NetBioSIG2013-KEYNOTE Benno Schwikowski
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Transcript of NetBioSIG2013-KEYNOTE Benno Schwikowski
Computa(onal tools for goingfrom molecules to interac(ons
...and back
Benno SchwikowskiSystems Biology LabIns(tut Pasteur, Paris
Phenotype
Adapted from E. Zerhouni’s talkKohn, 1999
20,000 200,000,000
Molecules Interac(ons
Nature News, 18 July 2012 Reactome, 18 July 2013
From molecules to networks Network inference in Cytoscape 3
From networks to moleculesHow networks can help to iden(fy proteins
Cytoscape
Open-‐source plaLorm for biological network data integra(on, analysis, and visualiza(on
– Free & Open-‐source (LPGL)
– Developed and maintained by universi(es, companies, and research ins(tu(ons
– Expandable by Apps/Plugins
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Show the results
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VizMapperLayouts
Cytoscape Apps
Visualiza3on
Computa3onalAnalysis
Humananalysis
FilteringSelec(on
Dataimport
Dataexport
Cytoscape Workflow
Annotated Network
Core Concepts -‐ Integra(on
• Networks & Data Tables (A[ributes)
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VizMapper
Core Concepts -‐ Visual mapping
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Use specific line types to indicate different types of
interac(ons
Browse extremely dense networks by controlling for the
opacity of nodes Expression data mapping
Set node sizes based on the degree of connec(vity of the nodes
Encode specific physical en((es as different node shapes
Data Table
Core Concepts -‐ Analysis
Apps/Plugins: Expanding Cytoscape Func(onality
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Berlin, July 18, 2013
Import Networks
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• Network Data Formats– SIF
– GML
– XGMML
– GraphML
– BioPAX
– PSI-‐MI
– SBML
– KGML(KEGG)
– Excel
– Delimited Text Table
– CSV
– Tab
• Network Databases– Protein - Protein
– STRING - IntAct– Genetic
– BioGRID– Protein - Compound
– ChEMBL– Human-Curated
Pathways– KEGG, Reactome,
PathwayCommons
Berlin, July 18, 2013
Import Data Table (A[ributes)
• Data Table: Any data that describes or provides details about nodes, edges, and networks
• Anything saved as a table can be loaded into Cytoscape– Excel
– Tab Delimited Document
– CSV
• As long as proper mapping key is available, Cytoscape can map them to your networks
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BRCA1
GO Terms:DNA RepairCell CycleDNA Binding
NCBI Gene ID 672
On Chromosome 16 Ensemble IDENSG00000012048
Public Data Sources
Berlin, July 18, 2013
What’s new in 3.0
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• 2.x done without explicit design guidelines or standards
• No well-‐defined API
• Hard to maintain and improve (plugins breaking)
• Plugins could not share func^onality
Berlin, July 18, 2013
Cytoscape 3 – Reasons for the rewrite
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Berlin, July 18, 2013
Cytoscape 3.0 – A complete rewrite
• New modular architecture based on OSGi
• Compa^bility with 3.0 guarantees compa^bility with 3.x
• Clear and simplified API (implementa^on separate)
• RootNetwork/SubNetwork design
• Acributes are replaced by Tables (‘first-‐class ci^zens’)– CyRow and CyColumn interfaces
• Apps can talk to each other now, much less likely to break
• All plugins need to be converted to Apps
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• 140+ plugins for version 2.x series• 16 apps for 3.x series
Berlin, July 18, 2013
Status of apps/plugins
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3.0 AppsjActiveModulesMCODEAgilentLiterature SearchVennDiagramGeneratorClusterONECentiscapeGeneMANIA
Integrated in 3.0 Core
EnhancedSearchBiomartClientNetworkAnalyzer
Plugins being ported
ClusterMakerGenoscapeMiMiplugin...
