Dataset Information

Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in Arabidopsis thaliana.

ABSTRACT: Along with the canonical miRNA, distinct miRNA-like sequences called sibling miRNAs (sib-miRs) are generated from the same pre-miRNA. Among them, isomeric sequences featuring slight variations at the terminals, relative to the canonical miRNA, constitute a pool of isomeric sibling miRNAs (isomiRs). Despite the high prevalence of isomiRs in eukaryotes, their features and relevance remain elusive. In this study, we performed a comprehensive analysis of mature precursor miRNA (pre-miRNA) sequences from Arabidopsis to understand their features and regulatory targets. The influence of isomiR terminal heterogeneity in target binding was examined comprehensively. Our comprehensive analyses suggested a novel computational strategy that utilizes miRNA and its isomiRs to enhance the accuracy of their regulatory target prediction in Arabidopsis. A few targets are shared by several members of isomiRs; however, this phenomenon was not typical. Gene Ontology (GO) enrichment analysis showed that commonly targeted mRNAs were enriched for certain GO terms. Moreover, comparison of these commonly targeted genes with validated targets from published data demonstrated that the validated targets are bound by most isomiRs and not only the canonical miRNA. Furthermore, the biological role of isomiRs in target cleavage was supported by degradome data. Incorporating this finding, we predicted potential target genes of several miRNAs and confirmed them by experimental assays. This study proposes a novel strategy to improve the accuracy of predicting miRNA targets through combined use of miRNA with its isomiRs.

SUBMITTER: Ahmed F

PROVIDER: S-EPMC4615835 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in Arabidopsis thaliana.

Ahmed Firoz F Senthil-Kumar Muthappa M Lee Seonghee S Dai Xinbin X Mysore Kirankumar S KS Zhao Patrick Xuechun PX

RNA biology 20140101 11

Along with the canonical miRNA, distinct miRNA-like sequences called sibling miRNAs (sib-miRs) are generated from the same pre-miRNA. Among them, isomeric sequences featuring slight variations at the terminals, relative to the canonical miRNA, constitute a pool of isomeric sibling miRNAs (isomiRs). Despite the high prevalence of isomiRs in eukaryotes, their features and relevance remain elusive. In this study, we performed a comprehensive analysis of mature precursor miRNA (pre-miRNA) sequences ...[more]

PMID: 25629686

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Project description:BACKGROUND:Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are developed in various small datasets, which each represent a limited sample space. Thus, the prediction accuracy of these tools has not been systematically validated at a larger scale. Accordingly, comparing the prediction accuracy of these tools and determining their applicability become challenging. In addition, the accuracy of these tools, especially in large datasets, needs to be improved for broader applications. RESULTS:In this project, a large dataset containing more than 46,600 miRNA:mRNA interactions was assembled and split into eleven subsets based on the availability of prediction scores of four individual predictors, which are miRanda, miRDB, PITA, and TargetScan. In each of these subsets, the predictive results of four individual predictors were integrated using decision-tree based artificial neural networks to make the meta-prediction. The decision-tree is used here to sort the predictive results of four individual predictors, and artificial neural networks are applied to make meta-prediction based on the outputs of individual predictors. In the decision tree, dual-threshold and two-step significance-voting were incorporated, information gain was analysed to select threshold values. The prediction performance of this new strategy was improved significantly in most of the eleven datasets comparing to the individual predictors and other meta-predictors, such as ComiR, under multi-fold cross-validation, as well as in independent datasets. The overall improvement of prediction accuracy in independent datasets is at least 9 percentile points comparing to the other predictors, and the percentage of improvement of F1 and MCC scores is at least 40% compared to the other predictors. CONCLUSIONS:The combination of dual-threshold, two-step significance-voting, and analysis of information gain is very effective in optimizing the outcome of decision-tree, and further integration with artificial neural networks is critical for further improving the performance of meta-predictor. A new pipeline based on this integration for miRNA target prediction has been developed. A strategy using outputs of individual predictors to reorganize large-scale miRNA:mRNA interaction dataset has also been validated and used to evaluate the prediction accuracy of predictors. The predictor is available at: https://github.com/xueLab/mirTarDANN ).

Project description:The application of double-stranded RNAs (dsRNAs) to plant surfaces has emerged as a promising tool for manipulating gene expression in plants and pathogens, offering new opportunities for crop improvement. While research has shown the capability of exogenous dsRNAs to silence genes, the full spectrum of their impact, particularly on the intricate network of microRNAs (miRNAs), remains largely unexplored. Here, we show that the exogenous application of chalcone synthase (CHS)-encoding dsRNA to the rosette leaves of Arabidopsis thaliana induced extensive alterations in the miRNA profile, while non-specific bacterial neomycin phosphotransferase II (NPTII) dsRNA had a minimal effect. Two days after treatment, we detected 60 differentially expressed miRNAs among the 428 miRNAs found in the A. thaliana genome. A total of 59 miRNAs were significantly changed after AtCHS-dsRNA treatment compared with water and NPTII-dsRNA, and 1 miRNA was significantly changed after AtCHS-dsRNA and NPTII-dsRNA compared with the water control. A comprehensive functional enrichment analysis revealed 17 major GO categories enriched among the genes potentially targeted by the up- and downregulated miRNAs. These categories included processes such as aromatic compound biosynthesis (a pathway directly related to CHS activity), heterocycle biosynthesis, RNA metabolism and biosynthesis, DNA transcription, and plant development. Several predicted targets of upregulated and downregulated miRNAs, including APETALA2, SCL27, SOD1, GRF1, AGO2, PHB, and PHV, were verified by qRT-PCR. The analysis showed a negative correlation between the expression of miRNAs and the expression of their predicted targets. Thus, exogenous plant gene-specific dsRNAs induce substantial changes in the plant miRNA composition, ultimately affecting the expression of a wide range of genes. These findings have profound implications for our understanding of the effects of exogenously induced RNA interference, which can have broader effects beyond targeted mRNA degradation, affecting the expression of other genes through miRNA regulation.

Dataset Information

Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in Arabidopsis thaliana.

Publications

Comprehensive analysis of small RNA-seq data reveals that combination of miRNA with its isomiRs increase the accuracy of target prediction in Arabidopsis thaliana.

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