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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

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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

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• 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:

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• 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

…

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• 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

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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

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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

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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

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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

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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

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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

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A probabilistic model for microRNA regulation

Targeting probabilities

Probability of data given targeting interaction

Joint probability

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• 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

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Detecting microRNA targets

Permuted miRNA data miRNA data

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Detecting microRNA targets

LESSONS:

1) We CAN learn from expression and sequence data!

2) Combinatorics are critical for learning targets!

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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/

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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