Dataset Information

PmiREN: a comprehensive encyclopedia of plant miRNAs.

ABSTRACT: MicroRNAs (miRNAs) are small non-coding RNA molecules that function as diverse endogenous gene regulators at the post-transcriptional level. In the past two decades, as research effort on miRNA identification, function and evolution has soared, so has the demand for miRNA databases. However, the current plant miRNA databases suffer from several typical drawbacks, including a lack of entries for many important species, uneven annotation standards across different species, abundant questionable entries, and limited annotation. To address these issues, we developed a knowledge-based database called Plant miRNA Encyclopedia (PmiREN, http://www.pmiren.com/), which was based on uniform processing of sequenced small RNA libraries using miRDeep-P2, followed by manual curation using newly updated plant miRNA identification criteria, and comprehensive annotation. PmiREN currently contains 16,422 high confidence novel miRNA loci in 88 plant species and 3,966 retrieved from miRBase. For every miRNA entry, information on precursor sequence, precursor secondary structure, expression pattern, clusters and synteny in the genome, potential targets supported by Parallel Analysis of RNA Ends (PARE) sequencing, and references is attached whenever possible. PmiREN is hierarchically accessible and has eight built-in search engines. We believe PmiREN is useful for plant miRNA cataloguing and data mining, therefore a resource for data-driven miRNA research in plants.

SUBMITTER: Guo Z

PROVIDER: S-EPMC6943064 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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PmiREN: a comprehensive encyclopedia of plant miRNAs.

Guo Zhonglong Z Kuang Zheng Z Wang Ying Y Zhao Yongxin Y Tao Yihan Y Cheng Chen C Yang Jing J Lu Xiayang X Hao Chen C Wang Tianxin T Cao Xiaoyan X Wei Jianhua J Li Lei L Yang Xiaozeng X

Nucleic acids research 20200101 D1

MicroRNAs (miRNAs) are small non-coding RNA molecules that function as diverse endogenous gene regulators at the post-transcriptional level. In the past two decades, as research effort on miRNA identification, function and evolution has soared, so has the demand for miRNA databases. However, the current plant miRNA databases suffer from several typical drawbacks, including a lack of entries for many important species, uneven annotation standards across different species, abundant questionable en ...[more]

PMID: 31602478

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Project description:BackgroundMicroRNAs (miRNAs) are a set of short (19∼24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates.Methodology/principal findingsA miRNA:miRNA* duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA* duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA* duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with animal data to predict animal miRNAs. The model also achieves higher prediction performance. It further confirms the efficiency of our miRNA prediction method.ConclusionsThe superior performance of the proposed prediction model can be attributed to the extracted features of plant miRNAs and miRNA*s, the selected training dataset, and the carefully selected features. The web service of MaturePred, the training datasets, the testing datasets, and the selected features are freely available at http://nclab.hit.edu.cn/maturepred/.

Dataset Information

PmiREN: a comprehensive encyclopedia of plant miRNAs.

Publications

PmiREN: a comprehensive encyclopedia of plant miRNAs.

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