2021
DOI: 10.1016/j.csbj.2021.01.029
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Integrative approaches for analysis of mRNA and microRNA high-throughput data

Abstract: Highlights Review on tools and databases linking miRNA and its mRNA targetome. Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects. Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions.

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Cited by 24 publications
(15 citation statements)
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“…Although miRNA biogenesis pathways have been well characterized, miRNA target identification remains a major challenge. Recent methodological developments in miRNA sequence analysis have significantly improved the accuracy of miRNA target prediction on the genome-wide scale ( Lewis et al 2005 ; Agarwal et al 2015 ; McGeary et al 2019 ; Nazarov and Kreis 2021 ). However, these predictions rely heavily on conserved base-pairing of the miRNA seed region, which can lead to a high error rate given the high frequency of 6-nt seed-matches in the transcriptome ( Pinzon et al 2017 ) and the existence of other functional nonseed interactions between miRNAs and their targets ( Broughton et al 2016 ; Zhang et al 2018 ; Chipman and Pasquinelli 2019 ; McGeary et al 2019 ).…”
Section: Introductionmentioning
confidence: 99%
“…Although miRNA biogenesis pathways have been well characterized, miRNA target identification remains a major challenge. Recent methodological developments in miRNA sequence analysis have significantly improved the accuracy of miRNA target prediction on the genome-wide scale ( Lewis et al 2005 ; Agarwal et al 2015 ; McGeary et al 2019 ; Nazarov and Kreis 2021 ). However, these predictions rely heavily on conserved base-pairing of the miRNA seed region, which can lead to a high error rate given the high frequency of 6-nt seed-matches in the transcriptome ( Pinzon et al 2017 ) and the existence of other functional nonseed interactions between miRNAs and their targets ( Broughton et al 2016 ; Zhang et al 2018 ; Chipman and Pasquinelli 2019 ; McGeary et al 2019 ).…”
Section: Introductionmentioning
confidence: 99%
“…The exploration of miRNAs generally requires the prediction of the seed sequences that bind to the 3′-UTR using various algorithms and de novo or machine learning tools, but various computational analyses could not identify enough binding ability or downstream biological roles of the predicted miRNAs in vitro or in vivo. Accordingly, we selected new potential miRNAs that can bind to the 3′-UTR of HCMV UL123 , which downregulates IE72 expression, using high-throughput sequencing data from AGO-CLIP-seq instead of traditional algorithms in silico, because cellular miRNAs interacting with UL123 -3′-UTR have not been evaluated despite the crucial role of IE72 during early infection [ 13 , 27 , 59 , 63 , 87 , 94 , 95 ]. However, sophisticated SC-based flow cytometry of GFP expression revealed that miRNAs with the most abundant transcripts in AGO-CLIP-seq from HCMV-infected cells did not bind to the 3′-UTR of UL123 .…”
Section: Discussionmentioning
confidence: 99%
“…It has been long established that miRNAs act cooperatively with other miRNAs and transcription factors that are themselves targeted by miRNAs, typically acting as hubs in regulatory networks. Over the past decade, fed by the increased availability of expression data and high-throughput miRNA target determination, important efforts have been made to develop in silico approaches that integrate this information to identify key regulatory networks ( 211 ). Nevertheless, most available networks are built based on gene-wide miRNA targeting and gene expression correlations, with very little input on regulation of miRNA gene expression.…”
Section: Molecular Mechanisms Underlying Nutrient-mediated Regulation Of Mirna Actionmentioning
confidence: 99%