Transcript Reactome Functional Interaction (FI) Network

Reactome Functional
Interaction (FI) Network
Steve Jupe
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Motivation for Pathway and Network Analysis in Cancer
• “Exclusivity principle” in cancer.
• once a gene is mutated in a pathway, the selective growth advantage
incurred by a second mutated gene in the same pathway is minimal.
• rare for a single tumor to have multiple genes altered in the same
pathway.
• Analyzing mutated genes in a pathway context.
• reveals relationships among these genes.
• facilitates hypothesis generation on roles of these genes in disease
phenotype.
• identifies genes on the affected pathways as therapeutic targets.
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Analysis Workflow – Cancer Microarray
Experiment: Gene expression profiling of pancreatic tumour (vs normal tissue).
Output: list of genes that show significant changes in expression.
Downstream analysis:
1. Validation of the top genes sorted by fold-change (or p-value): artificial cut-off, information loss;
2. Manual reviewing and gene classification based on literature, databases and own prior
experience: time-consuming and bias;
3. GO annotation and pathway analysis (e.g. Reactome): what biological terms or functional
categories are represented in the gene list more often than expected by chance;
4. Network-based analysis. - identify markers not as individual genes but as sub-networks
extracted from protein interaction databases and the resulting sub-networks provide novel
hypotheses for pathways involved in cancer.
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What is the Functional Interaction Network?
• A reliable interaction network based on manually curated pathways
extended with verified interactions
• Covers almost 50% of human Uniprot proteins
• A resource for construction of FI sub-networks based on gene sets
• Tools that:
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Provide the underlying evidence for FIs
Build and analyze network modules of highly-interacting groups
Perform functional enrichment to annotate modules
Expand the network by finding genes related to the experimental data
display source pathway diagrams and overlay with a variety of information
sources such as cancer gene index annotations.
• Method and practical application: A human functional protein
interaction network and its application to cancer data analysis, Wu et
al. 2010 Genome Biology
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Pairwise Problem
• Convert reactions in pathways into pair-wise relationships
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Functional Interaction: an interaction in which two proteins are involved in the
same reaction as input, catalyst, activator and/or inhibitor, or as components in a
complex
Reaction
Functional Interaction
Input1-Input2, Input1-Catalyst,
Input1-Activator, Input1Inhibitor, Input2-Catalyst, Input2Activator, Input2-Inhibtior,
Catalyst-Activator, CatalystInhibitor, Activator-Inhibitor
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Construction of the Reactome FI Network
Reactome Functional Interaction (FI) Network [15%]
Wu et al. Genome Biology 2010
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Coverage after Prediction
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10,956 proteins (9,542 genes)
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209,988 FIs
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5% of network shown here
www.reactome.org
Reactome Pathway Diagram with FI Overlay
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Limitations of web FI overlay
• Simple viewer – no extra tools
• Not easy to produce networks
• Not dynamic – you can’t move the pathway diagram
objects
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Projecting Experimental Data onto FI Network
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A FI Sub-Network Generated from Significant
Network Modules based on a TCGA OV
Expression Data Set
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Cytoscape
• Cytoscape - open source platform for visualizing complexnetworks and integrating these with any type of attribute
data.
• Java-based
• Plug-in development framework
• First public release: July 2002, v0.8
• Current release: 2.8.1
• Website: http://www.cytoscape.org
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Reactome FI Cytoscape Plug-In
• Cytoscape v2.7.0 or later.
• Reactome Homepage (see URL below)
• Menu Bar -> Tools, FI Cytoscape Plugin
• Save jar file caBigR3.jar into Cytoscape plugins folder,
restart.
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http://reactomedev.oicr.on.ca:8080/caBigR3WebApp/Cytoscape/plugins/caBigR3.jar
• Also available as Java Web Start: Cytoscape.jnlp
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http://reactomedev.oicr.on.ca:8080/caBigR3WebApp/Cytoscape/Cytoscape.jnlp
Hinxton 30 March 2011
www.reactome.org
FI Network Analysis Pipeline
Select gene list (mutated, overexpressed, down-regulated,
amplified or deleted genes)
Project genes of interest onto
Reactome F.I. Network
Identify Disease/Cancer Subnetwork
Apply Clustering Algorithms
Apply Pathway/GO Annotation to
each cluster
Generate Biological Hypothesis!
Predict Disease Gene Function
Classify Patients & Samples
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T2D
OvCa
GBM
Breast
Prostate
TCGA
ICGC
Using the Reactome FIs Plug-in
• Choose Plugins, Reactome FIs.
• FI plug-in supports three file formats:
• Simple gene set: one line per gene.
• Gene/sample number pair. Contains two required columns, gene
and number of samples having gene mutated, and an optional
third column listing sample names (delimited by semi-colon ;).
