Author: Jim C. Huang etc.Lecturer: Dong YueDirector: Dr. Yufei Huang

OutlineThe background of miRNA. The biology modelA Bayesian model for mRNA regulationAlgorithm evaluationValidating GenMiR++-predicted let-7b

targets

What is miRNA?MicroRNAs (miRNA) are single-stranded

RNA molecules of about 21-23 nucleotides in length thought to regulate the expression of other genes.

The Process of miRNA

A movie to explain miRNA.http://www.rosettagenomics.com/?CategoryI

D=174

Biology Problem

Biologist can not do experiment for all mRNAs and miRNAs .

Some many targets been predicted by TargetScan which is a very popular miRNA target algorithm.

TargetScan use sequence level data, can we use so other kind of data?

Microarray data Red(solid) curve means the expression of mRNA which was repressed by miRNA in specific tissue.Blue(dashed) curve means the background distribution which is the normal expression mRNA.

Thinking the Model biologically for only one target

Looking at the miRNA target which is predicted by TargetScan. What is TargetScan.

2 If this miRNA is highly expressed in a given tissue?

3. Whether the expression of a targeted transcript is negatively shifted with respect to a background expression level.

If 2,3 is Yes, it is very likely a target in reality.

In situation of Multiple miRNAs

The down-regulation of target mRNAs can subject to the action of multiple miRNAs.

miRNA scores are given according to how much the miRNA expression profile contributed to explaining downregulation of the mRNA expression.

Process in general

Definition of Bayesian Model

X and Z are the sets of expression profiles for mRNAs and miRNAs.C is the set of candidate miRNA targets.

is a positive tissue scaling parameter which accounts for differences in hybridization conditions and normalization between the miRNA and mRNA expression data.

Prior distribution

The goal of Bayesian Model Find the posterior probability:

Graphical model

A Bayesian model for mRNA regulation

Why we need an approximate method

Require integrating over the parameters

Sum over an exponential number of combinations of miRNA interactions per mRNA.

Variational Bayesian Learning of miRNA targets

•Observed variables v: X, Z, and C •Unobserved variables u: S•Model parameters η:Λ Γ•The exact posterior is: •The sorrogate distribution is:

KL-divergence:

Variational Bayesian Learning of miRNA targets

Define: represents the

probability that miRNA k targets mRNA g given the data.

represents the expected values of the regulatory weights.

represent the means and variances of the tissue scaling parameters.

Variational Bayesian Learning of miRNA targets

How to calculate the score

Algorithm evaluation

What is fraction of targets detected?# of candidate interactions detected/ # of candidate interactions.

Validating GenMiR++-predicted let-7b targets

Predict target for let-7b misregulation in retinoblastoma.No neural tissue was represented in the expression data used to build GenMiR++.

Validating GenMiR++-predicted let-7b targets

Use microarrays to profile 3 retinoblastoma samples and 1 healthy samples.Let-7b was on average ~50-fold lower in abundance in retinoblastoma verss healthy retina.

Validating GenMiR++-predicted let-7b targets

Reference[1] Jim C. Huang etc, Bayesian Inference of MicroRNA Targets from Sequence and Expression Data. J. Comput. Biol. 14, 550–563 (2007). [2]Jim C. Huang etc, Using Expression Profiling Data to Identify Human microRNA Targets, nature methods, VOL.4 NO.12, DEC 2007, p1045

SummarizationThe background of miRNA. The biology modelA Bayesian model for mRNA regulationAlgorithm evaluationValidating GenMiR++-predicted let-7b

targets.

Questions Or Comments?