A Review on Recent Computational Methods for Predicting Noncoding RNAs
Joint Authors
Yang, Jialiang
Zhang, Yi
Huang, Haiyun
Zhang, Dahan
Qiu, Jing
Yang, Jiasheng
Wang, Kejing
Zhu, Lijuan
Fan, Jingjing
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-03
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) 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 k-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.
American Psychological Association (APA)
Zhang, Yi& Huang, Haiyun& Zhang, Dahan& Qiu, Jing& Yang, Jiasheng& Wang, Kejing…[et al.]. 2017. A Review on Recent Computational Methods for Predicting Noncoding RNAs. BioMed Research International،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1139351
Modern Language Association (MLA)
Zhang, Yi…[et al.]. A Review on Recent Computational Methods for Predicting Noncoding RNAs. BioMed Research International No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1139351
American Medical Association (AMA)
Zhang, Yi& Huang, Haiyun& Zhang, Dahan& Qiu, Jing& Yang, Jiasheng& Wang, Kejing…[et al.]. A Review on Recent Computational Methods for Predicting Noncoding RNAs. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1139351
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139351