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NetBioSIG2012 eriksonnhammer

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  • 1. Comparative Interactomics with FunCoup 2.0 Erik Sonnhammer Stockholm Bioinformatics Centre Science for Life Laboratory Dept. Biochemistry and Biophysics Stockholm University
  • 2. How to map the human interactome?• Genes: ~22000• Interactions: 100000-300000?• Known direct interactions: ~74000 (Intact)• Experiments have high false negative and false positive rates.• → Most interactions need to be inferred combinatorially
  • 3. FunCoup: Predicting Functional Coupling Between Genes/Proteins Using Genomics Data and Orthology• Alexeyenko et al., NAR 40:D821 (2012)• Alexeyenko & Sonnhammer, Genome Research 19:1107 (2009)
  • 4. Co-expression patterns Phylogenetic profiles Domain interactionsSharedtranscription FunCoup Protein-protein interactionsfactorbinding Shared Orthology Genetic Subcellular miRNA interactions co-localisation targeting Other Organisms
  • 5. Naïve Bayesian training -1.0 1.0Continuous variable 0.6 1.0Discrete categoriesExtract links + + +Test against positive and”negative” referencedatasets - - -Calculate enrichmentas likelihood ratio = 1 4 20P(+) / P(-)
  • 6. FunCoup prediction of 1 link Raw data Raw data Raw data Raw data Raw data Bayesian LLR score Bayesian LLR score Bayesian LLR score Bayesian LLR score Bayesian LLR scoreSum of LLR scoresConfidence value pfc
  • 7. Naïve Bayesian training• Training: – Learn log likelihood ratios (LLRs) for each individual evidence bin – When predicting, sum all the LLRs to a full Bayesian score (FBS). |ε | P( Eij | FC ) FBS (ε ) = ∑ log i =1 P ( Eij ) FC Functional coupling ε Set of evidences Eij Evidence i, bin j
  • 8. 4 training datasets → 4 different types of functional coupling• Metabolic pathway (KEGG)• Signalling pathway (KEGG)• Physical protein-protein interaction• Complex member
  • 9. FunCoup training BAYESIAN FRAMEWORK INPUT DATA107 Human105 Rat Mouse TRAINING SETS 103 Fly × MEX Worm MIR SCL Yeast PPI PEX PHP Plant TFB DOM ƒx, ƒy, ƒz, … 25000 20000 15000 10000 Human 5000 Mouse Rat Fly FC-PI Worm FC-CM Yeast FC-ML FC-SL Plant
  • 10. Raw data metrics on CDC2 – KPNB1 Fly MEX (Li and White, 2003) PLC=0.42 Rat MEX (Di Giovanni et al., 2004) PLC=0.48 Mouse SLC (UniProt, ESLDB) WMI=0.04 Mouse MEX (Zapala et al., 2005) PLC=0.70 Mouse MEX (Su et al., 2004) PLC= -0.01 Mouse MEX (Siddiqui et al., 2005) PLC=0.56 Mouse MEX (Hutton et al., 2004) PLC=0.61 Human PPI (IntAct, HPRD, BIND) PPI score=0.17 Human MEX (Su et al., 2004) PLC=0.60 … FC-SL model FC-ML model ΣSL =0+0-0.6+1.2-0.4+0.2+1.2+6.8+1.4=5.5 FC-CM model ΣSL =0+0-0.6+1.2-0.4+0.2+1.2+6.8+1.4=5.8 FC-PI model ΣSL =0+0-0.6+1.2-0.4+0.2+1.2+6.8+1.4=7.9 FBSPI = 0+0-0.6+1.2-0.4+0.2+1.2+6.3+1.4…= 11.2(pfc scores)
  • 11. FBS score and pfc confidence |ε | P ( Eij | FC ) FC Functional couplingFBS (ε ) = ∑ log i =1 P ( Eij ) ε Set of evidences Eij Evidence i, bin j |ε | P( FC )∏ P( Eij | FC )pfc(ε ) = |ε | i =1 |ε | P( FC )∏ P( Eij | FC ) + ∏ P( Eij ) i =1 i =1
  • 12. The total human FunCoup 2.0 networkNr of links5,000,0004,500,0004,000,0003,500,0003,000,0002,500,0002,000,0001,500,0001,000,000 500,000 0 0.1 0.25 0.75 Confidence cutoff
  • 13. Nr of links at pfc cutoffs 10000000 H. sapiens M. musculus 8000000 R. norvegicus C. familiaris D. rerio 6000000 C. intestinalis# links D. melanogaster C. elegans G. gallus 4000000 A. thaliana 2000000 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 pfc cutoff 
  • 14. Comparison to STRING• FunCoup on average 75% larger (based on all links) 5000000 4000000 FunCoup 2.0 STRING 9.0 3000000 2000000 1000000 0 C. elegans C. intestinalis D. rerio H. sapiens R. norvegicus A. thaliana C. familiaris D. melanogaster G. gallus M. musculus S. cerevisiae
  • 15. Support from species and evidence type MEX: mRNA co-expression PHP: phylogenetic profile similarity PPI: protein–protein interaction SCL: sub-cellular co-localization MIR: co-miRNA regulation by shared miRNA targeting DOM: domain interactions PEX: protein co-expression TFB: shared transcription factor binding GIN: genetic interaction profile similarity
  • 16. Validation: Recovering cancer pathways• 36 signalling links in RTK/RAS/PI(3)K, p53, and RB signalling pathways (TCGARN, Science 2008).• FunCoup predicted 29 of 36 links.• 25 more links found.
  • 17. Independent validation: Recovering tumour mutation sets• Lists of genes co-mutated in glioblastoma tumours (The Cancer Genome Atlas).• 6 of 9 lists (>= 10 genes) enriched (p<10-3) with internal FunCoup connections compared to random networks (preserving degree distribution).
  • 18. FunCoup applications Find novel interactionsExtend Find networkpathways FunCoup modules Find novel disease genes Cross-talk between groups
  • 19. http://FunCoup.sbc.su.se ASPMASPM - Abnormal spindle-like microcephaly-associated protein
  • 20. Data details
  • 21. Klammer M, Roopra S, Sonnhammer EL. ”jSquid: a Java applet forgraphical on-line network exploration” Bioinformatics 2008, 24:1467
  • 22. Comparative interactomicsNew in FunCoup 2.0 – ensures true conservation
  • 23. Human presenilin in worm
  • 24. RNA-polymerase II subunits: yeast-all
  • 25. Comparative interactomics Applications• Hypothesis testing – Is a given pathway/complex conserved in another species?• New discoveries – Finding ortholog pairs with conserved functional coupling – very strong evidence for functional conservation – Can also find conservation that is not strictly 4-way:

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