1. 04/02/2006RECOMB 2006 Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data Joint work with Quaid Morris (2) and Brendan Frey (1),(2) Jim Huang (1) (1)Probabilistic and Statistical Inference Group, Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto (2)Banting & Best Department of Medical Research, University of Toronto

2. 04/02/2006RECOMB 2006 Transcriptional regulation Transcription and splicing mRNA transcript Protein- coding gene Transcription factor

3. 04/02/2006RECOMB 2006 Post-transcriptional regulation Mature microRNA microRNA target site RISC mRNA transcript Silencing microRNA gene

4. 04/02/2006RECOMB 2006 Finding microRNA targets Lots of targets: are they all real? IDEA: Use high-throughput data to find bona fide targets Mature microRNA microRNA target site RISC mRNA transcript Silencing Expression Down- regulation

5. 04/02/2006RECOMB 2006 Post-transcriptional degradation of target mRNA transcript –microRNA triggers the destruction of target Mechanisms for microRNA regulation Translational repression –microRNA prevents translation to protein RISC Transcription RISC Transcription Translation

6. 04/02/2006RECOMB 2006 Mechanisms for microRNA regulation Toronto microRNA, mRNA and protein data TargetScanS microRNA target predictions RISC Transcription Translation RISC miRNA x yz mRNAprotein x yz miRNAmRNAprotein Post-transcriptional degradation Translational repression Combine:

7. 04/02/2006RECOMB 2006 1,770 TargetScanS candidate targets linking 788 targeted mRNA transcripts to 22 microRNAs in 17 tissues Linking microRNA and mRNA expression miR-16/Spleen Expression of putative targets Background expression p < 10 -7

8. 04/02/2006RECOMB 2006 GenMiR Generative model for microRNA regulation Get candidate targets microRNA sequence data mRNA sequence data microRNA expression data mRNA expression data Detected microRNA targets GCATCAT AACTGCA …

9. 04/02/2006RECOMB 2006 Observed: –Set of candidate microRNA targets –microRNA expression data –mRNA expression data Unobserved: –Indicator variables Model parameters: –Regulatory weight for each microRNA –Background level of mRNA expression The GenMiR method

10. 04/02/2006RECOMB 2006 Some notation messenger RNA microRNA Indicator variable for whether microRNA k truly targets mRNA g regulatory weight Indicator of putative interaction between microRNA k and target transcript g

11. 04/02/2006RECOMB 2006 A Bayesian network for detecting microRNA targets Indicator variable for whether microRNA k truly targets transcript g microRNA expression level Target transcript expression level Indicator of putative interaction between microRNA k and target transcript g x gt z kt s gk c gk tissues t = 1,…,T microRNAs k = 1,…,K messenger RNAs g = 1,…,G

12. 04/02/2006RECOMB 2006 A probabilistic model for microRNA regulation Indicator variable for whether microRNA k truly targets transcript g microRNA expression level Target transcript expression level Indicator of putative interaction between microRNA k and target transcript g x gt z kt s gk c gk tissues t = 1,…,T microRNAs k = 1,…,K messenger RNAs g = 1,…,G

13. 04/02/2006RECOMB 2006 A probabilistic model for microRNA regulation Targeting probabilities Indicator variable for whether microRNA k truly targets transcript g Indicator of putative interaction between microRNA k and target transcript g s gk c gk

14. 04/02/2006RECOMB 2006 A probabilistic model for microRNA regulation Indicator variable for whether microRNA k truly targets transcript g microRNA expression level Target transcript expression level Indicator of putative interaction between microRNA k and target transcript g x gt z kt s gk c gk tissues t = 1,…,T microRNAs k = 1,…,K messenger RNAs g = 1,…,G

15. 04/02/2006RECOMB 2006 A probabilistic model for microRNA regulation Probability of data given targeting interaction Indicator variable for whether microRNA k truly targets transcript g microRNA expression level Target transcript expression level x gt z kt s gk

16. 04/02/2006RECOMB 2006 A probabilistic model for microRNA regulation Targeting probabilities Probability of data given targeting interaction Joint probability

17. 04/02/2006RECOMB 2006 Maximize likelihood of observed data: Upper bound on negative log likelihood: Learning microRNA targets GOAL: Optimize fit of model to data Inference Parameter estimation OR

18. 04/02/2006RECOMB 2006 Exact inference: Posterior is intractable to compute! Approximate the posterior distribution: Variational Inference

19. 04/02/2006RECOMB 2006 Detecting microRNA targets Permuted miRNA data miRNA data

20. 04/02/2006RECOMB 2006 Detecting microRNA targets LESSONS: 1) We CAN learn from expression and sequence data! 2) Combinatorics are critical for learning targets!

21. 04/02/2006RECOMB 2006 Summary Evidence that microRNAs operate by degrading target mRNAs Model for combinatorial microRNA regulation High-throughput method for learning bona fide miRNA targets Full list of detected microRNA targets is available at www.psi.toronto.edu/~GenMiR/

22. 04/02/2006RECOMB 2006 The road ahead… J.C. Huang, Q.D. Morris and B.J. Frey. Bayesian Learning of MicroRNA Targets from Sequence and Expression Data (submitted for publication) Differences in normalization and hybridization conditions in mRNA and microRNA data? Bayesian learning Robustness of model and learning algorithm to –Subsampling of data? –Introducing fake targets? Biological verification and network mining

23. 04/02/2006RECOMB 2006 Sufficient statistics

24. 04/02/2006RECOMB 2006 Variational Expectation-Maximization Variational E-step Variational M-step GOAL: Optimize fit of model to data and look at  gk ’s

25. 04/02/2006RECOMB 2006 Variational EM updates Variational E-step Variational M-step

26. 04/02/2006RECOMB 2006 Combinatorial microRNA regulation

27. 04/02/2006RECOMB 2006 Robustness of the GenMiR model