miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences

Cui, Haibo; Zhai, Jingjing; Ma, Chuang

doi:10.1371/journal.pone.0142753

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…19 Although the latest encouraging research has reported that up-regulated ZFAS1 acts as a promoter for the occurrence of NPC through the activation of the Wnt/β-catenin pathway, the role of ZFAS1 in NPC requires further investigation. [21][22][23] Multiple miRNAs including miR-1, miR-let-7 and miR-138 participate in the tumorigenesis, progression and metastasis of NPC through biological functions, such as inducing apoptosis and inhibiting proliferation. [21][22][23] Multiple miRNAs including miR-1, miR-let-7 and miR-138 participate in the tumorigenesis, progression and metastasis of NPC through biological functions, such as inducing apoptosis and inhibiting proliferation.…”

Section: Introductionmentioning

confidence: 99%

Long noncoding RNA ZFAS1 promotes tumorigenesis and metastasis in nasopharyngeal carcinoma by sponging miR‐892b to up‐regulate LPAR1 expression

Peng

Liu

Zheng

et al. 2019

J Cellular Molecular Medi

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Objective: In this study, we explored the NPC-specific expression of ZFAS1 and the mechanism of ZFAS1-mediated growth, aggressiveness and tumorigenesis in NPC. Methods:The expression profile of lncRNAs was detected in NPC tissues and matching para-carcinoma tissues using microarray analysis. LncRNA-miRNA and miRNA-mRNA interaction networks were constructed using the miRcode v11 and TargetScanHuman v7.2 web server and then validated using dual-luciferase assay.Western blot and RT-qPCR were performed to detect protein and RNA expression.The effects of ZFAS1, miR-892b and LPAR1 dysregulation on the proliferative, migratory and invasive abilities of NPC cells were observed using colony formation, cell counting kit-8 (CCK-8) and transwell assays in vitro. In vivo, a xenograft nude mouse model was established to detect the impact of ZFAS1 dysregulation on the tumorigenicity of NPC cells. Results:The expression of multiple lncRNAs, of which ZFAS1 was up-regulated, was dysregulated in NPC tissues. ZFAS1 directly targeted miR-892b, and miR-892b negatively regulated the expression of downstream LPAR1. The proliferation, migration and invasion of NPC cells could be largely enhanced by the downregulation of miR-892b as well as the up-regulation of ZFAS1 and LPAR1, while the overexpression of miR-892b and the downregulation of ZFAS1 and LPAR1 decreased these abilities.In nude mice, the growth of tumour xenografts formed by HONE1 cells was significantly suppressed when ZFAS1 was silenced. Conclusion:The study demonstrated that lncRNA ZFAS1 may act as a promoter of tumorigenesis and metastasis in nasopharyngeal carcinoma, by up-regulating the expression of LPAR1 in a miR-892b-dependent manner. K E Y W O R D Slong noncoding RNAs, LPAR1, miR-892b, nasopharyngeal carcinoma, ZFAS1

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Section: Introductionmentioning

confidence: 99%

Long noncoding RNA ZFAS1 promotes tumorigenesis and metastasis in nasopharyngeal carcinoma by sponging miR‐892b to up‐regulate LPAR1 expression

Peng

Liu

Zheng

et al. 2019

J Cellular Molecular Medi

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“…There are several tools for in silico miRNA identification such as miRanalyzer, miR-PREFeR, miRTRAP, miRLocator, and MIReNA (Hendrix et al, 2010; Mathelier and Carbone, 2010; Hackenberg et al, 2011; Lei and Sun, 2014; Cui et al, 2015). Majority of these methods rely on the sequence information of previously validated miRNA and non-miRNA sequences such as genes (Friedländer et al, 2008; An et al, 2013), while others perform de novo prediction (Yousef et al, 2006; Liu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Prescription for Plant miRNA Identification

