@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