1. Large scale genomic data mining Curtis Huttenhower 10-23-09 Harvard School of Public Health Department of Biostatistics

2. Mining Biological Data ~100 GB More than 100GB

3. Mining Biological Data ~100 GB More than 100GB

4. Mining Biological Data ~100 GB More than 100GB How can we ask and answer specific biomedical questions using thousands of genome-scale datasets?

5. Outline 5 2. Applications: Human molecular data and clinical cancer cohorts 1. Methodology: Algorithms for mining genome-scale datasets 3. Next steps: Methods for microbial communities and functional metagenomics

6. A Definition of Functional Genomics 6 Genomic data Prior knowledge Data ↓ Function ↓ Function Gene ↓ Gene ↓ Function

7. MEFIT: A Framework for Functional Genomics 7 BRCA1BRCA20.9 BRCA1RAD510.8 RAD51TP530.85 … Related Gene Pairs High Correlation Low Correlation Frequency MEFIT

8. MEFIT: A Framework for Functional Genomics 8 BRCA1BRCA20.9 BRCA1RAD510.8 RAD51TP530.85 … BRCA2SOX20.1 RAD51FOXP20.2 ACTR1H6PD0.15 … Related Gene Pairs Unrelated Gene Pairs High Correlation Low Correlation Frequency MEFIT

9. MEFIT: A Framework for Functional Genomics 9 Golub 1999 Butte 2000 Whitfield 2002 Hansen 1998 Functional Relationship

10. MEFIT: A Framework for Functional Genomics 10 Golub 1999 Butte 2000 Whitfield 2002 Hansen 1998 Functional Relationship Biological Context Functional area Tissue Disease …

11. Functional Interaction Networks 11 MEFIT Global interaction network Autophagy network Vacuolar transport network Translation network Currently have data from 30,000 human experimental results, 15,000 expression conditions + 15,000 diverse others, analyzed for 200 biological functions and 150 diseases

12. Predicting Gene Function 12 Cell cycle genes Predicted relationships between genes High Confidence Low Confidence

13. Predicting Gene Function 13 Predicted relationships between genes High Confidence Low Confidence Cell cycle genes

14. Predicting Gene Function 14 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.

15. Comprehensive Validation of Computational Predictions 15 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

16. Evaluating the Performance of Computational Predictions 16 106 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

17. Evaluating the Performance of Computational Predictions 17 106 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.

18. Functional Associations Between Contexts 18 Predicted relationships between genes High Confidence Low Confidence The average strength of these relationships indicates how cohesive a process is. Cell cycle genes

19. Functional Associations Between Contexts 19 Predicted relationships between genes High Confidence Low Confidence Cell cycle genes

20. Functional Associations Between Contexts 20 DNA replication genes The average strength of these relationships indicates how associated two processes are. Predicted relationships between genes High Confidence Low Confidence Cell cycle genes

21. Functional mapping: Scoring functional associations 21 How can we formalize these relationships? Any sets of genes G 1 and G 2 in a network can be compared using four measures: Edges between their genes Edges within each set The background edges incident to each set The baseline of all edges in the network Stronger connections between the sets increase association. Stronger within self-connections or nonspecific background connections decrease association.

22. Functional mapping: Bootstrap p-values Scoring functional associations is great… …how do you interpret an association score? –For gene sets of arbitrary sizes? –In arbitrary graphs? –Each with its own bizarre distribution of edges? 22 Empirically! # Genes 151050 1 5 10 50 Histograms of FAs for random sets For any graph, compute FA scores for many randomly chosen gene sets of different sizes. Null distribution is approximately normal with mean 1. Standard deviation is asymptotic in the sizes of both gene sets. Maps FA scores to p-values for any gene sets and underlying graph. Null distribution σ s for one graph

23. Functional Associations Between Processes 23 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

24. Functional Associations Between Processes 24 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

25. Functional Associations Between Processes 25 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 AHP1 DOT5 GRX1 GRX2 … APE3 LAP4 PAI3 PEP4 …

26. Functional Maps: Focused Data Summarization 26 ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA Data integration summarizes an impossibly huge amount of experimental data into an impossibly huge number of predictions; what next?

