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MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data

BACKGROUND: MicroRNAs (miRNAs) constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation. Microarray expression profiling of miRNAs is an effective means of acquiring genome-level information of miRNA activation an...

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Autores principales: Brock, Guy N, Mukhopadhyay, Partha, Pihur, Vasyl, Webb, Cynthia, Greene, Robert M, Pisano, M Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654997/
https://www.ncbi.nlm.nih.gov/pubmed/23298515
http://dx.doi.org/10.1186/1751-0473-8-1
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author Brock, Guy N
Mukhopadhyay, Partha
Pihur, Vasyl
Webb, Cynthia
Greene, Robert M
Pisano, M Michele
author_facet Brock, Guy N
Mukhopadhyay, Partha
Pihur, Vasyl
Webb, Cynthia
Greene, Robert M
Pisano, M Michele
author_sort Brock, Guy N
collection PubMed
description BACKGROUND: MicroRNAs (miRNAs) constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation. Microarray expression profiling of miRNAs is an effective means of acquiring genome-level information of miRNA activation and inhibition, as well as the potential regulatory role that these genes play within a biological system. As with mRNA expression profiling arrays, miRNA microarrays come in a variety of platforms from numerous manufacturers, and there are a multitude of techniques available for reducing and analyzing these data. RESULTS: In this paper, we present an analysis of a typical two-color miRNA microarray experiment using publicly available packages from R and Bioconductor, the open-source software project for the analysis of genomic data. Covered topics include visualization, normalization, quality checking, differential expression, cluster analysis, miRNA target identification, and gene set enrichment analysis. Many of these tools carry-over from the analysis of mRNA microarrays, but with some notable differences that require special attention. The paper is presented as a “compendium” which, along with the accompanying R package MmPalateMiRNA, contains all of the experimental data and source code to reproduce the analyses contained in the paper. CONCLUSIONS: The compendium presented in this paper will provide investigators with an access point for applying the methods available in R and Bioconductor for analysis of their own miRNA array data.
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spelling pubmed-36549972013-05-16 MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data Brock, Guy N Mukhopadhyay, Partha Pihur, Vasyl Webb, Cynthia Greene, Robert M Pisano, M Michele Source Code Biol Med Methodology BACKGROUND: MicroRNAs (miRNAs) constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation. Microarray expression profiling of miRNAs is an effective means of acquiring genome-level information of miRNA activation and inhibition, as well as the potential regulatory role that these genes play within a biological system. As with mRNA expression profiling arrays, miRNA microarrays come in a variety of platforms from numerous manufacturers, and there are a multitude of techniques available for reducing and analyzing these data. RESULTS: In this paper, we present an analysis of a typical two-color miRNA microarray experiment using publicly available packages from R and Bioconductor, the open-source software project for the analysis of genomic data. Covered topics include visualization, normalization, quality checking, differential expression, cluster analysis, miRNA target identification, and gene set enrichment analysis. Many of these tools carry-over from the analysis of mRNA microarrays, but with some notable differences that require special attention. The paper is presented as a “compendium” which, along with the accompanying R package MmPalateMiRNA, contains all of the experimental data and source code to reproduce the analyses contained in the paper. CONCLUSIONS: The compendium presented in this paper will provide investigators with an access point for applying the methods available in R and Bioconductor for analysis of their own miRNA array data. BioMed Central 2013-01-08 /pmc/articles/PMC3654997/ /pubmed/23298515 http://dx.doi.org/10.1186/1751-0473-8-1 Text en Copyright © 2013 Brock et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Brock, Guy N
Mukhopadhyay, Partha
Pihur, Vasyl
Webb, Cynthia
Greene, Robert M
Pisano, M Michele
MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data
title MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data
title_full MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data
title_fullStr MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data
title_full_unstemmed MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data
title_short MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data
title_sort mmpalatemirna, an r package compendium illustrating analysis of mirna microarray data
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654997/
https://www.ncbi.nlm.nih.gov/pubmed/23298515
http://dx.doi.org/10.1186/1751-0473-8-1
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