RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
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DOI：
10.1146/annurev-biodatasci-072018-021255
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发表时间：
2019-01-01
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影响因子：
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通讯作者：
Robinson, Mark D.
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文献类型：
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作者：
Van den Berge, Koen;Hembach, Katharina M.;Robinson, Mark D.
基因表达是观察到各种遗传和调节程序的结果的基本水平。在短短几年内，对整个转录组基因表达的测量已经令人信服地从微阵列转向了测序。RNA测序(RNA-seq)为大规模分析转录结果提供了一个定量和开放的系统，因此促进了包括基础科学研究在内的各种应用，也包括农业或临床情况。在过去10年左右的时间里，人们对RNA-SEQ数据集的特点以及开发的各种方法的性能有了很大的了解。在这篇综述中，我们概述了RNA-SEQ数据分析的发展，包括实验设计，明确的重点是基因表达的量化和差异表达的统计方法。我们还重点介绍了新出现的数据类型，例如单细胞RNA-seq和使用长读技术的基因表达谱。
Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.