Guy N Brock, Partha Mukhopadhyay, Vasyl Pihur, Cynthia Webb, Robert M Greene, M Michele Pisano
{"title":"mmpalatmirna,一个R包纲要,说明miRNA微阵列数据的分析。","authors":"Guy N Brock, Partha Mukhopadhyay, Vasyl Pihur, Cynthia Webb, Robert M Greene, M Michele Pisano","doi":"10.1186/1751-0473-8-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":35052,"journal":{"name":"Source Code for Biology and Medicine","volume":" ","pages":"1"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1751-0473-8-1","citationCount":"35","resultStr":"{\"title\":\"MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data.\",\"authors\":\"Guy N Brock, Partha Mukhopadhyay, Vasyl Pihur, Cynthia Webb, Robert M Greene, M Michele Pisano\",\"doi\":\"10.1186/1751-0473-8-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":35052,\"journal\":{\"name\":\"Source Code for Biology and Medicine\",\"volume\":\" \",\"pages\":\"1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/1751-0473-8-1\",\"citationCount\":\"35\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Source Code for Biology and Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/1751-0473-8-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Source Code for Biology and Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/1751-0473-8-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Decision Sciences","Score":null,"Total":0}
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 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.
期刊介绍:
Source Code for Biology and Medicine is a peer-reviewed open access, online journal that publishes articles on source code employed over a wide range of applications in biology and medicine. The journal"s aim is to publish source code for distribution and use in the public domain in order to advance biological and medical research. Through this dissemination, it may be possible to shorten the time required for solving certain computational problems for which there is limited source code availability or resources.