Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that regulate cellular functions by finely controlling the transcription and translation efficiency of their targets. Understanding the functions of miRNAs entails predicting the targets of miRNAs and dissecting the miRNA–messenger RNA regulatory network. An increasing number of computational methods for rapidly screening miRNA potential targets have been developed with the increased demand for the identification of miRNA targets. Improvements in current bioinformatics methods allow the efficient identification of miRNA targets. This review describes the progress in the prediction and identification of miRNA targets. It also discusses the remaining challenges and provides insights on future directions.
Keywords: MicroRNA, target identification, computational method.
Current Bioinformatics
Title:Advances on Computational Methods for Identifying the Targets of microRNAs: A Review
Volume: 11 Issue: 3
Author(s): Pengjuan Zhang and Chenghua Li
Affiliation:
Keywords: MicroRNA, target identification, computational method.
Abstract: MicroRNAs (miRNAs) are small noncoding RNAs that regulate cellular functions by finely controlling the transcription and translation efficiency of their targets. Understanding the functions of miRNAs entails predicting the targets of miRNAs and dissecting the miRNA–messenger RNA regulatory network. An increasing number of computational methods for rapidly screening miRNA potential targets have been developed with the increased demand for the identification of miRNA targets. Improvements in current bioinformatics methods allow the efficient identification of miRNA targets. This review describes the progress in the prediction and identification of miRNA targets. It also discusses the remaining challenges and provides insights on future directions.
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Cite this article as:
Zhang Pengjuan and Li Chenghua, Advances on Computational Methods for Identifying the Targets of microRNAs: A Review, Current Bioinformatics 2016; 11 (3) . https://dx.doi.org/10.2174/1574893611666160114235726
DOI https://dx.doi.org/10.2174/1574893611666160114235726 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
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