RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis.
RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis.
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RNA-seq数据：实验设计和分析的挑战和建议。
DOI：
10.1002/0471142905.hg1113s83
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发表时间：
2014-10-01
影响因子：
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通讯作者：
Holloway, Alisha K
中科院分区：
文献类型：
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作者：
Williams, Alexander G;Thomas, Sean;Wyman, Stacia K;Holloway, Alisha K
RNA-seq广泛用于确定基因或转录物的差异表达，以及鉴定新的转录物，鉴定等位基因特异性表达，并精确测量转录物的翻译。合理的实验设计和分析工具的选择对于确保高质量的数据和可解释的结果至关重要。实验设计的重要考虑因素包括重复次数、是否收集双端或单端读数、序列长度和测序深度。所有RNA-seq实验中的常见分析步骤包括质量控制、读段比对、将读段分配给基因或转录本以及估计基因或转录本丰度。我们的目标是双重的：提出建议的共同组成部分的实验设计和评估工具的能力，这些步骤中的每一个。我们还测试了设计用于检测差异表达的工具，因为这是RNA-seq最广泛的用途。我们希望这些分析将有助于指导那些新的RNA-seq，并将产生关于工具改进和开发的剩余需求的讨论。
RNA-seq is widely used to determine differential expression of genes or transcripts as well as identify novel transcripts, identify allele-specific expression, and precisely measure translation of transcripts. Thoughtful experimental design and choice of analysis tools are critical to ensure high quality data and interpretable results. Important considerations for experimental design include number of replicates, whether to collect paired-end or single-end reads, sequence length, and sequencing depth. Common analysis steps in all RNA-seq experiments include quality control, read alignment, assigning reads to genes or transcripts, and estimating gene or transcript abundance. Our aims are two-fold: to make recommendations for common components of experimental design and assess tool capabilities for each of these steps. We also test tools designed to detect differential expression since this is the most widespread use of RNA-seq. We hope these analyses will help guide those who are new to RNA-seq and will generate discussion about remaining needs for tool improvement and development.