorectal cancer extended with validated target genes
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human ErbB signaling pathway extended with validated microRNA regulation
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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
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And then we have linked data?
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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
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Data 1
RDF 1
Descriptor
Data 2
RDF 2
Descriptor
Public Vocabularies
But really…, what about federated SPARQL queries?
OtherPublic
Vocabularies
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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.
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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)
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Thanks!
Funding. Dutch: IOP, NBIC, NuGO, NCSB. Regional: Transnational University. EU: NuGO and Microgennet, IMI: Open Phacts + Agilent thought leader grant.
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Analyzing GO representation in pathways using an independent library for ontology analysis
Combining efforts and information to increase biological understanding
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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
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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
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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
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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.
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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
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Combining Pathways and GO
• Display information on the function of the pathway
• Make a comparison between pathways• Quality control
– Single pathway– List of pathways
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Materials
• PathVisio– Open source Tool for visualizing and analyzing
pathway data• BridgeDb
– id mapping framework for bioinformatics• WikiPathways
– Community curated pathway data source
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Independent Library• Manager input:
1. Ontology Terms (File)2. Map of term with
identifier3. Method Selection
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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
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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
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Single Pathway analysis
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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%
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Multiple Pathway analysis
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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
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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.
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Independent library
• Reads GO terms from file• Mapping from term to identifier• Analysis on sample data• Framework enables more methods to be
added
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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