Abstract
The important role of non coding RNAs (ncRNAs) in the cell has made their identification a critical issue in the biological research. However, traditional approaches such as PT-PCR and Northern Blot are costly. With recent progress in bioinformatics and computational prediction technology, the discovery of ncRNAs has become realistically possible. This paper aims to introduce major computational approaches in the identification of ncRNAs, including homologous search, de novo prediction and mining in deep sequencing data. Furthermore, related software tools have been compared and reviewed along with a discussion on future improvements.
Keywords: Non-coding RNA, Machine learning, Bioinformatics, lncRNA, microRNA, Deep sequencing.
Current Genomics
Title:Computational Approaches in Detecting Non- Coding RNA
Volume: 14 Issue: 6
Author(s): Chunyu Wang, Leyi Wei, Maozu Guo and Quan Zou
Affiliation:
Keywords: Non-coding RNA, Machine learning, Bioinformatics, lncRNA, microRNA, Deep sequencing.
Abstract: The important role of non coding RNAs (ncRNAs) in the cell has made their identification a critical issue in the biological research. However, traditional approaches such as PT-PCR and Northern Blot are costly. With recent progress in bioinformatics and computational prediction technology, the discovery of ncRNAs has become realistically possible. This paper aims to introduce major computational approaches in the identification of ncRNAs, including homologous search, de novo prediction and mining in deep sequencing data. Furthermore, related software tools have been compared and reviewed along with a discussion on future improvements.
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Cite this article as:
Wang Chunyu, Wei Leyi, Guo Maozu and Zou Quan, Computational Approaches in Detecting Non- Coding RNA, Current Genomics 2013; 14 (6) . https://dx.doi.org/10.2174/13892029113149990005
DOI https://dx.doi.org/10.2174/13892029113149990005 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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