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Network integration and function prediction: Putting it all together
Curtis Huttenhower Harvard School of Public Health Department of Biostatistics
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Outline Functional network integration Network meta-analysis
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
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A computational definition of functional genomics
Prior knowledge Genomic data Gene ↓ Function Gene ↓ Data ↓ Function Function ↓
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A framework for functional genomics
100Ms gene pairs → G1 G2 + G4 G9 … G3 G6 - G7 G8 G5 ? 0.9 0.7 0.1 0.2 0.8 0.5 0.05 0.6 ← 1Ks datasets P(G2-G5|Data) = 0.85 High Correlation Low Frequency High Correlation Low Coloc. Not coloc. Frequency + = Similar Dissim. Frequency High Similarity Low
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MEFIT: A Framework for Functional Genomics
Functional area Tissue Disease … Functional Relationship Biological Context Golub 1999 Butte 2000 Whitfield 2002 Hansen 1998
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Functional network prediction and analysis
Global interaction network HEFalMp 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 Metabolism network Signaling network Gut community network
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HEFalMp: Predicting human gene function
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HEFalMp: Predicting human genetic interactions
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HEFalMp: Analyzing human genomic data
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HEFalMp: Understanding human disease
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Validating Human Predictions
With Erin Haley, Hilary Coller Autophagy 5½ of 7 predictions currently confirmed Predicted novel autophagy proteins Luciferase (Negative control) ATG5 (Positive control) LAMP2 RAB11A Not Starved Starved (Autophagic)
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Outline Functional network integration Network meta-analysis
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
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Meta-analysis for unsupervised functional data integration
Huttenhower 2006 Hibbs 2007 Evangelou 2007 Simple regression: All datasets are equally accurate Random effects: Variation within and among datasets and interactions
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Meta-analysis for unsupervised functional data integration
Huttenhower 2006 Hibbs 2007 Evangelou 2007 + =
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Unsupervised data integration: TB virulence and ESX-1 secretion
With Sarah Fortune Graphle
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Unsupervised data integration: TB virulence and ESX-1 secretion
With Sarah Fortune X ? Graphle
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Predicting gene function
Predicted relationships between genes High Confidence Low Cell cycle genes
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Predicting gene function
Predicted relationships between genes High Confidence Low Cell cycle genes
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Predicting gene function
Predicted relationships between genes High Confidence Low These edges provide a measure of how likely a gene is to specifically participate in the process of interest. Cell cycle genes
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Comprehensive validation of computational predictions
With David Hess, Amy Caudy Genomic data Prior knowledge Computational Predictions of Gene Function SPELL Hibbs et al 2007 bioPIXIE Myers et al 2005 MEFIT Retraining Genes predicted to function in mitochondrion organization and biogenesis New known functions for correctly predicted genes Could go (-) Laboratory Experiments Petite frequency Growth curves Confocal microscopy
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Evaluating the performance of computational predictions
Genes involved in mitochondrion organization and biogenesis 106 Original GO Annotations 135 Under-annotations 82 Novel Confirmations, First Iteration 17 Novel Confirmations, Second Iteration 340 total: >3x previously known genes in ~5 person-months Could go (-)
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Evaluating the performance of computational predictions
Genes involved in mitochondrion organization and biogenesis 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. 106 Original GO Annotations 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 Could go (-)
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Outline Functional network integration Network meta-analysis
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
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Functional mapping: mining integrated networks
Predicted relationships between genes High Confidence Low The strength of these relationships indicates how cohesive a process is. Chemotaxis
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Functional mapping: mining integrated networks
Predicted relationships between genes High Confidence Low Chemotaxis
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Functional mapping: mining integrated networks
Predicted relationships between genes High Confidence Low The strength of these relationships indicates how associated two processes are. Chemotaxis Flagellar assembly
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Functional mapping: Associations among processes
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 Edges Associations between processes Moderately Strong Very Strong
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Functional mapping: Associations among processes
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 Edges Associations between processes Moderately Strong Very Strong Borders Data coverage of processes Sparsely Covered Well Covered
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Functional mapping: Associations among processes
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 Edges Associations between processes Moderately Strong Very Strong Nodes Cohesiveness of processes Below Baseline Baseline (genomic background) Very Cohesive Borders Data coverage of processes Sparsely Covered Well Covered
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Functional mapping: Associations among processes
Edges Associations between processes Moderately Strong Very Strong Nodes Cohesiveness of processes Below Baseline Baseline (genomic background) Very Cohesive Borders Data coverage of processes Sparsely Covered Well Covered
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How do functional interactions become pathways?
