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

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. Furthe...

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Autores principales: Cihan, Mert, Andrade-Navarro, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182691/
https://www.ncbi.nlm.nih.gov/pubmed/35679295
http://dx.doi.org/10.1371/journal.pone.0269731
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author Cihan, Mert
Andrade-Navarro, Miguel A.
author_facet Cihan, Mert
Andrade-Navarro, Miguel A.
author_sort Cihan, Mert
collection PubMed
description 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.
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spelling pubmed-91826912022-06-10 Detection of features predictive of microRNA targets by integration of network data Cihan, Mert Andrade-Navarro, Miguel A. PLoS One Research Article 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. Public Library of Science 2022-06-09 /pmc/articles/PMC9182691/ /pubmed/35679295 http://dx.doi.org/10.1371/journal.pone.0269731 Text en © 2022 Cihan, Andrade-Navarro https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cihan, Mert
Andrade-Navarro, Miguel A.
Detection of features predictive of microRNA targets by integration of network data
title Detection of features predictive of microRNA targets by integration of network data
title_full Detection of features predictive of microRNA targets by integration of network data
title_fullStr Detection of features predictive of microRNA targets by integration of network data
title_full_unstemmed Detection of features predictive of microRNA targets by integration of network data
title_short Detection of features predictive of microRNA targets by integration of network data
title_sort detection of features predictive of microrna targets by integration of network data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182691/
https://www.ncbi.nlm.nih.gov/pubmed/35679295
http://dx.doi.org/10.1371/journal.pone.0269731
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