MiRNA target identification and prediction as a function of time in gene expression data. Issue 7 (2nd July 2020)

• Record Type: Journal Article
• Authors: Grigaitis, Pranas; Starkuviene, Vytaute; Rost, Ursula; Serva, Andrius; Pucholt, Pascal; Kummer, Ursula
• Is Part Of: RNA biology. Volume 17:Issue 7(2020)
• Page Start: 990
• Page End: 1000
• Publication Date: 2020-07-02
• Subjects: miRNA -- miRNA target identification -- miRNA target predictions -- bioinformatics -- miR-517a -- miR-17 -- miR-124 -- miR-135b; RNA -- Periodicals; Molecular biology -- Periodicals; Molecular biology; RNA; Periodicals; 572.8805
• DOI: 10.1080/15476286.2020.1748921
• Languages: English
• ISSNs: 1547-6286

Abstract

ABSTRACT: The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.