1. Network integration and function prediction: Putting it all together Curtis Huttenhower 04-13-11 Harvard School of Public Health Department of Biostatistics

2. Outline Functional network integration –B–Bayes nets and LR –T–The human genome, tissues, and disease Network meta-analysis –P–Pathogens and MTb –Q–Quantifying progress in yeast Networks to pathways –F–Functional mapping: networks of networks –H–Hierarchical integration –P–Pathway prediction Regulatory network integration –N–Network motifs 2

3. A computational definition of functional genomics 3 Genomic data Prior knowledge Data ↓ Function ↓ Function Gene ↓ Gene ↓ Function

4. A framework for functional genomics 4 High Similarity Low Similarity High Correlation Low Correlation G1 G2 + G4 G9 + … G3 G6 - G7 G8 - … G2 G5 ? 0.90.7…0.10.2…0.8 +-…--…+ 0.5…0.050.1…0.6 High Correlation Low Correlation Frequency Coloc.Not coloc. Frequency SimilarDissim. Frequency P(G2-G5|Data) = 0.85 100Ms gene pairs → ← 1Ks datasets + =

5. MEFIT: A Framework for Functional Genomics 5 Golub 1999 Butte 2000 Whitfield 2002 Hansen 1998 Functional Relationship Biological Context Functional area Tissue Disease …

6. Functional network prediction and analysis 6 Global interaction network Metabolism networkSignaling networkGut community network Currently includes data from 30,000 human experimental results, 15,000 expression conditions + 15,000 diverse others, analyzed for 200 biological functions and 150 diseases HEFalMp

7. HEFalMp: Predicting human gene function 7 HEFalMp

8. HEFalMp: Predicting human genetic interactions 8 HEFalMp

9. HEFalMp: Analyzing human genomic data 9 HEFalMp

10. HEFalMp: Understanding human disease 10 HEFalMp

11. Validating Human Predictions 11 Autophagy Luciferase (Negative control) ATG5 (Positive control) LAMP2RAB11A Not Starved (Autophagic) Predicted novel autophagy proteins 5½ of 7 predictions currently confirmed With Erin Haley, Hilary Coller

12. Outline Functional network integration –Bayes nets and LR –The human genome, tissues, and disease Network meta-analysis –Pathogens and MTb –Quantifying progress in yeast Networks to pathways –Functional mapping: networks of networks –Hierarchical integration –Pathway prediction Regulatory network integration –Network motifs 12

13. Meta-analysis for unsupervised functional data integration 13 Evangelou 2007 Huttenhower 2006 Hibbs 2007 Simple regression: All datasets are equally accurate Random effects: Variation within and among datasets and interactions

14. Meta-analysis for unsupervised functional data integration 14 Evangelou 2007 Huttenhower 2006 Hibbs 2007 + =

15. Unsupervised data integration: TB virulence and ESX-1 secretion 15 With Sarah Fortune Graphle http://huttenhower.sph.harvard.edu/graphle/

16. Unsupervised data integration: TB virulence and ESX-1 secretion 16 With Sarah Fortune Graphle http://huttenhower.sph.harvard.edu/graphle/ X ?

17. Predicting gene function 17 Cell cycle genes Predicted relationships between genes High Confidence Low Confidence

18. Predicting gene function 18 Predicted relationships between genes High Confidence Low Confidence Cell cycle genes

19. Predicting gene function 19 Predicted relationships between genes High Confidence Low Confidence These edges provide a measure of how likely a gene is to specifically participate in the process of interest.

20. Comprehensive validation of computational predictions 20 Genomic data Computational Predictions of Gene Function MEFIT SPELL Hibbs et al 2007 bioPIXIE Myers et al 2005 Genes predicted to function in mitochondrion organization and biogenesis Laboratory Experiments Petite frequency Growth curves Confocal microscopy New known functions for correctly predicted genes Retraining With David Hess, Amy Caudy Prior knowledge

21. Evaluating the performance of computational predictions 21 106 Original GO Annotations Genes involved in mitochondrion organization and biogenesis 135 Under-annotations 82 Novel Confirmations, First Iteration 17 Novel Confirmations, Second Iteration 340 total: >3x previously known genes in ~5 person-months

22. Evaluating the performance of computational predictions 22 106 Original GO Annotations Genes involved in mitochondrion organization and biogenesis 95 Under-annotations 40 Confirmed Under-annotations 80 Novel Confirmations First Iteration 17 Novel Confirmations Second Iteration 340 total: >3x previously known genes in ~5 person-months Computational predictions from large collections of genomic data can be accurate despite incomplete or misleading gold standards, and they continue to improve as additional data are incorporated.