27. Functional Maps: Focused Data Summarization 27 ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA How can a biologist take advantage of all this data to study his/her favorite gene/pathway/disease without losing information? Functional mapping Very large collections of genomic data Specific predicted molecular interactions Pathway, process, or disease associations Underlying experimental results and functional activities in data

28. Outline 28 2. Applications: Human molecular data and clinical cancer cohorts 1. Methodology: Algorithms for mining genome-scale datasets 3. Next steps: Methods for microbial communities and functional metagenomics

29. HEFalMp: Predicting human gene function 29 HEFalMp

30. HEFalMp: Predicting human genetic interactions 30 HEFalMp

31. HEFalMp: Analyzing human genomic data 31 HEFalMp

32. HEFalMp: Understanding human disease 32 HEFalMp

33. Validating Human Predictions 33 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

34. Current Work: Molecular Mechanisms in a Colon Cancer Cohort 34 With Shuji Ogino, Charlie Fuchs ~3,100 gastrointestinal subjects ~3,800 tissue samples ~1,450 colon cancer samples ~1,150 CpG island methylation ~1,200 LINE-1 methylation ~700 TMA immuno- histochemistry ~2,100 cancer mutation tests Health Professionals Follow-Up Study Nurse’s Health Study LINE-1 Methylation Repetitive element making up ~20% of mammalian genomes Very easy to assay methylation level (%) Good proxy for whole-genome methylation level DASL Gene Expression Gene expression analysis from paraffin blocks Thanks to Todd Golub, Yujin Hoshida ~775 gene expression

35. Colon Cancer: LINE-1 methylation levels 35 ρ = 0.718, p < 0.01 Ogino et al, 2008 Lower LINE-1 methylation associates with poor colon cancer prognosis. LINE-1 methylation varies remarkably between individuals… …but it is highly correlated within individuals. What does it all mean?? What is the biological mechanism linking LINE-1 methylation to colon cancer? With Shuji Ogino, Charlie Fuchs

36. Colon Cancer: LINE-1 methylation levels 36 ρ = 0.718, p < 0.01 Ogino et al, 2008 Lower LINE-1 methylation associates with poor colon cancer prognosis. LINE-1 methylation varies remarkably between individuals… …but it is highly correlated within individuals. This suggests a genetic effect. This suggests a copy number variation. This suggests linkage to a cancer-related pathway. Is anything different about these outliers? What is the biological mechanism linking LINE-1 methylation to colon cancer? With Shuji Ogino, Charlie Fuchs

37. Colon Cancer: LINE-1 methylation levels 37 What is the biological mechanism linking LINE-1 methylation to colon cancer? Preliminary Data Six genes differentially expressed even using naïve methods One uncharacterized, one oncogene, three malignancy, one histone 1/3 are from a family with known variable GI expression, prognostic value 2/3 fall in same cytogenic band, which is also a known CNV hotspot HEFalMp links to a set of transmembrane receptors/channels Better analysis pulls out mostly one-carbon metabolism and a few more signaling pathways (neurotransmitters??) Check back in a couple of months!

38. Outline 38 2. Applications: Human molecular data and clinical cancer cohorts 1. Methodology: Algorithms for mining genome-scale datasets 3. Next steps: Methods for microbial communities and functional metagenomics

39. Next Steps: Microbial Communities Data integration is off to a great start in humans –Complex communities of distinct cell types –Very sparse prior knowledge Concentrated in a few specific areas –Variation across populations –Critical to understand mechanisms of disease 39

40. Next Steps: Microbial Communities What about microbial communities? –Complex communities of distinct species/strains –Very sparse prior knowledge Concentrated in a few specific species/strains –Variation across populations –Critical to understand mechanisms of disease 40