Gene expression Physical PPIs Genetic interactions Colocalization Sequence Protein domains Regulatory binding sites … ? + =
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Simultaneous inference of physical, genetic, regulatory, and functional networks
With Chris Park, Olga Troyanskaya Functional genomic data Functional interactions Regulatory interactions Post-transcriptional regulation Phosphorylation Metabolic interactions Protein complexes
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Learning a compendium of interaction networks
Train one SVM per interaction type Resolve consistency using hierarchical Bayes net
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Learning a compendium of interaction networks
Both presence/absence and directionality of interactions are accurately inferred AUC 0.5 1.0
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Using network compendia to predict complete pathways
With David Hess Additional 20 novel synthetic lethality predictions tested, 14 confirmed (>100x better than random) Adr1 – known carbon metabolism TF activator with many poorly characterized regulatory inputs Snf1 is a primary glucose-responsive regulator (kinase, repressor), but the mechanism of downstream regulation isn’t known Cmk2 is a calmodulin-dependent kinase with known involvement in the glucose response Glc7 is a protein phosphatase that’s known to be post-translationally regulated by Snf1 We predict specific mechanisms for these regulatory interactions and order them into a pathway based on synthetic alleviation Gph1 is a glycogen phosphorylase that mobilizes glycogen for initial processing into glucose Both Adr1 and Gph1 known to be cAMP dependent Our only metabolic interaction prediction hypothesizes coregulation by metabolite dependence Syn. let. predictions chosen from hubs in DNA topological change, isolated pairs in protein biosynthesis Confirmed Unconfirmed
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Interactive aligned network viewer – http://function. princeton
Graphle
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Outline Functional network integration Network meta-analysis
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
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Human Regulatory Networks
Quiescence: reversible exit from the cell cycle I III IV V VI VII IX VIII II X 6,829 genes Serum re-stimulated (hrs) Serum starved (hrs) 1 5< <5 2 4 8 24 96 48 FIRE: Elemento et al. 2007 Elk-1 Sp1 NF-Y YY1 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 Development Cholesterol Protein localization Cell cycle RNA processing Metabolism
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COALESCE: Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction
5’ UTR 3’ UTR Upstream flank Downstream flank Nucleosome Positions Gene Expression DNA Sequence Evolutionary Conservation Create a new module Feature selection: Tests for differential expression/frequency Identify conditions where genes coexpress Identify motifs enriched in genes’ sequences Bayesian integration Select genes based on conditions and motifs Regulatory modules Coregulated genes Conditions where they’re coregulated Putative regulating motifs Subtract mean from all data
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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 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
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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 ACGACGT ACGACAT | ATGTCGT A T C G This can distinguish flanks from UTRs Fast! Efficient enough to search coding sequence (e.g. exons/introns)
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COALESCE: Selecting Probable Genes
For each gene in the genome… 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? 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… Distributions of each feature in and out of the developing module are observed from the data.
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COALESCE: Integrating Additional Data Types
Nucleosome placement Evolutionary conservation N C G1 2.5 0.0 G2 0.6 0.5 G3 1.2 0.9 … 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. TCCGGTAGAACTACTGGTATTGTTTTGGATTCCGGTGATG
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COALESCE Results: S. cerevisiae Modules
~2,200 conditions A needle 100 genes 80 conditions The haystack ~6,000 genes
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COALESCE Results: S. cerevisiae Modules
54 genes, 144 conditions Conjugation 112 genes, 82 conditions Mitosis and DNA replication 266 1612 Ste12 Stb1/Swi6 33 genes, 434 conditions Budding Swi5 284
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COALESCE Results: S. cerevisiae Modules
126 genes, 660 conditions Glycolysis, iron and phosphate transport, amino acid metabolism… Aft1/2 50 genes, 775 conditions Iron transport 174 175 Helix-Loop-Helix Tye7/Cbf1/Pho4 11 genes, 844 conditions Phosphate transport 176 Pho4
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COALESCE Results: S. cerevisiae Modules
72 genes, 319 conditions Mitochondrial translation Puf3 822 …plus more ribosome clusters than you can shake a stick at!
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COALESCE Results: Yeast TF/Target Accuracy
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COALESCE Results: TF/Targets Influenced by Supporting Data
Decreased by addl. data Improved by conservation Improved only by both Improved by any addl. data, mainly conservation
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COALESCE Results: Yeast Clustering Accuracy
~2,200 yeast conditions Recapitulation of known biology from Gene Ontology
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COALESCE Results: Yeast Clustering Accuracy
C. elegans: Up in larvae, down in adults GATA in 5’ flank, miR-788 seed in 3’ UTR ~2,200 yeast conditions Recapitulation of known biology from Gene Ontology ASCL1 in 5’ flank, unch. sequences underenriched in 3’ UTR M. musculus: Up in callosal and motor neurons AAGGGGC (zf?) and enriched in 5’ flank H. sapiens: Up in normal muscle, down in diabetic
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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.)
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COALESCE: Coregulated Quiescence Modules
Up during quiescence entry, down during quiescence exit Many known related (proliferation) motifs: Pax4, Staf, NFKB1, Gfi, ESR1, Runx1, Su(H) Down with let-7 exposure let-7 motifs predicted in 3’ UTR (UACCUC) Down during quiescence entry, enriched for transport/trafficking miR-297 motif predicted in 3’ UTR (CACATAC) Down during quiescence entry, up during quiescence exit, down with adenoviral infection Specific predicted uncharacterized reverse complement motif
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Network Motifs Feedback Positive auto-regulation
Negative auto-regulation memory delay speed + stability Coherent feed-forward Bi-fan filter Incoherent feed-forward WGD and evolvability pulse
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From Milo, et al., Science, 2002 March 1, 2010
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Outline Functional network integration Network meta-analysis
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
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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, 2010
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