PncStress: a manually curated database of experimentally validated stress-responsive non-coding RNAs in plants

Wu, Weite; Wu, Yan; Hu, Dahui; Zhou, Yincong; Hu, Yanshi; Chen, Yujie; Chen, Ming

doi:10.1093/database/baaa001

Cited by 25 publications

(18 citation statements)

References 45 publications

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“…We collected 1428 abiotic stress-responsive miRNA sequences from the PncStress database [59], which is accessible at http://bis.zju.edu.cn/pncstress/, accessed on 28 December 2021. This database contains 4227 experimentally validated stress-responsive non-coding RNAs (miRNA, LncRNA, and circRNA) from 114 plants, covering 48 biotic and 91 abiotic stresses.…”

Section: Retrieval and Processing Of Sequence Datamentioning

confidence: 99%

“…In addition, the NGS and deep sequencing technologies have also led to the identification of a large number of abiotic stress-responsive miRNAs [52]. All the miRNAs identified through wet lab experiments and sequencing methods have been populated in the form of databases such as miRBase [53], miRNEST [54], PMRD [55], miRPlant [56], PlantMirnaT [57], PAS-miR [58], PncStress [59], NtUE-Webresource [41], and others [50]. The PncStress database is the most updated, and it contains the experimentally validated miRNA sequences associated with different environmental stresses.…”

Section: Introductionmentioning

confidence: 99%

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ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

Meher

Begam

Sahu

et al. 2022

IJMS

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MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.

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Section: Retrieval and Processing Of Sequence Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

Meher

Begam

Sahu

et al. 2022

IJMS

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“…Stress-responsive ncRNAs have been identified in various plant species. Recently, a database curating experimentally validated stress-responsive ncRNAs in plants was constructed ( Wu et al, 2020 ). The database contains 4,227 entries of ncRNAs, including miNRAs, lncRNAs, and circular RNAs, covering 48 biotic and 91 abiotic stress conditions ( Wu et al, 2020 ).…”

Section: Non-coding Rnas Are Regulators Of Stress Responses In Soybeanmentioning

confidence: 99%

Using the Knowledge of Post-transcriptional Regulations to Guide Gene Selections for Molecular Breeding in Soybean

Cheung

Cheng

et al. 2022

Front. Plant Sci.

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The omics approaches allow the scientific community to successfully identify genomic regions associated with traits of interest for marker-assisted breeding. Agronomic traits such as seed color, yield, growth habit, and stress tolerance have been the targets for soybean molecular breeding. Genes governing these traits often undergo post-transcriptional modifications, which should be taken into consideration when choosing elite genes for molecular breeding. Post-transcriptional regulations of genes include transcript regulations, protein modifications, and even the regulation of the translational machinery. Transcript regulations involve elements such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) for the maintenance of transcript stability or regulation of translation efficiency. Protein modifications involve molecular modifications of target proteins and the alterations of their interacting partners. Regulations of the translational machinery include those on translation factors and the ribosomal protein complex. Post-transcriptional regulations usually involve a set of genes instead of a single gene. Such a property may facilitate molecular breeding. In this review, we will discuss the post-transcriptional modifications of genes related to favorable agronomic traits such as stress tolerance, growth, and nutrient uptake, using examples from soybean as well as other crops. The examples from other crops may guide the selection of genes for marker-assisted breeding in soybean.

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“…Many plant ncRNAs are expressed in response to abiotic stresses including drought, temperature, salinity, and nutrient deprivation [ 27 , 28 , 30 , 78 ]. Recently, a database of experimentally validated stress-responsive ncRNAs, named PncStress, has been established, which contains 4227 entries including lncRNAs and miRNAs from 114 plant species and covers 91 abiotic stresses and 48 biotic stresses [ 79 ].…”

Section: Classification and Functions Of Plant Ncrnasmentioning

confidence: 99%

Roles of Non-Coding RNAs in Response to Nitrogen Availability in Plants

Fukuda

Fujiwara

Nishida

2020

IJMS

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Nitrogen (N) is an essential nutrient for plant growth and development; therefore, N deficiency is a major limiting factor in crop production. Plants have evolved mechanisms to cope with N deficiency, and the role of protein-coding genes in these mechanisms has been well studied. In the last decades, regulatory non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), small interfering RNAs (siRNAs), and long ncRNAs (lncRNAs), have emerged as important regulators of gene expression in diverse biological processes. Recent advances in technologies for transcriptome analysis have enabled identification of N-responsive ncRNAs on a genome-wide scale. Characterization of these ncRNAs is expected to improve our understanding of the gene regulatory mechanisms of N response. In this review, we highlight recent progress in identification and characterization of N-responsive ncRNAs in Arabidopsis thaliana and several other plant species including maize, rice, and Populus.

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PncStress: a manually curated database of experimentally validated stress-responsive non-coding RNAs in plants

Cited by 25 publications

References 45 publications

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

Using the Knowledge of Post-transcriptional Regulations to Guide Gene Selections for Molecular Breeding in Soybean

Roles of Non-Coding RNAs in Response to Nitrogen Availability in Plants

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