A survey of best practices for RNA-seq data analysis.
A survey of best practices for RNA-seq data analysis.
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RNA-seq数据分析最佳实践综述
DOI：
10.1186/s13059-016-0881-8
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发表时间：
2016-01-26
期刊：
影响因子：
12.3
通讯作者：
Mortazavi A
中科院分区：
文献类型：
--
作者：
Conesa A;Madrigal P;Tarazona S;Gomez-Cabrero D;Cervera A;McPherson A;Szcześniak MW;Gaffney DJ;Elo LL;Zhang X;Mortazavi A
RNA测序（RNA-seq）具有广泛的应用，但没有单一的分析管道可以在所有情况下使用。我们回顾了RNA-seq数据分析的所有主要步骤，包括实验设计，质量控制，读段比对，基因和转录水平的定量，可视化，差异基因表达，选择性剪接，功能分析，基因融合检测和eQTL定位。我们强调与每一步相关的挑战。我们讨论了小RNA的分析以及RNA-seq与其他功能基因组学技术的整合。最后，我们讨论了正在改变转录组学的最新技术的前景。本文的在线版本（doi：10.1186/s13059-016-0881-8）包含补充材料，可供授权用户使用。
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics. The online version of this article (doi:10.1186/s13059-016-0881-8) contains supplementary material, which is available to authorized users.
影响因子：
3
作者：
Yeang CH
通讯作者：
Yeang CH