41. Next Steps: Functional Metagenomics Metagenomics: data analysis from environmental samples –Microflora: environment includes us! Another data integration problem –Must include datasets from multiple organisms Another context-specificity problem –Now “context” can also mean “species” What questions can we answer? –How do human microflora interact with diabetes, obesity, oral health, antibiotics, aging, … –What’s shared within community X? What’s different? What’s unique? –What’s perturbed in disease state Y? One organism, or many? Host interactions? –Current methods annotate ~50% of synthetic data, <5% of environmental data 41

42. Next Steps: Microbial Communities 42 ~120 available expression datasets ~70 species Weskamp et al 2004 Flannick et al 2006 Kanehisa et al 2008 Tatusov et al 1997 Data integration works just as well in microbes as it does in humans We know an awful lot about some microorganisms and almost nothing about others Purely sequence-based and purely network-based tools for function transfer both fall short We need data integration to take advantage of both and mine out useful biology!

43. Functional Maps for Functional Metagenomics 43 YG17 YG16 YG15 YG10 YG6 YG9 YG8 YG5 YG11 YG7 YG12 YG13 YG14 YG2 YG1 YG4 YG3 KO8 KO 4 KO5 KO7 KO9 KO 6 KO2 KO3 KO1 KO1: YG1, YG2, YG3 KO2: YG4 KO3: YG6 … ECG1, ECG2 PAG1 ECG3, PAG2 …

44. Functional Maps for Functional Metagenomics 44

45. Validating Orthology-Based Functional Mapping 45 Does unweighted data integration predict functional relationships? What is the effect of “projecting” through an orthologous space? Recall log(Precision/Random) KEGG GO Recall log(Precision/Random) Recall log(Precision/Random) GO Unsupervised integration Individual datasets Recall log(Precision/Random) Individual datasets KEGG Unsupervised integration

46. Validating Orthology-Based Functional Mapping 46 YG17 YG16YG15 YG10 YG6 YG9 YG8 YG5 YG11 YG7 YG12 YG13 YG14 YG2 YG1 YG4 YG3 Holdout set, uncharacterized “genome” Random subsets, characterized “genomes”

47. Validating Orthology-Based Functional Mapping 47

48. KEGG GO Validating Orthology-Based Functional Mapping 48 Can subsets of the yeast genome predict a heldout subset’s functional maps? Can subsets of the yeast genome predict a heldout subset’s interactome? 0.680.48 0.390.25 0.300.37 0.270.39 0.43 0.40 What have we learned? Yeast is incredibly well-curated KEGG tends to be more specific than GO Predicting interactomes by projecting through functional maps works decently in the absolute best case

49. Functional Maps for Functional Metagenomics 49 Now, what happens if you do this for characterized microbes? ~20 (somewhat) well-characterized species 1-35 datasets each Integrate within species Evaluate using KEGG Then cross-validate by holding out species Recall log(Precision/Random) KEGG Unsupervised integrations

50. Next Steps: Missing Methodology, Mining Most machine learning algorithms are optimized for one of two cases: –Small, dense data –Large, sparse data HEFalMp integrates ~300M records using ~1K features, relatively few of which are missing, in ~200 contexts 50 Feature selection Regularization Dimension reduction Simple models, efficient algorithms Slightly less

51. Next Steps: Missing Methodology, Models 51 Dataset #1 Dataset #2 … Functional Relationship

52. Next Steps: Missing Methodology, Models 52 Dataset #1 Dataset #2 Dataset #3 … Functional Relationship Biological Context

53. Next Steps: Missing Methodology, Models 53 Dataset #1 Dataset #2 Dataset #3 … Functional Relationship Cellular Processes Tissue/Cell Lineage Disease State Developmental Stage Cross-Species Orthology This is clearly not a sustainable system; novel large-scale hierarchical modeling is needed to capture the complex biology of metazoan and metagenomic interaction networks. Types of Interactions Regulation

54. Efficient Computation For Biological Discovery Massive datasets and genomes require efficient algorithms and implementations. 54 Sleipnir C++ library for computational functional genomics Data types for biological entities Microarray data, interaction data, genes and gene sets, functional catalogs, etc. etc. Network communication, parallelization Efficient machine learning algorithms Generative (Bayesian) and discriminative (SVM) And it’s fully documented! It’s also speedy: improves on Bayes Net Toolbox by ~22x in memory usage and up to >100x in runtime.

