Pathways & Networks analysis COST Functional Modeling Workshop 22-24 April, Helsinki
GO, Pathway, Network Analysis • Many GO analysis tools also include pathway & network analysis • Ingenuity Pathways Analysis (IPA) and Pathway Studios – commercial software • DAVID – includes multiple functional categories • Onto-Tools – includes Pathways Express tool
Pathways & Networks • A network is a collection of interactions • Pathways are a subset of networks (Pathways are a network of interacting proteins that carry out biological functions such as metabolism and signal transduction.) • All pathways are networks of interactions • Not all networks are pathways
1. Pathways analysis enables: • Analysis of data in the context of molecular mechanisms to move beyond statistical analysis of expression to novel biological insights • Applying biological filters to focus in on the changes most relevant to experimental model • Comparison of affected pathways and phenotypes across platform, time, dose etc (www.ingenuity.com)
What regulatory relationships exist between the genes, proteins in my dataset? Networks Which biological and disease processes are most relevant to my genes of interest? Functional analysis Which well-characterized cell signaling and metabolic pathways are most relevant to my experimental data? Metabolic and Signaling pathways http://www.ingenuity.com
Pathways Resources • Pathguide– the pathway resource list • http://www.pathguide.org KEGG http://www.genome.jp/kegg/pathway.html • Reactomehttp://www.reactome.org BioCychttp://www.biocyc.org • BioCartahttp://www.biocarta.com GenMAPPhttp://www.genmapp.org
Pathways Analysis Guide • Is there commercial pathways analysis software available at your institute? • For commercial/Free pathways packages: what species do they support • your species or finding orthologs for a supported species • What accession type do you need to use? • How complete is your analysis likely to be (are both signaling & metabolic pathways represented, how many pathways, how many of your gene products can be analyzed?).
2. Biological Networks • Networks often represented as graphs • Nodes represent proteins or genes that code for proteins • Edges represent the functional links between nodes (e.g. regulation or binding) • Small changes in graph’s topology/architecture can result in the emergence of novel properties.
Types of interactions • protein (enzyme) – metabolite (ligand) • metabolic pathways • protein – protein • cell signaling pathways, protein complexes • protein – gene • genetic networks
Network example: STRING Database http://string.embl.de/ Sod1 Musmusculus
Database/URL/FTP • DIP http://dip.doe-mbi.ucla.edu • BIND http://bind.ca • MPact/MIPS http://mips.gsf.de/services/ppi • STRING http://string.embl.de • MINT http://mint.bio.uniroma2.it/mint • IntAct http://www.ebi.ac.uk/intact • BioGRID http://www.thebiogrid.org • HPRD http://www.hprd.org • ProtCom http://www.ces.clemson.edu/compbio/ProtCom • 3did, Interprets http://gatealoy.pcb.ub.es/3did/ • Pibase, Modbase http://alto.compbio.ucsf.edu/pibase • CBM ftp://ftp.ncbi.nlm.nih.gov/pub/cbm • SCOPPI http://www.scoppi.org/ • iPfam http://www.sanger.ac.uk/Software/Pfam/iPfam • InterDom http://interdom.lit.org.sg • DIMA http://mips.gsf.de/genre/proj/dima/index.html • Prolinks http://prolinks.doe-mbi.ucla.edu/cgibin/functionator/pronav/ • Predictome http://predictome.bu.edu/ PLoS Computational Biology March 2007, Volume 3 e42
Cytoscape • Cytoscape is a freely-available (open-source, java-based) bioinformatics software platform for • visualizingbiological networks (e.g. molecular interaction networks) and • analyzing networks with gene expression profiles and other data
Cytoscape Core Features • Customize network data display using visual styles • Powerful graph layout tools • Easily organize multiple networks • Easily navigate large networks • Filter the network • Plugin API (“apps”)
Cytoscape includes a flexible plugin architecture that enables developers to add extra functionality beyond that provided in the core:
Some Cytoscape Plugins • jActiveModules: identify significant “active” subnetworks • Expression Correlation Network: cluster expression data • Agilent Literature Search: build networks by extracting interactions from scientific literature. • MCODE: finds clusters of highly interconnected regions in networks • cPath: query, retrieve and visualize interactions from the MSKCC Cancer Pathway database • BiNGO:determine which Gene Ontology (GO) categories are statistically over-represented in a set of genes • Motif Finder: runs a Gibbs sampling motif detector on sequences for nodes in a Cytoscape network. • Exhaustive list at: • http://chianti.ucsd.edu/cyto_web/plugins/index.php
Gene Ontology enrichment analysis BiNGO plugin
Networks Analysis Guide • Is there commercial networks analysis software available at your institute? • Can it be combined with pathways analysis? • For commercial/free software: what species do they support • your species or finding orthologs for a supported species • What accession type do you need to use? • How complete is your analysis likely to be (how much interaction data exists for your species, how many of your gene products can be analyzed?). • Host-Pathogen Interactions – as a special cases of interaction data.
Pathways and Networks – what next? Exercises or working on your own data sets: • Working on your own data set • continue with enrichment analysis, etc • Can you find tools that do enrichment analysis and pathways/network analysis? • Tutorial 5: Pathways & network analysis • Use tutorial to try different tools – compare, determine which will work for you data set.