PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs

Marsico, Annalisa; Huska, Matthew R.; Lasserre, Julia; Hu, Haiyang; Vučićević, Dubravka; Musahl, Anne; Ørom, Ulf Andersson; Vingron, Martin

doi:10.1186/gb-2013-14-8-r84

Cited by 113 publications

(152 citation statements)

References 51 publications

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“…A possible explanation, as proposed by Ozsolak et al (58) and Monteys et al (59), could be that a considerable percentage of intronic miRNAs [~35%, using miRBasev9 (58) and v12.0 (59)] are associated with Pol II or Pol III promoters that could drive transcription independently of their host gene promoter ( Figure 2E). More recently, Marsico et al (60), using their ProMIRNA tool (http:// promirna.molgen.mpg.de), detected up to 50% of intragenic miRNAs regulated by their own independent promoters (mirBase v 18.1). These authors also performed an analysis to identify the unique features of intronic miRNA promoters, such as miR-126 ( Figure 2E).…”

Section: Dna Methylationmentioning

confidence: 99%

Epigenetic regulation mechanisms of microRNA expression

Morales

Monzó

Navarro

2017

Biomolecular Concepts

121

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MicroRNAs (miRNAs) are single-stranded RNAs of 18-25 nucleotides that regulate gene expression at the post-transcriptional level. They are involved in many physiological and pathological processes, including cell proliferation, apoptosis, development and carcinogenesis. Because of the central role of miRNAs in the regulation of gene expression, their expression needs to be tightly controlled. Here, we summarize the different mechanisms of epigenetic regulation of miRNAs, with a particular focus on DNA methylation and histone modification.

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Section: Dna Methylationmentioning

confidence: 99%

Epigenetic regulation mechanisms of microRNA expression

Morales

Monzó

Navarro

2017

Biomolecular Concepts

121

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“…Here, we will use an example to explain how machine learning methods can be applied to answer the questions presented above; in particular, where is transcription most likely to begin for a miRNA or gene of interest, and which CREs are most likely to be contributing to transcription at these locations? Examples of machine learning studies that specifically address the search for miRNA primary transcript TSSs in animals include Zhou et al (2007), Megraw et al (2009), andMarsico et al (2013). Morton et al (2014) provide a machine-learning based plant study addressing miRNA TSSs as a subcategory of Pol II TSSs.…”

Section: Using Machine Learning To Identify Elements Predictive Of Trmentioning

confidence: 99%

Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants

et al. 2016

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ORCID IDs: 0000-0001-6793-6151 (M.M.); 0000-0002-0105-6431 (S.A.F.)RNA Polymerase II (Pol II) regulatory cascades involving transcription factors (TFs) and their targets orchestrate the genetic circuitry of every eukaryotic organism. In order to understand how these cascades function, they can be dissected into small genetic networks, each containing just a few Pol II transcribed genes, that generate specific signal-processing outcomes. Small RNA regulatory circuits involve direct regulation of a small RNA by a TF and/or direct regulation of a TF by a small RNA and have been shown to play unique roles in many organisms. Here, we will focus on small RNA regulatory circuits containing Pol II transcribed microRNAs (miRNAs). While the role of miRNA-containing regulatory circuits as modular building blocks for the function of complex networks has long been on the forefront of studies in the animal kingdom, plant studies are poised to take a lead role in this area because of their advantages in probing transcriptional and posttranscriptional control of Pol II genes. The relative simplicity of tissue-and cell-type organization, miRNA targeting, and genomic structure make the Arabidopsis thaliana plant model uniquely amenable for small RNA regulatory circuit studies in a multicellular organism. In this Review, we cover analysis, tools, and validation methods for probing the component interactions in miRNA-containing regulatory circuits. We then review the important roles that plant miRNAs are playing in these circuits and summarize methods for the identification of small genetic circuits that strongly influence plant function. We conclude by noting areas of opportunity where new plant studies are imminently needed.

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“…From the existing miRNA promoter recognition techniques, only the algorithms introduced by Marson et al 12 , PROmiRNA 14 and S-Peaker support predictions in mouse genome. Since source codes for miRStart and Marson et al 12 algorithms are not available, we have utilized their precompiled predictions.…”

Section: Resultsmentioning

confidence: 99%