1. Computational Methodology for Microbial and Metagenomic Characterization using Large Scale Functional Genomic Data Integration Curtis Huttenhower 03-08-10 Harvard School of Public Health Department of Biostatistics

2. Outline 2 1. Network models of functional data 2. Network models of microbes 3. Network models of microbiomes

3. Meta-analysis for unsupervised functional data integration 3 Following up with round-robin and semi-supervised evaluations Huttenhower 2006 Hibbs 2007 + =

4. Functional network prediction from diverse microbial data 4 486 bacterial expression experiments 876 raw datasets 310 postprocessed datasets 304 normalized coexpression networks in 27 species Integrated functional interaction networks in 15 species 307 bacterial interaction experiments 154796 raw interactions 114786 postprocessed interactions

5. Functional maps for cross-species knowledge transfer 5 Following up with unsupervised and partially anchored network alignment Huttenhower 2008 Huttenhower 2009

6. Functional maps for functional metagenomics 6 Mapping genes into pathways Mapping pathways into organisms + Integrated functional interaction networks in 27 species Mapping organisms into phyla = GOS 4441599.3 Hypersaline Lagoon, Ecuador

7. Functional maps for functional metagenomics 7 Nodes Process cohesiveness in obesity Very Downregulated Baseline (no change) Very Upregulated Edges Process association in obesity More Coregulated Less Coregulated Baseline (no change) Summarizes information from ~10M metagenomic reads and ~500 genome- scale microbial experiments.

8. 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! Efficient Computation For Biological Discovery Massive datasets and genomes require efficient algorithms and implementations. 8 It’s also speedy: microbial data integration computation takes <3hrs.

9. Thanks! 9 http://function.princeton.edu/hefalmp http://huttenhower.sph.harvard.edu/sleipnir Olga Troyanskaya Matt Hibbs Chad Myers David Hess Chris Park Ana Pop Aaron Wong Hilary Coller Erin Haley Jacques Izard Wendy Garrett Sarah Fortune Tracy Rosebrock

11. Functional mapping: Functional associations between processes 11 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 Information mapped from ~100 E. coli experiments

12. Meta-analysis for unsupervised functional data integration 12 Following up with round-robin and semi-supervised evaluations Evangelou 2007 Huttenhower 2006 Hibbs 2007 + =

13. Functional mapping: mining integrated networks 13 Predicted relationships between genes High Confidence Low Confidence The strength of these relationships indicates how cohesive a process is. Chemotaxis

14. Functional mapping: mining integrated networks 14 Predicted relationships between genes High Confidence Low Confidence Chemotaxis

15. Functional mapping: mining integrated networks 15 Flagellar assembly The strength of these relationships indicates how associated two processes are. Predicted relationships between genes High Confidence Low Confidence Chemotaxis

16. Functional maps for cross-species knowledge transfer 16 G17 G16 G15 G10 G6 G9 G8 G5 G11 G7 G12 G13 G14 G2 G1 G4 G3 O8 O4 O5 O7 O9 O6 O2 O3 O1 O1: G1, G2, G3 O2: G4 O3: G6 … ECG1, ECG2 BSG1 ECG3, BSG2 …

17. Functional network prediction from diverse microbial data 17 486 bacterial expression experiments 876 raw datasets 310 postprocessed datasets 304 normalized coexpression networks in 27 species Integrated functional interaction networks in 15 species 307 bacterial interaction experiments 154796 raw interactions 114786 postprocessed interactions E. Coli Integration ← Precision ↑, Recall ↓

18. Functional maps for functional metagenomics 18 GOS 4441599.3 Hypersaline Lagoon, Ecuador KEGG Pathways Organisms Pathogens Env. Mapping genes into pathways Mapping pathways into organisms + Integrated functional interaction networks in 27 species Mapping organisms into phyla =

19. Functional maps for cross-species knowledge transfer 19 ← Precision ↑, Recall ↓ Following up with unsupervised and partially anchored network alignment

20. E. Coli Integration Functional network prediction from diverse microbial data 20 486 bacterial expression experiments 876 raw datasets 310 postprocessed datasets 304 normalized coexpression networks in 27 species Integrated functional interaction networks in 15 species 307 bacterial interaction experiments 154796 raw interactions 114786 postprocessed interactions

21. Functional Maps: Focused Data Summarization 21 ACGGTGAACGTACA GTACAGATTACTAG GACATTAGGCCGTA TCCGATACCCGATA Data integration summarizes an impossibly huge amount of experimental data into an impossibly huge number of predictions; what next?

22. Functional Maps: Focused Data Summarization 22 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

23. Functional Mapping: Scoring Functional Associations 23 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.

24. 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? 24 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

25. Microbial Communities and Functional Metagenomics Metagenomics: data analysis from environmental samples –Microflora: environment includes us! Pathogen collections of “single” organisms form similar communities Another data integration problem –Must include datasets from multiple organisms What questions can we answer? –What pathways/processes are present/over/under- enriched in a newly sequences microbe/community? –What’s shared within community X? What’s different? What’s unique? –How do human microflora interact with diabetes, obesity, oral health, antibiotics, aging, … –Current functional methods annotate ~50% of synthetic data, <5% of environmental data 25 With Jacques Izard, Wendy Garrett

26. Data Integration for Microbial Communities 26 ~350 available expression datasets ~25 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 yeast and humans We know an awful lot about some microorganisms and almost nothing about others Sequence-based and network-based tools for function transfer both work in isolation We can use data integration to leverage both and mine out additional biology

27. Functional Maps for Functional Metagenomics 27

28. Validating Orthology-Based Functional Mapping 28 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

29. Validating Orthology-Based Functional Mapping 29 YG17 YG16YG15 YG10 YG6 YG9 YG8 YG5 YG11 YG7 YG12 YG13 YG14 YG2 YG1 YG4 YG3 Holdout set, uncharacterized “genome” Random subsets, characterized “genomes”

30. Validating Orthology-Based Functional Mapping 30

31. KEGG GO Validating Orthology-Based Functional Mapping 31 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