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Abstract
BackgroundMicroRNAs (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.ResultsIn 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.ConclusionsThe 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.
Highlights
MicroRNAs constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation
Preliminaries R packages that are needed for running the example code in this manuscript are MmPalateMiRNA [30] and its dependencies, and the additional packages latticeExtra [55], clValid [41], targetscan.Mm.eg.db [49], microRNA [46], org.Mm.eg.db [56], and GOstats [53]
In this paper, we present a complete analysis of miRNA data using R and Bioconductor, including preprocessing, normalization, differential expression, clustering, identification of target genes, and gene set enrichment analysis of putative miRNA gene targets
Summary
MicroRNAs (miRNAs) constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation. 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. Much of the recent bioinformatics literature has focused on the role small RNA molecules, termed microRNAs (miRNAs), play in regulating gene expression within plant and animal systems [1]. The Bioconductor project contains a variety of R packages for application to high-throughput “omics” data, including array preprocessing and normalization, identification of differentially expressed genes, clustering, classification, gene-set enrichment analysis, and other down-stream analysis methods. An integrated way to present the analysis from these experiments is in the form of a compendium [17,18], which encapsulates the primary data, supporting software, statistical analysis, and document text in a manner that allows other investigators to completely reproduce the results of the experiment
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