ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets

Abstract

Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.

Biomarker MicroRNAs for Diagnosis, Prognosis and Treatment of Hepatocellular Carcinoma: A Functional Survey and Comparison

“…miR2Disease23, TarBase (version 6.0)24, miRTarBase (version 4.5)25, miRecords (version 4.0)26 and computationally predicted, i.e. HOCTAR (version 2.0)27, ExprTargetDB28, and starBase (version 2.0)29 miRNA-target databases. To reduce false positives, we mainly selected miRNA-mRNA pairs validated by low-throughput experiments, i.e.…”

Population differences in microRNA expression and biological implications

“…Measured signals were background corrected using normexp with offset value 10 based on a convolution model 51 and normalized using the global Lowess regression algorithm. 52 Of the interrogated miRNAs, 201 were consistently expressed in samples from both CEU and YRI populations (defined as having an expression intensity value on the arrays in at least 70% of all samples and having average Hy3 intensity value greater than 100). We identified miRNAs differentially expressed between the populations using a 2-tailed Student's t-test (unpaired with unequal variance).…”

miR-21 and miR-101 regulate PLAP-1 expression in periodontal ligament cells

“…Bioinformatic scans of the 3'UTR of PLAP-1 were conducted using four web-based miRNA target prediction programs: miRanda, PICTAR 4, PITA and TargetScan (22)(23)(24)(25)(26)(27)(28)(29)(30). miRNAs were selected based on their targeted prediction by the four programs, conservation of the binding region and the strength of the predicted interaction.…”