55. Efficient Computation For Biological Discovery Massive datasets and genomes require efficient algorithms and implementations. 55 Sleipnir C++ library for computational functional genomics Data types for biological entities Microarray data, interaction data, genes and gene sets, functional catalogs, etc. etc. Network communication, parallelization Efficient machine learning algorithms Generative (Bayesian) and discriminative (SVM) And it’s fully documented! 8 hours 1 minute 30 years 2 months 18 hours Original processing time Current processing time 2-3 hours

56. Outline 56 2. Applications: Human molecular data and clinical cancer cohorts 1. Methodology: Algorithms for mining genome-scale datasets 3. Next steps: Methods for microbial communities and functional metagenomics Bayesian system for genomic data integration Sleipnir software for efficient large scale data mining Functional mapping to statistically summarize large data collections HEFalMp system for human data analysis and integration Six confirmed predictions in autophagy Ongoing analysis of LINE-1 methylation in colon cancer Data integration applied to microbial communities and functional metagenomics Efficient machine learning for large, dense feature spaces

57. Thanks! 57 http://function.princeton.edu/sleipnir http://function.princeton.edu/hefalmp Interested? We’re looking for students and postdocs! Biostatistics Department http://huttenhower.sph.harvard.edu Interested? We’re looking for students and postdocs! Biostatistics Department http://huttenhower.sph.harvard.edu Hilary Coller Erin Haley Tsheko Mutungu Olga Troyanskaya Matt Hibbs Chad Myers David Hess Edo Airoldi Florian Markowetz Shuji Ogino Charlie Fuchs

59. Colon Cancer: Immunohistochemistry 59 Tumor #1Tumor #2…Tumor #700 AKT101155 AURKA050 CCND125030 …… Genes Conditions Quantities The world’s smallest, cheapest microarray! What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation?

60. Colon Cancer: Immunohistochemistry 60 ~700 Tumor Samples LINE-1 hypomethylated outliersLINE-1 methylation “normal” What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? The world’s smallest, cheapest microarray!

61. Colon Cancer: Mining Microarrays 61 ~650 datasets ~15,000 expression conditions ~24,000 genes Most like our 26-gene LINE-1 differential methylation signature Least like the signature 26 genes in signature What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation.

62. Colon Cancer: Mining Microarrays 62 “The goal of GSEA is to determine whether members of a gene set S tend to occur toward the top (or bottom) of the list L.” data Subramanian et al, 2005 Most like our 26-gene LINE-1 differential methylation signature Least like the signature Bleomycin effect on mutagen- sensitive lymphoblastoid cells Folic acid deficiency effect on colon cancer cells Bladder tumor stage classification Normal tissue of diverse types Muscle function and aging Non-diseased lung tissue What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation. What CNV-linked genes are differentially expressed in these datasets? Dataset 1 Condition X Condition Y Condition Z Dataset 2 Condition A Condition B Condition C Condition D Condition E

63. Colon Cancer: Mining Microarrays 63 “The goal of GSEA is to determine whether members of a gene set S tend to occur toward the top (or bottom) of the list L.” Subramanian et al, 2005 What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation. What CNV-linked genes are differentially expressed in these datasets? CNV 1 Gene X Gene Y Gene Z CNV 2 Gene A Gene B Gene C Gene D Gene E Most upregulated in significantly enriched datasets Most downregulated PSGs (11 genes on 19q13.3) PCDHs (~50 genes on 5q31.3)Misc. ~12 genes on 16p13.3 Iafrate et al, 2005 ?

