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Computational Approaches in Detecting Non- Coding RNA

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 disco...

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Detalles Bibliográficos
Autores principales: Wang, Chunyu, Wei, Leyi, Guo, Maozu, Zou, Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861888/
https://www.ncbi.nlm.nih.gov/pubmed/24396270
http://dx.doi.org/10.2174/13892029113149990005
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author Wang, Chunyu
Wei, Leyi
Guo, Maozu
Zou, Quan
author_facet Wang, Chunyu
Wei, Leyi
Guo, Maozu
Zou, Quan
author_sort Wang, Chunyu
collection PubMed
description 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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spelling pubmed-38618882014-03-01 Computational Approaches in Detecting Non- Coding RNA Wang, Chunyu Wei, Leyi Guo, Maozu Zou, Quan Curr Genomics Article 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. Bentham Science Publishers 2013-09 2013-09 /pmc/articles/PMC3861888/ /pubmed/24396270 http://dx.doi.org/10.2174/13892029113149990005 Text en ©2013 Bentham Science Publishers http://creativecommons.org/licenses/by/2.5/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/), which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Wang, Chunyu
Wei, Leyi
Guo, Maozu
Zou, Quan
Computational Approaches in Detecting Non- Coding RNA
title Computational Approaches in Detecting Non- Coding RNA
title_full Computational Approaches in Detecting Non- Coding RNA
title_fullStr Computational Approaches in Detecting Non- Coding RNA
title_full_unstemmed Computational Approaches in Detecting Non- Coding RNA
title_short Computational Approaches in Detecting Non- Coding RNA
title_sort computational approaches in detecting non- coding rna
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861888/
https://www.ncbi.nlm.nih.gov/pubmed/24396270
http://dx.doi.org/10.2174/13892029113149990005
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