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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Unaffected SMC protein extracts ����������

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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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