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Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few ye...

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Autores principales: Chen, Xing, Yan, Chenggang Clarence, Zhang, Xu, You, Zhu-Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862301/
https://www.ncbi.nlm.nih.gov/pubmed/27345524
http://dx.doi.org/10.1093/bib/bbw060
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author Chen, Xing
Yan, Chenggang Clarence
Zhang, Xu
You, Zhu-Hong
author_facet Chen, Xing
Yan, Chenggang Clarence
Zhang, Xu
You, Zhu-Hong
author_sort Chen, Xing
collection PubMed
description LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.
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spelling pubmed-58623012018-08-09 Long non-coding RNAs and complex diseases: from experimental results to computational models Chen, Xing Yan, Chenggang Clarence Zhang, Xu You, Zhu-Hong Brief Bioinform Papers LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research. Oxford University Press 2017-07 2016-06-23 /pmc/articles/PMC5862301/ /pubmed/27345524 http://dx.doi.org/10.1093/bib/bbw060 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Papers
Chen, Xing
Yan, Chenggang Clarence
Zhang, Xu
You, Zhu-Hong
Long non-coding RNAs and complex diseases: from experimental results to computational models
title Long non-coding RNAs and complex diseases: from experimental results to computational models
title_full Long non-coding RNAs and complex diseases: from experimental results to computational models
title_fullStr Long non-coding RNAs and complex diseases: from experimental results to computational models
title_full_unstemmed Long non-coding RNAs and complex diseases: from experimental results to computational models
title_short Long non-coding RNAs and complex diseases: from experimental results to computational models
title_sort long non-coding rnas and complex diseases: from experimental results to computational models
topic Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862301/
https://www.ncbi.nlm.nih.gov/pubmed/27345524
http://dx.doi.org/10.1093/bib/bbw060
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