Tutorial session 3 Network analysis Exploring PPI networks using Cytoscape EMBO Practical Course Session 8 Nadezhda Doncheva and Piet Molenaar
Jan 31, 2016
Tutorial session 3Network 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
Find network clusters
Find network motifs
Concepts Enrichment Clustering Guild by association
Data Stored sessions; Drosophila and Neuroblastoma
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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
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jActiveModules (Demo)
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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
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Use case; Assignment 3.1 Systematic perturbations
Different cell-lines
Including controls: DMSO
97 arrays: data collected from R2: hugo-once etc
PI3K signatureRAS/ERK signature
RAS/ERK-dependentCell lines-SHEP2
-RD
PI3K-dependentCell lines
-SY5Y-D425
Harvest: RNA Affy (97samples) protein WB
PI3K
AKT
mTORC1 mTORC2
RAS
MEK
RAF
ERK
PI103
PP242
PIK90
Rapamycin
U0126
AKTi 1/2
MK2206
Use case; Assignment 3.11. Open the Neuroblastoma session and load
the pvalues from this experiment
2. Run jActiveModules on the annotated network
1. What regions are important?
2. Can you imagine any caveats for this method?
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Assignment 3.1: results1. Important regions
1. Several clusters; those with most mutations might deliver additional wet lab testable pathway players (drugtargets?)
2. Caveats:
1. Maintenance (housekeeping) processes
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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
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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
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BiNGO (Demo)
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Use case; Assignment 3.21. Open the Neuroblastoma session and run
BiNGO on the filtered network.
1. What categories are enriched?
2. Can you find these back in the article?
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Assignment 3.2: results1. Quite some categories!
1. Filter out less informative top level categories: in several deeper categories neuron projection pops up
2. A clustering method can specify
3. Use subsets only
4. Worth mentioning: other tools eg. David
2. In second cluster neuron projection clearer; and large set of mutated genes
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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
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NetworkAnalyzer (Demo)
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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
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CytoHubba (Demo)
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Use case; Assignment 3.3 Open the Drosophila network session
1.Check the network parameters
1. Is it scale free?
2. Can you find important players?
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Assignment 3.3: results1. It is scalefree; the
node degree distribution fits a power law
2. Depends on the type of player you want to find; between processes or master regulator over number of genes?
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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
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MCODE & ClusterMaker (Demo)
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Use case; Assignment 3.4 Open the Drosophila session
1.Run the MCODE algorithm
2.Run the MCL clustering algorithm
1. Compare the results
2. Can you corroborate some of the clusters found in the article?
3. Are there additional filtering options?
4. Play with the settings and observe their influence
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Assignment 3.4: results1. MCODE gives fuzzier clusters
2. E.g. the syx-syb cluster
3. The cluster parameters are set as attributes; these can be used to filter
4. Less stringent settings will produce additional clusters, but also larger clusters
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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
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NetMatch (Demo)
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Use case; Assignment 3.51. In the Drosophila session try to find a
feedforward motif
2. Finally toy around with the settings of the Vizmapper to produce a nice paper-ready figure!
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Assignment 3.5: results1. Simple feed forward gives lots of matches
1. Add attributes, or make more complex queries
2. Toying around can produce nice results!
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Other Useful Plugins PSICQUICUniversalCli
ent AgilentLiteratureSear
ch GeneMANIA CyThesaurus
structureViz ClusterMaker EnrichmentMap PiNGO ClueGO RandomNetworks
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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
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New hypotheses
End!
And a final note…..
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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