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Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression
MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3360044/ https://www.ncbi.nlm.nih.gov/pubmed/22655055 http://dx.doi.org/10.1371/journal.pone.0037537 |
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author | Wang, Bin Zhang, Shu-Guang Wang, Xiao-Feng Tan, Ming Xi, Yaguang |
author_facet | Wang, Bin Zhang, Shu-Guang Wang, Xiao-Feng Tan, Ming Xi, Yaguang |
author_sort | Wang, Bin |
collection | PubMed |
description | MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA ([Image: see text]60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay. |
format | Online Article Text |
id | pubmed-3360044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33600442012-05-31 Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression Wang, Bin Zhang, Shu-Guang Wang, Xiao-Feng Tan, Ming Xi, Yaguang PLoS One Research Article MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA ([Image: see text]60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay. Public Library of Science 2012-05-24 /pmc/articles/PMC3360044/ /pubmed/22655055 http://dx.doi.org/10.1371/journal.pone.0037537 Text en Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Wang, Bin Zhang, Shu-Guang Wang, Xiao-Feng Tan, Ming Xi, Yaguang Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression |
title | Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression |
title_full | Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression |
title_fullStr | Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression |
title_full_unstemmed | Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression |
title_short | Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression |
title_sort | testing for differentially-expressed micrornas with errors-in-variables nonparametric regression |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3360044/ https://www.ncbi.nlm.nih.gov/pubmed/22655055 http://dx.doi.org/10.1371/journal.pone.0037537 |
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