NetBioSIG2013-KEYNOTE Benno Schwikowski

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NetBioSIG2013-KEYNOTE Benno Schwikowski

  1. 1. Computa(onal  tools  for  going from  molecules  to  interac(ons ...and  back Benno  Schwikowski Systems  Biology  Lab Ins(tut  Pasteur,  Paris
  2. 2. Phenotype Adapted  from  E.  Zerhouni’s  talkKohn, 1999
  3. 3. 20,000 200,000,000 Molecules Interac(ons
  4. 4. Nature  News,  18  July  2012 Reactome,  18  July  2013
  5. 5. From  molecules  to  networks   Network  inference  in  Cytoscape  3 From  networks  to  molecules How  networks  can  help  to  iden(fy  proteins
  6. 6. 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 6
  7. 7. Show  the   results 7 VizMapper Layouts Cytoscape   Apps Visualiza3on Computa3onal Analysis Human analysis Filtering Selec(on Data import Data export Cytoscape  Workflow
  8. 8. Annotated  Network Core  Concepts  -­‐  Integra(on • Networks  &  Data  Tables  (A[ributes) 8
  9. 9. VizMapper Core  Concepts  -­‐  Visual  mapping 9 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
  10. 10. Core  Concepts  -­‐  Analysis Apps/Plugins:  Expanding  Cytoscape  Func(onality 10
  11. 11. Berlin,  July  18,  2013 Import  Networks 11 • 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
  12. 12. 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 12 BRCA1 GO Terms: DNA Repair Cell Cycle DNA Binding NCBI Gene ID 672 On Chromosome 16 Ensemble ID ENSG00000012048 Public  Data  Sources
  13. 13. Berlin,  July  18,  2013 What’s  new  in  3.0 13
  14. 14. • 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 14
  15. 15. 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 15
  16. 16. • 140+ plugins for version 2.x series • 16 apps for 3.x series Berlin,  July  18,  2013 Status of apps/plugins 16 3.0 Apps jActiveModules MCODE AgilentLiterature Search VennDiagramGenerator ClusterONE Centiscape GeneMANIA Integrated in 3.0 Core EnhancedSearch BiomartClient NetworkAnalyzer Plugins  being  ported ClusterMaker Genoscape MiMiplugin ...
  17. 17. Berlin,  July  18,  2013 What’s  new  in  3.0 • hcp://apps.cytoscape.org 17
  18. 18. Cytoscape 3.x Cyni Toolbox GUI Cyni API - Cyni Interfaces - Cyni Data Structure - Utility Methods Data Imputation Network Inference Data Discretization Metrics Cyni Apps User 2: Method Developer New Network Inference Method User 1: Biologists
  19. 19. Load Data Berlin,  July  18,  2013 Cyni network inference 19 Estimate Data Discretize Data Infer Network
  20. 20. 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) 20
  21. 21. From  molecules  to  networks   Network  inference  in  Cytoscape  3 From  networks  to  molecules How  networks  can  help  to  iden(fy  proteins
  22. 22. 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 22
  23. 23. Affected SMC protein extracts 3 104 5 6 7 8 9 43 34 26 55 95 130 17 11 Unaffected SMC protein extracts 43 34 26 55 95 130 17 11 3 104 5 6 7 8 9 DIGE Without DNAse I treatment DIGE With DNAse I treatment Acosta-Martin, Gwinner, Pinet, Schwikowski, unpublished
  24. 24. 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 24
  25. 25. The Steiner tree approach • “Explanation”= connected network • Parsimony principle: Use the minimum number of additional proteins 25
  26. 26. 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) 26 Schlicker (2007), Nucleic Acids Research Mehlhorn (1988) Information Processing Letters
  27. 27. Sanity check: Is the resulting network better than chance? 27 Network length Number of Steiner nodes
  28. 28. Resulting Steiner network 28 Gwinner et al. (2013), Proteomics
  29. 29. Resulting list of Steiner nodes 29 • Focus on Steiner nodes with meaningful connections to input proteins: Sort by score sum over all interactions to input proteins
  30. 30. 55 kDa 43 kDa ArbitraryUnits/1000 ArbitraryUnits/10 Experimental validation Gwinner et al., Proteomics (2013)
  31. 31. From  molecules  to  networks   Network  inference  in  Cytoscape  3 From  networks  to  molecules How  networks  can  help  to  iden(fy  proteins
  32. 32. Galagan  et  al., Nature  499  (11  July  2013)
  33. 33. 33 Large-scale measurement Biology Computation Manipulate Measure Mine Model Ideker/Lauffenburger  2006
  34. 34. 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? 34
  35. 35. Postdocs Ph.D. students Senior So@ware Engineer Xiaoyi  Chen   Oriol  GuitartFreddy  Cliquet Frederik  Gwinner   Robin  Friedman   Master students Iryna  Nikolayeva Systems  Biology  Lab Leif  Blaese  
  36. 36. Steiner  approach Adelina  Acosta-­‐Mar(n, Florence  Pinet  (Inst.  Pasteur  Lille) Cytoscape/Cyni Part  of     Gary  Bader  &  Co.  (U.  Toronto) Alexander  Pico  &  Co  (Gladstone  SFO) Trey  Ideker  &  Co.  (UC  San  Diego) Chris  Sander  &  Co.  (MSKCC  NYC) Piet  Molenaar Agilent Leroy  Hood  &  Co.  (ISB  Sea[le) Collaborators 36
  37. 37. Berlin,  July  18,  2013 Cytoscape Retreat 2013 Pasteur Institute, Paris Oct 9: Symposium on Network Biology Oct 10: Cytoscape User and Developer Tutorials http://nrnb.org/cyretreat/ 37

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