Improving Reproducibility and Candidate Selection in Transcriptomics Using Meta-analysis.
Improving Reproducibility and Candidate Selection in Transcriptomics Using Meta-analysis.
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DOI：
10.1177/1179069518756296
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发表时间：
2018
影响因子：
--
通讯作者：
Peirson SN
中科院分区：
文献类型：
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作者：
Brown LA;Peirson SN
转录组实验经常用于神经科学中来识别感兴趣的候选基因以供进一步研究。然而，从可比较的转录组研究中鉴定出的基因列表通常显示出有限的重叠。解决可重复性问题的一种方法是以荟萃分析的形式结合多项研究的数据。在这里，我们讨论昼夜节律生物学领域的最新工作，其中转录组荟萃分析已被用来改进候选基因的选择。随着公共数据库中微阵列和 RNA-Seq 数据的可用性不断增加，再加上免费提供的工具和代码，转录组荟萃分析为开放数据如何有益于神经科学研究提供了一个理想的例子。
Transcriptomic experiments are often used in neuroscience to identify candidate genes of interest for further study. However, the lists of genes identified from comparable transcriptomic studies often show limited overlap. One approach to addressing this issue of reproducibility is to combine data from multiple studies in the form of a meta-analysis. Here, we discuss recent work in the field of circadian biology, where transcriptomic meta-analyses have been used to improve candidate gene selection. With the increasing availability of microarray and RNA-Seq data due to deposition in public databases, combined with freely available tools and code, transcriptomic meta-analysis provides an ideal example of how open data can benefit neuroscience research.