Detection of features predictive of microRNA targets by integration of network data

Abstract

Gene activity is controlled by multiple molecular mechanisms, for instance through transcription factors or by microRNAs (miRNAs), among others. Established bioinformatics tools for the prediction of miRNA target genes face the challenge of ensuring accuracy, due to high false positive rates. Further, these tools present poor overlap. However, we demonstrated that it is possible to filter good predictions of miRNA targets from the bulk of all predictions by using information from the gene regulatory network. Here, we take advantage of this strategy that selects a large subset of predicted microRNA binding sites as more likely to possess less false-positives because of their over-representation in RE1 silencing transcription factor (REST)-regulated genes from the background of TargetScanHuman 7.2 predictions to identify useful features for the prediction of microRNA targets. These enriched miRNA families would have silencing activity for neural transcripts overlapping the repressive activity on neural genes of REST. We analyze properties of associated microRNA binding sites and contrast the outcome to the background. We found that the selected subset presents significant differences respect to the background: (i) lower GC-content in the vicinity of the predicted miRNA binding site, (ii) more target genes with multiple identical microRNA binding sites and (iii) a higher density of predicted microRNA binding sites close to the 3’ terminal end of the 3’-UTR. These results suggest that network selection of miRNA-mRNA pairs could provide useful features to improve microRNA target prediction.

Genome wide comprehensive characterization and expression analysis of RBOH genes revealed drought stress response in mung bean (Vigna radiate L.)

“…Thus, the presence and distribution of various CREs in VraRBOH gene promoters suggested that the VraRBOH genes might have involved in regulation of various biological activities including drought stress response in mung bean. The miRNA databases provide the insights into exploration of the plant genome for identifying miRNA targets and regulation of miRNA based gene regulations (Guo et al 2019;Yasin et al 2020;Cihan et al 2022). We identi ed 16 miRNAs targeting ve VraRBOH genes in the present study.…”

Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multiomics Research – A Thorough Review on the Role of ChIP-Seq Data

“…integrates information from 3 of 16 multiple prediction algorithms to get accurate results but it often worsens the inference [33]. This necessitates experimental validation to check the interaction between miRNA and its target mRNA directly [24] or computational methods that would learn from the true positive results of the existing large databases [34]. However, recent databases such as mirDIP 4.1 [35] claims to provide more accurate miRNA-target predictions by combining predictions from 30 independent resources using integrative score.…”