A Review on Recent Computational Methods for Predicting Noncoding RNAs.
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 invol...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
5/3/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=122828565&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122828565 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 5/3/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 122828565 122828565 122828565 10.1155/2017/9139504 122828565 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: A Review on Recent Computational Methods for Predicting Noncoding RNAs. aug: au: Zhang, Yi Huang, Haiyun Zhang, Dahan Qiu, Jing Yang, Jiasheng Wang, Kejing Zhu, Lijuan Fan, Jingjing Yang, Jialiang affil: Department of Mathematics and Information Retrieval of Library and Hebei Laboratory of Pharmaceutic Molecular Chemistry, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China sug: subj: RNA Analysis Computer Simulation Sequence Analysis Bioinformatics RNA Physiology Databases Algorithms ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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