Abstract
BackgroundMicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets.ResultsThe gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in Arabidopsis, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening.ConclusionOur approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.
Highlights
MicroRNAs are small and noncoding RNAs that play important roles in various biological processes
Classification of miRNA targets using gene expression profiles Our prediction model classifies the targets by combining gene expression profiles and sequence information (Figure 1)
Before testing the prediction model, we first investigated whether gene expression profile information can be used to discriminate the target genes from non-target genes
Summary
MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. MicroRNAs (miRNAs) are small RNAs that play important regulatory roles in animals and plants [1] They cause transcriptional cleavage or translational repression through binding their target mRNAs. miRNAs affect a variety of cellular processes such as development, cell proliferation, apoptosis, and stress response [2,3,4]. Several miRNA target prediction tools have been developed [1,5,6,7,8,9,10] The majority of these algorithms are based on the sequence alignment or the minimum free energy of the hybridization. The sequence alignment or the binding energy of miRNA/mRNA pairs can sometimes (page number not for citation purposes)
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