23. Outline Functional network integration –Bayes nets and LR –The human genome, tissues, and disease Network meta-analysis –Pathogens and MTb –Quantifying progress in yeast Networks to pathways –Functional mapping: networks of networks –Hierarchical integration –Pathway prediction Regulatory network integration –Network motifs 23

24. Functional mapping: mining integrated networks 24 Predicted relationships between genes High Confidence Low Confidence The strength of these relationships indicates how cohesive a process is. Chemotaxis

25. Functional mapping: mining integrated networks 25 Predicted relationships between genes High Confidence Low Confidence Chemotaxis

26. Functional mapping: mining integrated networks 26 Flagellar assembly The strength of these relationships indicates how associated two processes are. Predicted relationships between genes High Confidence Low Confidence Chemotaxis

27. Functional mapping: Associations among processes 27 Edges Associations between processes Very Strong Moderately Strong Hydrogen Transport Electron Transport Cellular Respiration Protein Processing Peptide Metabolism Cell Redox Homeostasis Aldehyde Metabolism Energy Reserve Metabolism Vacuolar Protein Catabolism Negative Regulation of Protein Metabolism Organelle Fusion Protein Depolymerization Organelle Inheritance

28. Functional mapping: Associations among processes 28 Edges Associations between processes Very Strong Moderately Strong Borders Data coverage of processes Well Covered Sparsely Covered Hydrogen Transport Electron Transport Cellular Respiration Protein Processing Peptide Metabolism Cell Redox Homeostasis Aldehyde Metabolism Energy Reserve Metabolism Vacuolar Protein Catabolism Negative Regulation of Protein Metabolism Organelle Fusion Protein Depolymerization Organelle Inheritance

29. Functional mapping: Associations among processes 29 Edges Associations between processes Very Strong Moderately Strong Nodes Cohesiveness of processes Below Baseline (genomic background) Very Cohesive Borders Data coverage of processes Well Covered Sparsely Covered Hydrogen Transport Electron Transport Cellular Respiration Protein Processing Peptide Metabolism Cell Redox Homeostasis Aldehyde Metabolism Energy Reserve Metabolism Vacuolar Protein Catabolism Negative Regulation of Protein Metabolism Organelle Fusion Protein Depolymerization Organelle Inheritance

30. Functional mapping: Associations among processes 30 Edges Associations between processes Very Strong Moderately Strong Nodes Cohesiveness of processes Below Baseline (genomic background) Very Cohesive Borders Data coverage of processes Well Covered Sparsely Covered

31. Gene expression Physical PPIs Genetic interactions Colocalization Sequence Protein domains Regulatory binding sites … ? How do functional interactions become pathways? 31 + =

32. Functional genomic data 32 With Chris Park, Olga Troyanskaya Simultaneous inference of physical, genetic, regulatory, and functional networks Functional interactions Regulatory interactions Post-transcriptional regulation Metabolic interactions Phosphorylation Protein complexes

33. Learning a compendium of interaction networks 33 Train one SVM per interaction type Resolve consistency using hierarchical Bayes net

34. Learning a compendium of interaction networks 34 AUC 0.51.0 Both presence/absence and directionality of interactions are accurately inferred

35. Using network compendia to predict complete pathways 35 Additional 20 novel synthetic lethality predictions tested, 14 confirmed (>100x better than random) Confirmed Unconfirmed With David Hess

36. Interactive aligned network viewer – http://function.princeton.edu/bioweaver http://function.princeton.edu/bioweaver 36 Graphle

37. Outline Functional network integration –Bayes nets and LR –The human genome, tissues, and disease Network meta-analysis –Pathogens and MTb –Quantifying progress in yeast Networks to pathways –Functional mapping: networks of networks –Hierarchical integration –Pathway prediction Regulatory network integration –Network motifs 37

38. Of only five regulators found, four have generic cell cycle/proliferation targets Just five basic regulators for ~7,000 genes? These motifs only appear upstream of ~half of the genes Human Regulatory Networks 38 G0 I III IV V VI VII IX VIII II X 6,829 genes Serum re-stimulated (hrs)Serum starved (hrs) 1 5<<50 248249612482448 Development Cholesterol Protein localization Cell cycle RNA processing Metabolism FIRE: Elemento et al. 2007 Elk-1 Sp1 NF-Y YY1 Quiescence: reversible exit from the cell cycle

39. COALESCE: Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction 39 Gene ExpressionDNA Sequence 5’ UTR 3’ UTR Upstream flankDownstream flank Evolutionary Conservation Nucleosome Positions Identify conditions where genes coexpress Identify motifs enriched in genes’ sequences Create a new module Select genes based on conditions and motifs Subtract mean from all data Regulatory modules Coregulated genes Conditions where they’re coregulated Putative regulating motifs Feature selection: Tests for differential expression/frequency Bayesian integration

40. COALESCE: Selecting Coexpressed Conditions For each gene expression condition… –Compare distributions of values for Genes in the module versus Genes not in the module –If significantly different, include the condition 40 Preserving data structure: If multiple conditions derive from the same dataset, can be included/excluded as a unit For example, time course vs. deletion collection Test using multivariate z-test Precalculate covariance matrix; still very efficient

