Improving miRNA Target Genes Prediction - PowerPoint Presentation

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Improving miRNA Target Genes Prediction

Rikky

Wenang

Purbojati

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miRNA

MicroRNA

(miRNA) is a class of RNA which is

believed to play important roles in gene regulation.It’s a short (21- to 23-nt) RNAs that bind to the 3′ untranslated regions (3′ UTRs) of target genes.

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miRNA Functions

miRNA plays a major role in RNA Induced Silencing Complex (RISC).

miRNAs

control the expression of large numbers of genes by:mRNA degradationTranslational repressionRecent studies indicates it plays a role in cancer development:

Surplus of miRNA might inhibit cell apoptosis process

Deficit of miRNA might cause excess of certain

oncogenes

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RNA Induced Silencing Complex

mRNA degradation

Breaks the structural integrity of a mRNA.

Translational repressionPrevent the mRNA from being translated.

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Characteristics of miRNA

Short (22-25nts)

Transcripted

from a miRNA geneIntragenic: miRNA gene is located inside a host gene (usually intron region)Intergenic

: miRNA gene is located outside gene bodies

A consistent 5’ and 3’ boundary:

Transcription Start Site

5’ Cap

Poly(A) tail

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Development of miRNA

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miRNA General Research Question

Much attention has been directed in miRNA processing and targeting.

Computational-wise, one basic challenge of miRNA:

Given a miRNA sequence, what are its target genes?

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miRNA sequence target prediction

Predict target genes by matching the complement of miRNA sequence.

Two types of complement:

Perfect complementImperfect complement

Find perfect match for

seed (2-8nt)

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miRNA sequence target prediction

Several requirements for matching:

Strong Watson-Crick base pairing of the 5’ seed (2-8

nts)Conservation of the miRNA binding site across speciesAnother approach: thermodynamic rule

Local miRNA-mRNA interaction with positive balance of minimum free energy

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Problems and Opportunities

Problem:

Pure computational target genes prediction produces a lot of candidates

No unifying theory for target gene prediction yetMost of them are not validated yetCommon assumption is that most of them are false positives

Can

we shorten the list to include only the strong candidates ?

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Problems and Opportunities

Opportunity:

Lots of publicly available experimental dataset i.e.

cDNA microarray, miRNA microarray, etc. Use the dataset to computationally validate some of the target genes

Current Research:

Preliminary research tries to utilizes the abundance of publicly available microarray data.

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Assumptions

miRNA works by silencing target genes, thus miRNA gene and target genes should be anti-correlated

Intragenic

miRNA are expressed along with the host gene.a host gene should be anti-correlated with a target geneIntergenic miRNA does not have a host gene, but we might be able to use available composite (miRNA microarray +

cDNA

microarray) dataset

If a miRNA is up-regulated in miRNA microarray, then its target genes should be down-regulated in

cDNA

microarray

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Current Work

There have been some works related to this idea (i.e.

HOCTAR)

However, we can improve it by:Using a stricter criteria across the microarray dataUsing a more diverse dataWe expect we will get a much better specifity

than the previous method

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Hoctar Method

Get a list of target genes from 3 different tools (

pictar

, TargetScan,miranda)Uses Pearson correlation to determine the correlation coefficient between 2 genesInclude target genes which have correlation below some threshold

(-)

Only works for intragenic miRNA

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Hoctar Method

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Shortcomings of Hoctar

Uses all probes data even though they are not consistent

Uses

only one target gene prediction algorithm approachDepends on Pearson Correlation, which is sensitive to outliers

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Improvement Idea (1)

Use only subset of data which probes are all

consistent

Treat each probes as different experiments

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Improvement Idea (2)

Pearson correlation is very sensitive to outliers, alternative solutions:

Uses

Rank correlation coefficients instead of Pearson correlation coefficientsNormalize the dataset to normal distributionIgnore outliers

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Improvement Idea (3)

In addition to probes consistency and rank correlation, we might use entropy rule in eliminating candidate target genes

Assumption:

Transcript level can be approximated from expression level dataOne miRNA transcript can only degrade one mRNA transcript

Thus miRNA expression changes should not be much different from mRNA expression changes

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Improvement Idea (4)

Uses a larger amount of microarray data

We might be able to include miRNA microarray to further refine target genes list for several miRNA

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Preliminary Result

GSE9234 dataset (

hipoxia

/normoxia)Using only consistency criteria

miRNA

Host Gene

Known

Target Gene

HOCTAR

Refined

miR-103-2

PANK3

GPD1

YES

YES

miR-103-2

PANK3

FBW1B

NO

YES

miR-140

WWP2

HDAC4

YES

YES

miR-224

GABRE

API5

NO

NO

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Refining Intergenic miRNA prediction

Refining intergenic miRNA prediction using microarray dataset is not a trivial task

Microarray can only be used to measure the expression of target genes, but not the miRNA gene

Might have to rely on additional data: Proxy measurement miRNA microarray

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Intergenic miRNA proxy measurement

Putative target gene

approximation

use the expression level of a known target genes for that specific intergenic miRNAIf its target genes are consistently down-regulated, then we can assume that the expression level of the intergenic miRNA gene is

up-regulated

Cluster miRNA

approximation

Some intergenic

miRNAs

are clustered with each other; according to (

Saini

et al. 2007) most of these clusters use the same

pri-mirNA

transcript

Use method 1 for neighboring miRNA to get the intergenic miRNA expression approximation

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Further Work

Implementation and evaluation

Standardizing

composite dataset repository