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Answering biological questions using large genomic data collections Curtis Huttenhower Harvard School of Public Health Department of Biostatistics 10-05-09 A Definition of Computational Functional Genomics Genomic data Gene ↓ Function Gene ↓ Gene Prior knowledge Data ↓ Function Function ↓ Function 2 MEFIT: A Framework for Functional Genomics Related Gene Pairs BRCA1 BRCA2 0.9 BRCA1 RAD51 0.8 RAD51 TP53 0.85 … Frequency MEFIT Low Correlation High Correlation 3 MEFIT: A Framework for Functional Genomics Related Gene Pairs BRCA1 BRCA2 0.9 BRCA1 RAD51 0.8 RAD51 TP53 0.85 … Frequency MEFIT Unrelated Gene Pairs BRCA2 SOX2 0.1 RAD51 FOXP2 0.2 ACTR1 H6PD 0.15 … Low Correlation High Correlation 4 MEFIT: A Framework for Functional Genomics Functional Relationship Golub 1999 Butte 2000 Whitfield 2002 Hansen 1998 5 MEFIT: A Framework for Functional Genomics Functional Relationship Golub 1999 Butte 2000 Biological Context Whitfield 2002 Functional area Tissue Disease … Hansen 1998 6 Functional Interaction Networks Global interaction 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 MEFIT Autophagy network Vacuolar transport network Translation network 7 Predicting Gene Function Predicted relationships between genes Low Confidence High Confidence Cell cycle genes 8 Predicting Gene Function Predicted relationships between genes Low Confidence High Confidence Cell cycle genes 9 Predicting Gene Function Predicted relationships between genes Low Confidence High Confidence These edges provide a measure of how likely a gene is to specifically participate in the process of interest. Cell cycle genes 10 Functional Associations Between Contexts Predicted relationships between genes Low Confidence High Confidence The average strength of these relationships indicates how cohesive a process is. Cell cycle genes 11 Functional Associations Between Contexts Predicted relationships between genes Low Confidence High Confidence Cell cycle genes 12 Functional Associations Between Contexts Predicted relationships between genes Low Confidence High Confidence The average strength of these relationships indicates how associated two processes are. Cell cycle genes DNA replication genes 13 Functional Associations Between Processes Hydrogen Transport Electron Transport Edges Associations between processes Cellular Respiration Aldehyde Metabolism Very Strong Cell Redox Homeostasis Peptide Metabolism Energy Reserve Metabolism Moderately Strong Vacuolar Protein Catabolism Protein Processing Negative Regulation of Protein Metabolism Protein Depolymerization Organelle Fusion Organelle Inheritance 14 Functional Associations Between Processes Hydrogen Transport Electron Transport Edges Associations between processes Cellular Respiration Aldehyde Metabolism Very Strong Cell Redox Homeostasis Peptide Metabolism Energy Reserve Metabolism Moderately Strong Vacuolar Protein Catabolism Protein Processing Negative Regulation of Protein Metabolism Borders Protein Depolymerization Data coverage of processes Organelle Fusion Sparsely Covered Well Covered Organelle Inheritance 15 Functional Associations Between Processes Hydrogen Transport Electron Transport AHP1 DOT5 GRX1 GRX2 … Aldehyde Metabolism Edges Associations between processes Cellular Respiration Very Strong Cell Redox Homeostasis Peptide Metabolism APE3 Energy LAP4 Reserve PAI3 Metabolism PEP4 … Moderately Strong Vacuolar Protein Catabolism Protein Processing Negative Regulation of Protein Metabolism Nodes Cohesiveness of processes Below Baseline Baseline (genomic background) Very Cohesive Borders Protein Depolymerization Data coverage of processes Organelle Fusion Sparsely Covered Well Covered Organelle Inheritance 16 HEFalMp: Predicting human gene function HEFalMp 17 HEFalMp: Predicting human genetic interactions HEFalMp 18 HEFalMp: Analyzing human genomic data HEFalMp 19 HEFalMp: Understanding human disease HEFalMp 20 Validating Human Predictions With Erin Haley, Hilary Coller Autophagy 5½ of 7 predictions currently confirmed Luciferase ATG5 (Negative control) (Positive control) Predicted novel autophagy proteins LAMP2 RAB11A Not Starved Starved (Autophagic) 21 Comprehensive Validation of Computational Predictions With David Hess, Amy Caudy Genomic data Prior knowledge Computational Predictions of Gene Function SPELL bioPIXIE MEFIT Hibbs et al 2007 Myers et al 2005 Retraining New known functions for correctly predicted genes Genes predicted to function in mitochondrion organization and biogenesis Laboratory Experiments Growth curves Petite frequency Confocal microscopy 22 Evaluating the Performance of Computational Predictions Genes involved in mitochondrion organization and biogenesis 106 135 Original GO Annotations Under-annotations 82 17 Novel Confirmations, Novel Confirmations, First Iteration Second Iteration 340 total: >3x previously known genes in ~5 person-months 23 Evaluating the Performance of Computational Predictions Genes involved in mitochondrion organization and biogenesis Computational 95 predictions 40from large 80 17 Original GO Annotations Under-annotations collections of genomicConfirmed data canNovel be Confirmations Novel Confirmations Under-annotations First Iteration Second Iteration accurate despite incomplete or misleading gold standards, 340 total: >3x previously known genesand in they ~5 person-months continue to improve as additional data are incorporated. 106 24 Functional Maps: Focused Data Summarization ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA Data integration summarizes an impossibly huge amount of experimental data into an impossibly huge number of predictions; what next? 25 Functional Maps: Focused Data Summarization ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA How can a researcher 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 26 Thanks! Olga Troyanskaya Matt Hibbs Chad Myers David Hess Edo Airoldi Florian Markowetz Hilary Coller Erin Haley Tsheko Mutungu Shuji Ogino Charlie Fuchs Interested? I’m accepting students and postdocs! http://www.huttenhower.org http://function.princeton.edu/hefalmp 27 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 29 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 30 Next Steps: Microbial Communities ~120 available expression datasets ~70 species • • • • 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! Weskamp et al 2004 Flannick et al 2006 Kanehisa et al 2008 Tatusov et al 1997 31 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 32