NetBioSIG2013-Talk Robin Haw
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Transcript of NetBioSIG2013-Talk Robin Haw
@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
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
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
• 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
Browsing Normal & Disease Pathways���
Signaling by EGFR Pathway in Cancer
Signaling by EGFR Pathway
The ‘Ideal’ Reactome Pathway
PI3K/Akt Pathway
Amyloid Pathway Google-map style pathway diagrams with data overlays
Pathway Browser
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.
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
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
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
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
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
Implementation of HotNet in FI Cytoscape Plugin
Pre-calculated heat-kernel for FI
Network in R RESTful API FI Cytoscape Plugin
Implementation of HotNet in FI Cytoscape Plugin
Implementation of HotNet in FI Cytoscape Plugin
Implementation of HotNet in FI Cytoscape Plugin
Yes….we’re working on it!
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.
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.
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