2017

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microRNAs (miRNAs) are tiny ribo-regulatory molecules involved in various essential pathways for persistence of cellular life, such as development, environmental adaptation, and stress response. In recent years, miRNAs have become a major focus in molecular biology because of their functional and diagnostic importance. This interest in miRNA research has resulted in the development of many specific software and pipelines for the identification of miRNAs and their specific targets, which is the key for the elucidation of miRNA-modulated gene expression. While the well-recognized importance of miRNAs in clinical research pushed the emergence of many useful computational identification approaches in animals, available software and pipelines are fewer for plants. Additionally, existing approaches suffers from mis-identification and annotation of plant miRNAs since the miRNA mining process for plants is highly prone to false-positives, particularly in cereals which have a highly repetitive genome. Our group developed a homology-based in silico miRNA identification approach for plants, which utilizes two Perl scripts “SUmirFind” and “SUmirFold” and since then, this method helped identify many miRNAs particularly from crop species such as Triticum or Aegliops. Herein, we describe a comprehensive updated guideline by the implementation of two new scripts, “SUmirPredictor” and “SUmirLocator,” and refinements to our previous method in order to identify genuine miRNAs with increased sensitivity in consideration of miRNA identification problems in plants. Recent updates enable our method to provide more reliable and precise results in an automated fashion in addition to solutions for elimination of most false-positive predictions, miRNA naming and miRNA mis-annotation. It also provides a comprehensive view to genome/transcriptome-wide location of miRNA precursors as well as their association with transposable elements. The “SUmirPredictor” and “SUmirLocator” scripts are freely available together with a reference high-confidence plant miRNA list.

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“…miRLocator is a software based on machine-based learning which correlate the sequence and structural patterns extracted from miRNA:miRNA* duplexes (Cui et al 2015a). The software is also compatible enough to distinguish between the few real miRNAs and the large number of pseudo miRNAs.…”

Section: Mirlocatormentioning

confidence: 99%

“…Leclercq et al (2013) reported the use of miRduplex (miRdup) (http://www.cs.mcgill.ca/*blanchem/mirdup/) as an efficient tool for the computational prediction of locating miRNAs within their pre-miRNAs. Ten-fold cross-validation of 5854 experimentally validated miRNAs across 19 plant species highlighted the fact that miRLocator is a better option in predicting mature miRNAs within plant pre-miRNAs than miRdup (Cui et al 2015a).…”

Section: Mirlocatormentioning

confidence: 99%

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Emerging techniques to decipher microRNAs (miRNAs) and their regulatory role in conferring abiotic stress tolerance of plants

Banerjee

Roychoudhury

Krishnamoorthi

2016

Plant Biotechnol Rep

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MicroRNAs (miRNAs) are a distinct class of non-coding, small regulatory RNAs which have evolved significantly in generating abiotic stress tolerance across a variety of model plants and crop species. These miRNAs, while undergoing post-transcriptional modifications, have often been found to be linked with epigenetic regulations of stress-responsive gene expression. The discovery of isomers of miRNAs (isomiRs) is also a remarkable event, as some schools of scientists believe them to be regulatory molecules distinct from the conventional miRNAs. The link between isomiRs and abiotic stress responses in plants is now a field of intense research. In this review, we have highlighted the mechanism of various tools and techniques which are essential to visualize high-throughput data analysis. Such data are required for generating large-scale libraries of small RNAs, from which stress-responsive miRNAs are conventionally screened. The concluding part of the review especially contains an exhaustive discussion on the recent developments of miRNA-mediated tolerance towards multiple stresses, such as nutrient deficiency, salinity, drought, oxidative stress, hypoxia, temperature stress, radiation, and heavy metal toxicity. Both transgenic as well as miRNAome approaches have been focussed in this section of the review.

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miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences

Cited by 26 publications

References 19 publications

Long noncoding RNA ZFAS1 promotes tumorigenesis and metastasis in nasopharyngeal carcinoma by sponging miR‐892b to up‐regulate LPAR1 expression

Long noncoding RNA ZFAS1 promotes tumorigenesis and metastasis in nasopharyngeal carcinoma by sponging miR‐892b to up‐regulate LPAR1 expression

A Comprehensive Prescription for Plant miRNA Identification

Emerging techniques to decipher microRNAs (miRNAs) and their regulatory role in conferring abiotic stress tolerance of plants

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