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BioMed Research International
Volume 2017 (2017), Article ID 9139504, 14 pages
Review Article

A Review on Recent Computational Methods for Predicting Noncoding RNAs

1Department of Mathematics and Information Retrieval of Library and Hebei Laboratory of Pharmaceutic Molecular Chemistry, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China
2College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
3Department of Network Engineering, School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
4Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576
5School of Mathematics and Information Science, Henan Polytechnic University, Henan 454000, China

Correspondence should be addressed to Yi Zhang; moc.361@2791iqahz and Jialiang Yang; ude.mssm@gnay.gnailaij

Received 29 November 2016; Revised 6 February 2017; Accepted 15 February 2017; Published 3 May 2017

Academic Editor: Ernesto Picardi

Copyright © 2017 Yi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: () homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, () de novo methods using RNA sequence and structure features, () transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and () RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and -mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next- and third-generation sequencing platforms to improve ncRNA prediction.