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NetBioSIG2013-Talk Robin Haw
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Transcript of NetBioSIG2013-Talk Robin Haw
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@robinhaw��� 19th July 2013 ��� Network Biology SIG Meeting www.reactome.org
Reactome Knowledgebase and Functional Interaction (FI) Network Cytoscape Plugin
Ministry of Economic Development and Innovation
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What is Reactome?
• Open source and open access pathway database – 1400+ human pathways encompassing metabolism, signaling, gene
regulation, and other biological processes
• Tools and datasets for browsing and visualizing pathway data, and
interpreting your experimental data.
• Support the open standards used for data exchange, integration,
analysis and visualization
www.reactome.org
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location (GO cell component)
protein (UniProt) or molecule (ChEBI) or complex (GO/PRO) or ncRNA (miRBase) or disease variants (UniProt) therapeutics (ChEBI) CatalystActivity
(GO mol function)
Output 1
Reaction
Input 1
Input 2 Output 2
Regulation (GO biol process)
Data model in a nutshell
• Reactome is a Reaction Network Database • Explicitly describe biological processes as a series of biochemical
reactions and events.
• SBGN Process Descrip/on language: represent mechanis/c and temporal aspects of biological events
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• Not new to Reactome! • Reorganized the Pathway Hierarchy. • Modified the Data Model. • Updating the Pathway Browser. • Annotate:
• An infection introduces new proteins into the cell.
• The amount of a normal protein is changed and this changes the function of the protein.
• A mutation (somatic or germline) changes the function of a protein.
• Mode of action of anti-cancer therapeutics.
Focusing on Disease Curation
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Browsing Normal & Disease Pathways���
Signaling by EGFR Pathway in Cancer
Signaling by EGFR Pathway
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The ‘Ideal’ Reactome Pathway
PI3K/Akt Pathway
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Amyloid Pathway Google-map style pathway diagrams with data overlays
Pathway Browser
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Reactome Functional Interaction (FI) Network
• Gateway to the Reactome database.
• Annotation candidates for Reactome pathways.
• Network-based data analysis platform for high-throughput data analyses for cancer and other diseases.
• Analyzing mutated genes in a network context:
– reveals relationships among these genes.
– can elucidate mechanism of action of drivers.
– facilitates hypothesis generation on roles of these genes in disease phenotype.
– reduces hundreds of mutated genes to < dozen mutated pathways.
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Creation of the Reactome FI Network Human PPI [45-47]" Fly PPI [45]"
Domain Interaction [52]"
Prieto’s Gene Expression [50]"Lee’s Gene Expression [49]"
GO BP Sharing [51]"Yeast PPI [45]"
Worm PPI [45]"
PPIs from GeneWays [53]"
Data sources for Predicted FIs"
Reactome [23]"
Panther [60]"
KEGG [63]"TRED [64]"
NCI-BioCarta [62]"
NCI-Nature [62]"
CellMap [61]"
Data sources for "Annotated FIs"
Naïve Bayes Classifier"
trained by"
validated by"
Predicted FIs" Annotated FIs"
Reactome FI Network"
Mouse PPI
2,3
2 2
2,3
2
2,3
ENCODE TF/Target
273K interactions and 11K proteins
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Reactome FI Cytoscape Plugin
• Software platform based on the FI network for performing network based data analysis for cancer and other diseases.
• MySQL DB backend supported by RESTful API
• Access statistics: 4K unique IPs (last 2 years)
Server Side in Spring
Container Cytoscape Database in
MySQL
hibernate XML
Messaging
Reactome API RESTful WS
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Cytoscape FI Plugin Pipeline
Your gene list (e.g. mutated, over-expressed, down-regulated, amplified or deleted genes in disease samples)
Project genes of interest onto Reactome F.I. Network
Identify Disease/Cancer Subnetwork
Apply Clustering Algorithms
Apply Pathway/GO Annotation to each cluster
Perform Survival Analysis (optional)
Generate Biological Hypothesis! Predict Disease Gene Function
Classify Patients & Samples
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Clustering of TCGA Breast Cancer Mutations
NCI MAF (mutation annotation file)
Signaling by Tyrosine Kinase receptors
NOTCH and Wnt signaling
Cadherin signaling
Focal adhesion ECM-Receptor interaction
Signaling by Rho GTPases
Axon guidance
Mucin cluster
Neuroactive ligand-receptor interaction
Calcium signaling
Ubiquitin-mediated proteolysis
M phase G2/M Transition
Metabolism of proteins
DNA repair Cell cycle
Cell adhesion molecules
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Implementation of HotNet Analysis in Reactome FI Plugin
• HotNet is an algorithm for finding significantly altered
subnetworks in a large protein-protein interaction network
• Developed by Ben Raphael’s group at Brown in 2011
• Published - Vandin et al 2011. J Comp Biology 18(3): 507-522
• Employs a heat diffusion model to simultaneously analyze
both the mutation frequency and network topology.
• HotNet software is downloadable although there are some requirements:
• MatLab • Python
• Cytoscape plugin to view the results
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Implementation of HotNet in FI Cytoscape Plugin
Pre-calculated heat-kernel for FI
Network in R RESTful API FI Cytoscape Plugin
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Implementation of HotNet in FI Cytoscape Plugin
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Implementation of HotNet in FI Cytoscape Plugin
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Implementation of HotNet in FI Cytoscape Plugin
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Yes….we’re working on it!
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Continuing Priorities Reactome Database and Website • Increase the number of curated proteins and other functional entities. • Supplement normal pathways with variant reactions representing disease
states. • Improve annotation consistency and enrich the data model.
• Continued support for SBML, SBGN, BioPAX and PSI-MITAB.
• Enhance the web site and other resources to meet the needs of a growing and diverse user community.
Reactome FI Network and Cytoscape plug-in
• Yearly FI Network Update.
• Adding miRNA/target interaction data to FI network.
• Native Reactome pathway diagrams in Cytoscape. • Porting plugin from v2.8 to 3.
• Multiple data type integration.
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Conclusions
• Reactome is a highly reliable, curated database of biological pathways.
• Web site provides tools and datasets for visualizing pathway data and interpreting your experimental data.
• All data and software are open to public; no licensing required.
• Cytoscape FI network plugin provides a powerful way to visualize and analyze cancer and disease data sets.
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Acknowledgements
Ministry of Economic Development and Innovation
• Marija Orlic-Milacic • Karen Rothfels • Lisa Matthews • Marc Gillespie • Guanming Wu • Irina Kalatskaya • Christina Yung • Michael Caudy • David Croft • Eric Dawson • Adrian Duong • Phani Garapati
• Bijay Jassal • Steve Jupe • Maulik Kamdar • Bruce May • Antonio Fabregat Mundo • Veronica Shamovsky • Heeyeon Song • Joel Weiser • Mark Williams • Henning Hermjakob • Peter D’Eustachio • Lincoln Stein