Post on 06-Feb-2016
description
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
04/02/2006RECOMB 2006
Transcriptional regulation
Transcription and splicing
mRNA transcript
Protein- coding gene
Transcription factor
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Post-transcriptional regulation
Mature microRNA
microRNA target site
RISC
mRNA transcript
Silencing
microRNA gene
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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
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
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Mechanisms for microRNA regulation
• Toronto microRNA, mRNA and protein data
• TargetScanS microRNA target predictions
RISC
Transcription
Transcription
TranslationRISC
miRNA
x yz
mRNA protein
x yz
miRNA mRNA protein
Post-transcriptional degradation
Translational repression
Combine:
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
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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
…
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
04/02/2006RECOMB 2006
Some notation
messengerRNA
microRNA
Indicator variable for whether microRNA k truly targets mRNA g
regulatory weight
Indicator of putative interaction between microRNA k and target transcript g
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
xgt
zkt sgk
cgk
tissues t = 1,…,T
microRNAs k = 1,…,K
messenger RNAs g = 1,…,G
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
xgt
zkt sgk
cgk
tissues t = 1,…,T
microRNAs k = 1,…,K
messenger RNAs g = 1,…,G
04/02/2006RECOMB 2006
A probabilistic model for microRNA regulation
Targeting probabilitiesIndicator variable for whether microRNA k truly targets transcript g
Indicator of putative interaction between microRNA k and target transcript g
sgk
cgk
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
xgt
zkt sgk
cgk
tissues t = 1,…,T
microRNAs k = 1,…,K
messenger RNAs g = 1,…,G
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
xgt
zkt sgk
04/02/2006RECOMB 2006
A probabilistic model for microRNA regulation
Targeting probabilities
Probability of data given targeting interaction
Joint probability
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
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• Exact inference:
• Posterior is intractable to compute!
• Approximate the posterior distribution:
Variational Inference
04/02/2006RECOMB 2006
Detecting microRNA targets
Permuted miRNA data miRNA data
04/02/2006RECOMB 2006
Detecting microRNA targets
LESSONS:
1) We CAN learn from expression and sequence data!
2) Combinatorics are critical for learning targets!
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/
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