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Prediction of guide strand of microRNAs from its sequence and secondary structure

BACKGROUND: MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene ex...

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Autores principales: Ahmed, Firoz, Ansari, Hifzur Rahman, Raghava, Gajendra PS
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2676257/
https://www.ncbi.nlm.nih.gov/pubmed/19358699
http://dx.doi.org/10.1186/1471-2105-10-105
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author Ahmed, Firoz
Ansari, Hifzur Rahman
Raghava, Gajendra PS
author_facet Ahmed, Firoz
Ansari, Hifzur Rahman
Raghava, Gajendra PS
author_sort Ahmed, Firoz
collection PubMed
description BACKGROUND: MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways. RESULTS: This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets. CONCLUSION: In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study .
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spelling pubmed-26762572009-05-03 Prediction of guide strand of microRNAs from its sequence and secondary structure Ahmed, Firoz Ansari, Hifzur Rahman Raghava, Gajendra PS BMC Bioinformatics Research Article BACKGROUND: MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways. RESULTS: This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets. CONCLUSION: In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study . BioMed Central 2009-04-09 /pmc/articles/PMC2676257/ /pubmed/19358699 http://dx.doi.org/10.1186/1471-2105-10-105 Text en Copyright © 2009 Ahmed et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ahmed, Firoz
Ansari, Hifzur Rahman
Raghava, Gajendra PS
Prediction of guide strand of microRNAs from its sequence and secondary structure
title Prediction of guide strand of microRNAs from its sequence and secondary structure
title_full Prediction of guide strand of microRNAs from its sequence and secondary structure
title_fullStr Prediction of guide strand of microRNAs from its sequence and secondary structure
title_full_unstemmed Prediction of guide strand of microRNAs from its sequence and secondary structure
title_short Prediction of guide strand of microRNAs from its sequence and secondary structure
title_sort prediction of guide strand of micrornas from its sequence and secondary structure
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2676257/
https://www.ncbi.nlm.nih.gov/pubmed/19358699
http://dx.doi.org/10.1186/1471-2105-10-105
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AT raghavagajendraps predictionofguidestrandofmicrornasfromitssequenceandsecondarystructure