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Detecting MicroRNA Targets by Linking Sequence,MicroRNA and Gene Expression DataJoint work with Quaid Morris(2)and Brendan Frey(1),(2)Jim Huang(1)(1)Probabilistic and Statistical Inference Group,(2)Edward S.Rogers Department of Electrical and Computer Engineering,University of Toronto(2)Banting&Best Department of Medical Research,(3)University of Toronto04/02/2006RECOMB 2006Detecting MicroRNA Targets by 1Transcriptional regulationTranscription and splicingmRNA transcriptProtein-coding geneTranscription factor04/02/2006RECOMB 2006Transcriptional regulationTran2Post-transcriptional regulationMature microRNAmicroRNA target siteRISCmRNA transcriptSilencingmicroRNA gene04/02/2006RECOMB 2006Post-transcriptional regulatio3miRNA-靶分子的检测课件4miRNA-靶分子的检测课件5Mechanisms for microRNA regulationToronto microRNA,mRNA and protein dataTargetScanS microRNA target predictionsRISCTranscriptionTranscriptionTranslationRISCmiRNAxyzmRNAproteinxyzmiRNA mRNAproteinPost-transcriptional degradationTranslational repressionCombine:04/02/2006RECOMB 2006Mechanisms for microRNA regula61,770 TargetScanS candidate targets linking 788 targeted mRNA transcripts to 22 microRNAs in 17 tissuesLinking microRNA and mRNA expressionmiR-16/SpleenExpression of putative targetsBackground expressionp 10-704/02/2006RECOMB 20061,770 TargetScanS candidate ta7GenMiRGenerative model for microRNA regulationGet candidate targets microRNA sequence datamRNA sequence datamicroRNA expression datamRNA expression dataDetected microRNA targetsGCATCATAACTGCA04/02/2006RECOMB 2006GenMiRGenerative model for mic8Observed:Set of candidate microRNA targetsmicroRNA expression datamRNA expression dataUnobserved:Indicator variablesModel parameters:Regulatory weight for each microRNABackground level of mRNA expressionThe GenMiR method04/02/2006RECOMB 2006Observed:The GenMiR method04/09Some notationmessengerRNAmicroRNAIndicator variable for whether microRNA k truly targets mRNA gregulatory weightIndicator of putative interaction between microRNA k and target transcript g04/02/2006RECOMB 2006Some notationmessengerRNAmicro10A Bayesian network for detecting microRNA targetsIndicator variable for whether microRNA k truly targets transcript gmicroRNA expression levelTarget transcript expression levelIndicator of putative interaction between microRNA k and target transcript gxgtzktsgkcgktissues t=1,TmicroRNAs k=1,Kmessenger RNAs g=1,G04/02/2006RECOMB 2006A Bayesian network for detecti11A probabilistic model for microRNA regulationIndicator variable for whether microRNA k truly targets transcript gmicroRNA expression levelTarget transcript expression levelIndicator of putative interaction between microRNA k and target transcript gxgtzktsgkcgktissues t=1,TmicroRNAs k=1,Kmessenger RNAs g=1,G04/02/2006RECOMB 2006A probabilistic model for micr12A probabilistic model for microRNA regulationTargeting probabilitiesIndicator variable for whether microRNA k truly targets transcript gIndicator of putative interaction between microRNA k and target transcript gsgkcgk04/02/2006RECOMB 2006A probabilistic model for micr13A probabilistic model for microRNA regulationIndicator variable for whether microRNA k truly targets transcript gmicroRNA expression levelTarget transcript expression levelIndicator of putative interaction between microRNA k and target transcript gxgtzktsgkcgktissues t=1,TmicroRNAs k=1,Kmessenger RNAs g=1,G04/02/2006RECOMB 2006A probabilistic model for micr14A probabilistic model for microRNA regulationProbability of data given targeting interactionIndicator variable for whether microRNA k truly targets transcript gmicroRNA expression levelTarget transcript expression levelxgtzktsgk04/02/2006RECOMB 2006A probabilistic model for micr15A probabilistic model for microRNA regulationTargeting probabilitiesProbability of data given targeting interactionJoint probability04/02/2006RECOMB 2006A probabilistic model for micr16Maximize likelihood of observed data:Upper bound on negative log likelihood:Learning microRNA targetsGOAL:Optimize fit of model to dataInferenceParameter estimationOR04/02/2006RECOMB 2006Maximize likelihood of observe17Exact inference:Posterior is intractable to compute!Approximate the posterior distribution:Variational Inference04/02/2006RECOMB 2006Exact inference:Variational In18Detecting microRNA targetsPermuted miRNA datamiRNA data04/02/2006RECOMB 2006Detecting microRNA targetsPerm19Detecting microRNA targetsLESSONS:1)We CAN learn from expression and sequence data!2)Combinatorics are critical for learning targets!04/02/2006RECOMB 2006Detecting microRNA targetsLESS20SummaryEvidence that microRNAs operate by degrading target mRNAsModel for combinatorial microRNA regulationHigh-throughput method for learning bona fide miRNA targetsFull list of detected microRNA targets is available at www.psi.toronto.edu/GenMiR/04/02/2006RECOMB 2006SummaryEvidence that microRNAs21The road aheadJ.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 learningRobustness of model and learning algorithm to Subsampling of data?Introducing fake targets?Biological verification and network mining 04/02/2006RECOMB 2006The road aheadJ.C.Huang,Q.D22
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