Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”

1. @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

2. 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

3. 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

4. • 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

5. Browsing Normal Disease Pathways
Signaling by EGFR
Pathway in Cancer
Signaling by EGFR
Pathway

6. The ‘Ideal’ Reactome Pathway
PI3K/Akt Pathway

7. Amyloid Pathway
Google-map style pathway diagrams with data overlays
Pathway Browser

8. 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.

9. 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

10. 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

11. 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

12. 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

13. 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

14. Implementation of HotNet in FI Cytoscape Plugin
Pre-calculated
heat-kernel for FI
Network in R
RESTful API
FI Cytoscape Plugin

15. Implementation of HotNet in FI Cytoscape Plugin

16. Implementation of HotNet in FI Cytoscape Plugin

17. Implementation of HotNet in FI Cytoscape Plugin

18. Yes….we’re working on it!

19. 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.

20. 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.

21. Acknowledgements
Ministry of Economic
Development and Innovation
• Marija Orlic-Milacic
• Karen Rothfels
• Lisa Matthews
• Marc Gillespie
• Guanming Wu
• Irina Kalatskaya
• ChristinaYung
• 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