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Tutorial session 3 Network analysis

Tutorial session 3 Network analysis. Exploring PPI networks using Cytoscape EMBO Practical Course Session 8 Nadezhda Doncheva and Piet Molenaar. Overview. Focus: Network analysis Identify active subnetworks Analyze Gene Ontology enrichment Perform topological analysis

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Tutorial session 3 Network analysis

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  1. Tutorial session 3Network analysis Exploring PPI networks using Cytoscape EMBO Practical Course Session 8 Nadezhda Doncheva and Piet Molenaar

  2. Overview • Focus: Network analysis • Identify active subnetworks • Analyze Gene Ontology enrichment • Perform topological analysis • Find network clusters • Find network motifs • Concepts • Enrichment • Clustering • Guild by association • Data • Stored sessions; Drosophila and Neuroblastoma

  3. Identify active subnetworks • jActiveModules plugin • Active modules are sub-networks that show differential expression over user-specified conditions or time-points • Microarray gene-expression attributes • Mass-spectrometry protein abundance • Input: interaction network and p-values for gene expression values over several conditions • Output: significant sub-networks that show differential expression over one or several conditions

  4. jActiveModules (Demo)

  5. Use case; Assignment 3.1 • Using neuroblastoma cell lines inhibitors to elucidate important pathways • 2 neuroblastoma cell lines: SHEP21, SY5Y • 7 inhibitors • Profiled on Affymetrix array • http://r2.amc.nl • Other resource e.g. GEO

  6. Use case; Assignment 3.1 • Systematic perturbations • Different cell-lines • Including controls: DMSO • 97 arrays: data collected from R2: hugo-once etc PIK90 PI3K-dependent Cell lines -SY5Y -D425 RAS/ERK-dependent Cell lines -SHEP2 -RD RAS PI3K AKTi 1/2 MK2206 RAF AKT PI103 MEK U0126 Harvest: RNA  Affy (97samples) protein  WB mTORC1 mTORC2 ERK PI3K signature RAS/ERK signature Rapamycin PP242

  7. Use case; Assignment 3.1 • Open the Neuroblastoma session and load the pvalues from this experiment • Run jActiveModules on the annotated network • What regions are important? • Can you imagine any caveats for this method?

  8. Gene Ontology • Provides three structured controlled vocabularies (ontologies) of defined terms representing gene product properties: • Biological Process (23074 terms): biological goal or objective • Molecular Function (9392 terms): elemental activity/task • Cellular Component (2994 terms): location or complex

  9. Analyze Gene Ontology enrichment • BiNGO plugin: http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html • Calculates over-representation of a subset of genes with respect to a background set in a specific GO category • Input: subnetwork or list, background set by user • Output: tree with nodes color reflecting overrepresentation; also as lists • Caveats: Gene identifiers must match; low GO term coverage, background determining

  10. BiNGO (Demo)

  11. Use case; Assignment 3.2 • Open the Neuroblastoma session and run BiNGO on the filtered network. • What categories are enriched? • Can you find these back in the article?

  12. Compute topological parameters • NetworkAnalyzer plugin: http://med.bioinf.mpi-inf.mpg.de/netanalyzer/ • Computes a comprehensive set of simple and complex topological parameters • Displays the results in charts, which can be saved as images or text files • Can be combined with the ShortestPath plugin http://www.cgl.ucsf.edu/Research/cytoscape/shortestPath/index.html

  13. NetworkAnalyzer (Demo)

  14. Identify hubs • CytoHubba plugin: http://hub.iis.sinica.edu.tw/cytoHubba/ • Computes several topological node parameters • Identifies essential nodes based on their score and displays them in a ranked list • Generates a subnetwork composed of the best-scored nodes

  15. CytoHubba (Demo)

  16. Use case; Assignment 3.3 • Open the Drosophila network session • Check the network parameters • Is it scale free? • Can you find important players?

  17. Find network clusters • Network clusters are highly interconnected sub-networks that may be also partly overlapping • Clusters in a protein-protein interaction network have been shown to represent protein complexes and parts of biological pathways • Clusters in a protein similarity network represent protein families • Network clustering is available through the MCODE • Cytoscape plugin: http://baderlab.org/Software/MCODE

  18. MCODE & ClusterMaker (Demo)

  19. Use case; Assignment 3.4 • Open the Drosophila session • Run the MCODE algorithm • Run the MCL clustering algorithm • Compare the results • Can you corroborate some of the clusters found in the article? • Are there additional filtering options? • Play with the settings and observe their influence

  20. Find network motifs • NetMatch plugin: http://alpha.dmi.unict.it/~ctnyu/netmatch.html • Network motif is a sub-network that occurs significantly more often than by chance alone • Input: query and target networks, optional node/edge labels • Output: topological query matches as subgraphs of target network • Supports: subgraph matching, node/edge labels, label wildcards, approximate paths

  21. NetMatch (Demo)

  22. Use case; Assignment 3.5 • In the Drosophila session try to find a feedforward motif • Finally toy around with the settings of the Vizmapper to produce a nice paper-ready figure!

  23. Other Useful Plugins • PSICQUICUniversalClient • AgilentLiteratureSearch • GeneMANIA • CyThesaurus • structureViz • ClusterMaker • EnrichmentMap • PiNGO • ClueGO • RandomNetworks

  24. Wrapping up… • Biological questions • I have a protein • Function, characteristics from known interactions • I have a list of proteins • Shared features, connections • I have data • Derive causal networks • Network • Topology • Hubs • Clusters New hypotheses

  25. End! And a final note…..

  26. Announcing Cytoscape 3.0 Beta • Easier data import • Improved user experience • Graphical annotations • One-click install from AppStore • Improved API for app developers http://cytoscape.org

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