Predicting the target genes of miRNAs in preterm via targetscore algorithm

Abstract

Compared with normal neonates, preterm infants have an immature immune system which causes them to have a higher morbidity rate and even death. In order to reduce the mortality of newborns, we need to find the target genes which affect the preterm and understand their mechanism. It has been verified that microRNA (miRNA)-200 and miRNA-182 are closely related to the incidence of preterm. Therefore, it is significant to predict the target genes which are regulated by them for further understanding the mechanism of preterm. We chose the targetscore method for calculating the variational Bayesian-Gaussian mixture model (VB-GMM) as the target genes prediction method. It is designed for condition-specific target predictions and not limited to predict conserved genes, so the results are more accurate than previous sequence-based target prediction algorithms. In this study, our major contribution is to predict the target mRNAs of the chosen miRNAs with the gene expression profiles and a new method, which can effectively improve the accuracy of the prediction.

Keywords: bioinformatics; microRNA; preterm; targetscore; variational Bayesian-Gaussian mixture model.

Figure 1.

The |logFC| of all genes. Each black spot represents a gene in the volcano plot of the logFC. The result in the chart is the logFC value of the gene obtained by the limma package. Compared with the normal placenta, there are 9,783 upregulated genes and 10,731 downregulated genes in preterm placenta.

Figure 2.

The prediction of target genes use targetscore algorithm and the results are preliminary screening. The predicted target genes of (A) miRNA-200, and (B) miRNA-182. The values between the miRNAs and the predicted genes are the targetscore and the yellow-labeled genes are the validated target genes. miRNA, microRNA.