41. COALESCE: Selecting Significant Motifs Coalesce looks for three kinds of motifs: –K-mers –Reverse complement pairs –Probabilistic Suffix Trees (PSTs) For every possible motif… –Compare distributions of values for Genes in the module versus Genes not in the module –If significantly different, include the motif 41 ACGACGT ACGACAT | ATGTCGT A TC G T TG CA This can distinguish flanks from UTRs Fast! Efficient enough to search coding sequence (e.g. exons/introns)

42. COALESCE: Selecting Probable Genes For each gene in the genome… 42 For each significant condition…For each significant motif… What’s the probability the gene came from the module’s distribution? What’s the probability that it came from outside the module? Distributions of each feature in and out of the developing module are observed from the data. Prior is used to stabilize module convergence; genes already in the module are more likely to stay there next iteration. The probability of a gene being in the module given some data…

43. COALESCE: Integrating Additional Data Types 43 Nucleosome placement Evolutionary conservation Can be included as additional datasets and feature selected just like expression conditions/motifs. Or can be used as a prior or weight on the values of individual motifs. NC G12.50.0 G20.60.5 G31.20.9 ……… TCCGGTAGAACTACTGGTATTGTTTTGGATTCCGGTGATG

44. COALESCE Results: S. cerevisiae Modules 44 ~2,200 conditions ~6,000 genes The haystack A needle 100 genes 80 conditions

45. COALESCE Results: S. cerevisiae Modules 45 54 genes, 144 conditions Conjugation 33 genes, 434 conditions Budding 112 genes, 82 conditions Mitosis and DNA replication Swi5 Stb1/Swi6 Ste12 1612 266 284

46. COALESCE Results: S. cerevisiae Modules 46 174 175 176 50 genes, 775 conditions Iron transport 11 genes, 844 conditions Phosphate transport 126 genes, 660 conditions Glycolysis, iron and phosphate transport, amino acid metabolism… Pho4 Helix-Loop-Helix Tye7/Cbf1/Pho4 Aft1/2

47. COALESCE Results: S. cerevisiae Modules 47 72 genes, 319 conditions Mitochondrial translation Puf3 822 …plus more ribosome clusters than you can shake a stick at!

48. COALESCE Results: Yeast TF/Target Accuracy 48

49. COALESCE Results: TF/Targets Influenced by Supporting Data 49 Improved by any addl. data, mainly conservation Decreased by addl. dataImproved by conservation Improved only by both

50. COALESCE Results: Yeast Clustering Accuracy ~2,200 yeast conditions –Recapitulation of known biology from Gene Ontology 50

51. COALESCE Results: Yeast Clustering Accuracy ~2,200 yeast conditions –Recapitulation of known biology from Gene Ontology 51 ASCL1 in 5’ flank, unch. sequences underenriched in 3’ UTR M. musculus: Up in callosal and motor neurons C. elegans: Up in larvae, down in adults GATA in 5’ flank, miR-788 seed in 3’ UTR AAGGGGC (zf?) and enriched in 5’ flank H. sapiens: Up in normal muscle, down in diabetic

52. COALESCE: Coregulated Quiescence Modules Predicts regulatory modules from genomic data: –Coregulated genes –Conditions under which coregulation occurs –Putative regulatory motifs 5 quiescence-related microarray datasets, 60 conditions –Quiescence program(Coller et al. 2006) –Adenoviral infection(Miller et al. 2007) –let-7 response(Legesse-Miller et al. unpub.) –Contact inhibition(Scarino et al. unpub.) –Serum withdrawal(Legesse-Miller et al. unpub.) 52

53. COALESCE: Coregulated Quiescence Modules 53 Down during quiescence entry, up during quiescence exit, down with adenoviral infection Specific predicted uncharacterized reverse complement motif Up during quiescence entry, down during quiescence exit Many known related (proliferation) motifs: Pax4, Staf, NFKB1, Gfi, ESR1, Runx1, Su(H) Down during quiescence entry, enriched for transport/trafficking miR-297 motif predicted in 3’ UTR (CACATAC) Down with let-7 exposure let-7 motifs predicted in 3’ UTR (UACCUC)

54. Network Motifs 54 Coherent feed-forward filter Incoherent feed-forward pulse Bi-fan Positive auto-regulation delay WGD and evolvability Negative auto-regulation speed + stability Feedback memory

55. March 1, 201055 From Milo, et al., Science, 2002

56. Outline Functional network integration –Bayes nets and LR –The human genome, tissues, and disease Network meta-analysis –Pathogens and MTb –Quantifying progress in yeast Networks to pathways –Functional mapping: networks of networks –Hierarchical integration –Pathway prediction Regulatory network integration –Network motifs 56

57. 1:1 Lewis Carroll Map “… And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to the mile!" "Have you used it much?" I enquired. "It has never been spread out, yet," said Mein Herr: "the farmers objected: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well. Sylvie and Bruno Concluded by Lewis Carroll, 1893. March 1, 201057