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Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data

Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data. Jim Huang (1). Joint work with Quaid Morris (2) and Brendan Frey (1),(2). Probabilistic and Statistical Inference Group, Edward S. Rogers Department of Electrical and Computer Engineering,

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Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data

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

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

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

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

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

  6. Mechanisms for microRNA regulation Post-transcriptional degradation z y x Transcription RISC RISC miRNA mRNA protein Translational repression z y x miRNA mRNA protein Transcription Translation • Toronto microRNA, mRNA and protein data • TargetScanS microRNA target predictions Combine: RECOMB 2006

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

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

  9. The GenMiR method • 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 RECOMB 2006

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

  11. A Bayesian network for detecting microRNA targets tissues t = 1,…,T messenger RNAs g = 1,…,G microRNAs k = 1,…,K Indicator of putative interaction between microRNA k and target transcript g cgk microRNA expression level Indicator variable for whether microRNA k truly targets transcript g sgk zkt Target transcript expression level xgt RECOMB 2006

  12. A probabilistic model for microRNA regulation tissues t = 1,…,T messenger RNAs g = 1,…,G microRNAs k = 1,…,K Indicator of putative interaction between microRNA k and target transcript g cgk microRNA expression level Indicator variable for whether microRNA k truly targets transcript g sgk zkt Target transcript expression level xgt RECOMB 2006

  13. A probabilistic model for microRNA regulation Indicator of putative interaction between microRNA k and target transcript g cgk Targeting probabilities Indicator variable for whether microRNA k truly targets transcript g sgk RECOMB 2006

  14. A probabilistic model for microRNA regulation tissues t = 1,…,T messenger RNAs g = 1,…,G microRNAs k = 1,…,K Indicator of putative interaction between microRNA k and target transcript g cgk microRNA expression level Indicator variable for whether microRNA k truly targets transcript g sgk zkt Target transcript expression level xgt RECOMB 2006

  15. A probabilistic model for microRNA regulation Probability of data given targeting interaction microRNA expression level Indicator variable for whether microRNA k truly targets transcript g sgk zkt Target transcript expression level xgt RECOMB 2006

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

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

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

  19. Detecting microRNA targets Permuted miRNA data miRNA data RECOMB 2006

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

  21. 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/ RECOMB 2006

  22. The road ahead… • 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 J.C. Huang, Q.D. Morris and B.J. Frey. Bayesian Learning of MicroRNA Targets from Sequence and Expression Data (submitted for publication) RECOMB 2006

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