64. Colon Cancer: Mining Microarrays 64 What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation. What CNV-linked genes are differentially expressed in these datasets? Iafrate et al, 2005 Pregnancy specific β glycoproteins Salahshor et al, 2005 “PSG9 is not found in the non- pregnant adult except in association with cancer, and it appears to be an early molecular event associated with colorectal cancer.” Differential gene expression profile reveals deregulation of pregnancy specific β1 glycoprotein 9 early during colorectal carcinogenesis

65. Colon Cancer: Generating a Hypothesis 65 What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation. What CNV-linked genes are differentially expressed in these datasets? Pregnancy specific β glycoproteins

66. Colon Cancer: Generating a Hypothesis 66 What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation. What CNV-linked genes are differentially expressed in these datasets? Pregnancy specific β glycoproteins

67. Colon Cancer: Using All the Data 67 What is the biological mechanism linking LINE-1 methylation to colon cancer? What does the IHC data tell us about LINE-1 hypomethylation? Can existing microarrays amplify the LINE-1 hypomethylation signal? Identify microarray datasets with conditions enriched for LINE-1 hypomethylation. What CNV-linked genes are differentially expressed in these datasets? Pregnancy specific β glycoproteins GI cancers and chemotherapy Yes (caveat investigator) Get back to me in a couple of months… What’s the state of the data? Extremely hypomethylated colon cancer carries a significantly poor prognosis In our cohort, these ~20 tumors are weakly enriched for a protein activity signature based on IHC The expression datasets most enriched for the same signature represent mainly GI cancer and chemotherapy conditions The PSG gene family is upregulated in these datasets and is linked to a known CNV HEFalMp associates the PSGs with cancer based on correlation with known colorectal cancer genes in a variety of expression datasets Nothing definite – yet.

68. 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 68 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

69. Regulatory Modules: Expression Biclusters + Sequence Motifs 69 CRG1 CRG2 CRG3 CRG4 RND1 RND2 RND3 RND4 RND5 RND6 RND7 RND8 34712568 Bicluster: Coregulated subset of genes and conditions

70. Regulatory Modules: Expression Biclusters + Sequence Motifs 70 CRG1 CRG2 CRG4 CRG3 RND1 RND2 RND3 RND4 RND5 RND6 RND7 RND8 34712568 Bicluster: Coregulated subset of genes and conditions

71. Regulatory Modules: Expression Biclusters + Sequence Motifs 71 CRG1 CRG2 CRG4 CRG3 RND1 RND2 RND3 RND4 RND5 RND6 RND7 RND8 34712568 Bicluster: Coregulated subset of genes and conditions …do all that, and simultaneously find (under)enriched sequence motifs! …any dataset can contain many overlapping biclusters… …any gene or condition can participate in multiple biclusters…

72. COALESCE: Combinatorial Algorithm for Expression and Sequence-based Cluster Extraction 72 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

73. 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 73 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

74. 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 74 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)

75. COALESCE: Selecting Probable Genes For each gene in the genome… 75 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…

76. COALESCE: Integrating Additional Data Types 76 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

77. COALESCE Results: S. cerevisiae Modules 77 ~2,200 conditions ~6,000 genes The haystack A needle 100 genes 80 conditions

78. COALESCE Results: Yeast TF/Target Accuracy 78

79. COALESCE Results: Yeast Clustering Accuracy ~2,200 yeast conditions –Recapitulation of known biology from Gene Ontology 79

80. COALESCE Results: Yeast Clustering Accuracy ~2,200 yeast conditions –Recapitulation of known biology from Gene Ontology 80 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

81. COALESCE: Coregulated Quiescence Modules 81 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)

82. Summary COALESCE algorithm for regulatory module prediction –Biclustering + putative de novo motifs –Optimized for complex organisms (fast!) Large genomes, large data collections –High accuracy, low false positives –Leverage prior knowledge, multiple data types 82