Berlin, July 18, 2013
What’s new in 3.0
• hcp://apps.cytoscape.org
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Cytoscape 3.x
Cyni ToolboxGUI
Cyni API- Cyni Interfaces- Cyni Data Structure- Utility Methods
Data Imputation
NetworkInference
DataDiscretization
Metrics
Cyni Apps
User 2: Method Developer
New NetworkInference Method
User 1: Biologists
Load Data
Berlin, July 18, 2013
Cyni network inference
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Estimate Data Discretize Data Infer Network
Berlin, July 18, 2013
Cyni Network inference toolbox
• Cyni provides– A few built-‐in algorithms
– Data imputa^on and discre^za^on techniques
– Several known metrics (correla^on, bayesian,...)
– Documented API
– Tutorials and sample code
• First 3.0 app that exports func^onality
• Addi^onal implementa^ons underway (ARACNe)
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From molecules to networks Network inference in Cytoscape 3
From networks to moleculesHow networks can help to iden(fy proteins
Motivation
• Study of 24 smooth muscle cells over many years
• Proteomic analysis of many samples revealed systematic differences between two groups
• Close analysis revealed that the causative factor is the use of bovine DNAse I in the protein extraction protocol
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Affected SMC protein extracts
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43
34
26
55
95 130
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11
Unaffected SMC protein extracts ����������
43
34
26
55
95 130
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11
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DIGEWithoutDNAse I treatment
DIGEWith
DNAse I treatment
Acosta-Martin, Gwinner, Pinet, Schwikowski, unpublished
First bioinformatic analysis
• 11 unaffected and 13 affected SMC protein extracts (as identified by absence of 3 large spots)
• 569 out of 853 spots differentially expressed, 408 with FC>2, 135 significant (62 down, 73 up)
• Identification of 41 proteins from 102 spots• GO analysis: >50% in apoptosis, cell motion,
actin cytoskeleton reorganization
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The Steiner tree approach
• “Explanation”= connected network
• Parsimony principle: Use the minimum number of additional proteins
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Steiner PPI analysis
• Started with 41 original proteins + DNAse I – ACAP1 (unconnected)
• Use BIND and IntAct databases:–51,975 interactions among 21,022 proteins
• Weight edges with inverse functional similarity score (between 0 and 10)
• Use Steiner heuristic implemented in the GOBLIN tool (Univ. Augsburg)
26Schlicker (2007), Nucleic Acids Research
Mehlhorn (1988) Information Processing Letters
Sanity check: Is the resulting networkbetter than chance?
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Network length Number of Steiner nodes
Resulting Steiner network
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Gwinner et al. (2013), Proteomics
Resulting list of Steiner nodes
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• Focus on Steiner nodes with meaningful connections to input proteins:Sort by score sum over all interactions to input proteins
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Experimental validation
Gwinner et al., Proteomics (2013)
From molecules to networks Network inference in Cytoscape 3
From networks to moleculesHow networks can help to iden(fy proteins
Galagan et al.,Nature 499 (11 July 2013)
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Large-scalemeasurement
Biology
Computation
Manipulate
Measure Mine
Model
Ideker/Lauffenburger 2006
Berlin, July 18, 2013
Questions beyond ‘the best network’
• Which parts of a given network are consistent with the data?
• Which parts of the network are we sure of, given the data?
• Which interactions could be added (removed) to make the data compatible with the model?
• Which experiment could be done to better distinguish different possible models?
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Postdocs
Ph.D.students
SeniorSo@wareEngineer
Xiaoyi Chen
Oriol GuitartFreddy Cliquet
Frederik Gwinner
Robin Friedman
Masterstudents
Iryna Nikolayeva
Systems Biology Lab
Leif Blaese
Steiner approach
Adelina Acosta-‐Mar(n,Florence Pinet (Inst. Pasteur Lille)
Cytoscape/CyniPart of
Gary Bader & Co. (U. Toronto)Alexander Pico & Co (Gladstone SFO)Trey Ideker & Co. (UC San Diego)Chris Sander & Co. (MSKCC NYC)Piet MolenaarAgilentLeroy Hood & Co. (ISB Sea[le)
Collaborators
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Berlin, July 18, 2013
Cytoscape Retreat 2013
Pasteur Institute, ParisOct 9: Symposium on Network Biology
Oct 10: Cytoscape User and Developer Tutorials
http://nrnb.org/cyretreat/
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