Improving miRNA Target Genes Prediction
Rikky Wenang Purbojati
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.
miRNA Functions miRNA plays a major role in RNA Induced
Silencing Complex (RISC). miRNAs control the expression of large
numbers of genes by: mRNA degradation Translational repression
Recent 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
RNA Induced Silencing Complex mRNA degradation
Breaks the structural integrity of a mRNA. Translational repression
Prevent the mRNA from being translated.
Characteristics of miRNA Short (22-25nts) Transcripted from a miRNA gene
Intragenic: 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
Development of miRNA
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?
miRNA sequence target prediction Predict target genes by matching the
complement of miRNA sequence. Two types of complement:
Perfect complement
Imperfect complement
Find perfect match for seed (2-8nt)
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 species
Another approach: thermodynamic rule Local miRNA-mRNA interaction with positive
balance of minimum free energy
Problems and Opportunities Problem:
Pure computational target genes prediction produces a lot of candidates No unifying theory for target gene prediction yet Most of them are not validated yet Common assumption is that most of them are
false positives Can we shorten the list to include only the strong
candidates ?
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.
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
gene Intergenic 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
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 data Using a more diverse data
We expect we will get a much better specifity than the previous method
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 genes Include target genes which have correlation
below some threshold (-) Only works for intragenic miRNA
Hoctar Method
Shortcomings of Hoctar
Uses all probes data even though they are not consistent
Uses only one target gene prediction algorithm approach
Depends on Pearson Correlation, which is sensitive to outliers
Improvement Idea (1) Use only subset of data which probes are all
consistent Treat each probes as different experiments
Improvement Idea (2)
Pearson correlation is very sensitive to outliers, alternative solutions: Uses Rank correlation coefficients instead of
Pearson correlation coefficients Normalize the dataset to normal distribution Ignore outliers
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
data One miRNA transcript can only degrade one mRNA transcript Thus miRNA expression changes should not be much
different from mRNA expression changes
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
Preliminary Result GSE9234 dataset (hipoxia/normoxia) Using only consistency criteria
miRNA Host Gene Known Target Gene
HOCTAR Refined
miR-103-2 PANK3 GPD1 YES YESmiR-103-2 PANK3 FBW1B NO YESmiR-140 WWP2 HDAC4 YES YESmiR-224 GABRE API5 NO NO
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
Intergenic miRNA proxy measurement Putative target gene approximation
use the expression level of a known target genes for that specific intergenic miRNA
If 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
Further Work Implementation and evaluation Standardizing composite dataset repository