• NCI MAF (mutation annotation file).
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File Formats
Simple
Gene/Sample
NCI MAF
Gene
(mutation
List
Number
annotation
Pairs file)
MSI2
PTPRT
PELO
SLC18A1
TACC2
FAM148B
PRC1
MSTN
ATP6V1G2
APOE
IMPA2
AGER
XPO5
MEST
RREB1
BAT1
WIPI1
CATSPERB
SSR1
VEGFA
EGFL8
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Loading a file
• Choose file
• Choose file format and parameters
• Click OK
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Results Display
• Constructed network is displayed in the Network View
panel using an FI specific visual style
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Pop-up menu
• Right-click an empty space in the network view panel.
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Fetch FI annotations
• Details information on selected
FIs. Three edge attributes are
created: FI Annotation, FI
Direction, and FI Score. Edges
display direction attribute values.
• --> for activating/catalyzing
• --| for inhibition
• solid line for complexes or inputs
• --- for predicted FIs.
• VizMapper tab, Edge Source
Arrow Shape and Edge Target
Arrow Shape values give details.
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Analyze network functions
• Pathway or GO term enrichment analysis for the displayed
network.
• Use Data Panel to:
• Filter enrichment results by FDR value.
• Check “Hide nodes in not selected rows” to display only nodes
in the selected row or rows
• To analyze an unlinked set of genes choose the "Show genes
not linked to others" option when importing data
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Cluster FI network
• Runs spectral partition based network clustering
(Newman, 2006) on the displayed FI network.
• Nodes in different network modules will be shown in
different colours (max 15 colours).
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Analyze module functions
• Pathway or GO term enrichment analysis on individual
network modules.
• Use filter to remove small network modules
• Filter by FDR
• Button selects nodes in the network corresponding to
selected row or rows in the Data Panel.
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Other features - Query FI source
• Right-click edge, select "Reactome FI/Query FI
Source".
• If the FI was extracted from a curated pathway the
original data source(s) is displayed
• Double click a row in the table to show details
• If the FI is predicted the evidence is displayed.
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Other features - Fetch FIs for node
• Select node in the network panel, right click for pop-up
menu, select "Reactome FI, Fetch FIs"
• FI partners for the selected node are displayed in two
sections: Partners that are displayed and partners that are
not displayed.
• Select undisplayed partners and click Add
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Other features - Show pathway diagram (KEGG)
• Select a pathway in "Pathways in Network/Modules" tabs, right click,
select "Show Pathway Diagram”
• KEGG pathways - diagram page is opened. Nodes listed in "Nodes"
column are highlighted in red.
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Other features - Show pathway diagram (Reactome)
• Reactome pathway diagrams open in a separate window.
• For human data the manually drawn pathway diagram is displayed.
• For other species an automated layout is generated.
• Nodes in the Cytoscape network are highlighted in blue on the diagram
• Detailed information is viewed using a pop-up menu item "View
Instance".
• Zoom slider bottom of the window.
• Overview window top-right corner
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Cancer Gene Index annotations
• Cancer Gene Index annotations can be added in two
ways:
• popup menu "Load Cancer Gene Index" when no object is
selected (left)
• popup menu "Fetch Cancer Gene Index" for a selected
node (right)
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Load Cancer Gene Index
• This method loads the tree of NCI disease terms in the left
panel.
• Select a disease term in the tree to select all diagram
objects that have this annotation or one of its sub-terms.
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Fetch Cancer Gene Index
• This method is for viewing detailed annotations for the
selected gene or protein.
• Annotations are sortable by PubMed ID, Cancer type,
status, and several other criteria.
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Cancer Gene Survival Analysis
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Based on a server-side R script that runs
either coxph or Kaplan-Meyer survival
analysis.
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Requires appropriate data file (see online).
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Popup menu “Analyze Module Functions,
Survival Analysis...”
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Results are displayed with a tab "Survival
Analysis" (right).
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You can display multiple analyses.
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Only Kaplan-Meyer analysis will show a plot
file. You can click the file to view the actual
plot (far right).
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Summary
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Reactome is a highly reliable, human-curated database of biological pathways
and reactions.
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Expanded the human curated Reactome pathways by importing pathways from
other databases and integrating predicted FIs: ~50% coverage.
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Functional interaction network provides a powerful way to visualize cancer and
other disease genes to reveal functional relationships among these genes.
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Future
• Implement multiple network clustering algorithms
• Currently spectral partition only
• Future: edge-betweenness, MCL, and hierarchical clustering
• Integrate with other data types: gene expressions,
CNVs…
• Seamless integration with Reactome pathway diagrams
• Expand known cancer pathways by using FIs
• Integrate miRNA/target interactions to study miRNA
activities on cancers
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Software Demo
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