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用于全基因组表达研究荟萃分析的可用软件。

Available Software for Meta-analyses of Genome-wide Expression Studies.

作者信息

Forero Diego A

机构信息

PhD Program in Health Sciences, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia.

Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia.

出版信息

Curr Genomics. 2019 Aug;20(5):325-331. doi: 10.2174/1389202920666190822113912.

Abstract

Advances in transcriptomic methods have led to a large number of published Genome-Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

摘要

转录组学方法的进步已促使在人类和模式生物中开展了大量已发表的全基因组表达研究(GWES)。多年来,GWES涉及使用微阵列平台来比较两组或更多感兴趣样本组的基因组表达数据。GWES的荟萃分析是一种强大的方法,可结合来自多项初步研究的信息,以识别感兴趣的生物学主题或疾病中差异表达的基因。在本文中,对用于进行GWES荟萃分析的现有软件的主要特征进行了综述,并描述了来自Bioconductor平台的七个软件包和来自CRAN平台的五个软件包。此外,还对九个先前描述的程序和四个在线程序进行了综述。最后,讨论了这些现有程序的优缺点以及未来发展的关键要点。

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本文引用的文献

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Ten simple rules for carrying out and writing meta-analyses.进行和撰写荟萃分析的十条简单规则。
PLoS Comput Biol. 2019 May 16;15(5):e1006922. doi: 10.1371/journal.pcbi.1006922. eCollection 2019 May.

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