Plant miRNA Conservation and Evolution

Abstract Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.

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PmiRExAt: plant miRNA expression atlas database and web applications

Database ◽

10.1093/database/baw060 ◽

2016 ◽

Vol 2016 ◽

pp. baw060 ◽

Cited By ~ 14

Author(s):

Anoop Kishor Singh Gurjar ◽

Abhijeet Singh Panwar ◽

Rajinder Gupta ◽

Shrikant S. Mantri

Keyword(s):

Mirna Expression ◽

Web Applications ◽

Plant Mirna ◽

Expression Atlas

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Cross-Kingdom Regulation by Plant microRNAs Provides Novel Insight into Gene Regulation

Advances in Nutrition ◽

10.1093/advances/nmaa095 ◽

2020 ◽

Author(s):

Abdul Fatah A Samad ◽

Mohd Farizal Kamaroddin ◽

Muhammad Sajad

Keyword(s):

Gene Expression ◽

Target Genes ◽

Current Knowledge ◽

Circulatory System ◽

Dietary Therapy ◽

Transcriptional Level ◽

Plant Mirnas ◽

Health Issues ◽

Plant Mirna ◽

Potential Applications

ABSTRACT microRNAs (miRNAs) are well known as major players in mammalian and plant genetic systems that act by regulating gene expression at the post-transcriptional level. These tiny molecules can regulate target genes (mRNAs) through either cleavage or translational inhibition. Recently, the discovery of plant-derived miRNAs showing cross-kingdom abilities to regulate mammalian gene expression has prompted exciting discussions among researchers. After being acquired orally through the diet, plant miRNAs can survive in the digestive tract, enter the circulatory system, and regulate endogenous mRNAs. Here, we review current knowledge regarding the cross-kingdom mechanisms of plant miRNAs, related controversies, and potential applications of these miRNAs in dietary therapy, which will provide new insights for plant miRNA investigations related to health issues in humans.

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PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA–lncRNA interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa074 ◽

2020 ◽

Vol 36 (10) ◽

pp. 2986-2992 ◽

Cited By ~ 3

Author(s):

Qiang Kang ◽

Jun Meng ◽

Jun Cui ◽

Yushi Luan ◽

Ming Chen

Keyword(s):

Biological Activities ◽

Learning Model ◽

Supplementary Information ◽

Fuzzy Decision ◽

Interaction Prediction ◽

Machine Learning Model ◽

Plant Mirna ◽

Mechanism Of Interaction ◽

Polymerase Chain ◽

Deep Learning Model

Abstract Motivation The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA–lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant. Results This article proposes a new method for fulfilling plant miRNA–lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA–lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time–polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness. Availability and implementation The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/. Supplementary information Supplementary data are available at Bioinformatics online.

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Predict plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models

10.1101/345249 ◽

2018 ◽

Cited By ~ 1

Author(s):

Qi Zhao ◽

Qian Mao ◽

Zheng Zhao ◽

Tongyi Dou ◽

Zhiguo Wang ◽

...

Keyword(s):

Neural Network ◽

Random Forest ◽

Convolutional Neural Network ◽

Human Body ◽

Expression Profiles ◽

Mirna Sequence ◽

Selective Absorption ◽

Sequence Specificity ◽

One Dimensional ◽

Plant Mirna

AbstractBackgroundAn increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals.ResultsHere, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed.ConclusionsOur study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

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