Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.
Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.
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DOI：
10.1038/s41598-020-76881-x
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发表时间：
2020-11-12
影响因子：
4.6
通讯作者：
Burguillo FJ
中科院分区：
文献类型：
--
作者：
Corchete LA;Rojas EA;Alonso-López D;De Las Rivas J;Gutiérrez NC;Burguillo FJ
RNA-seq 目前被认为是在全基因组水平上测量基因表达和转录激活的最强大、稳健和适应性最强的技术。由于RNA-seq数据的分析十分复杂，促使了对算法和方法的大量研究。这导致分析的每个步骤中可用的选项数量大幅增加。因此，对于用于分析 RNA-seq 数据的最合适的算法和流程还没有明确的共识。在本研究中，使用替代方法的 192 个管道应用于来自两种人类细胞系的 18 个样本，并对结果的性能进行了评估。通过非参数统计对原始基因表达信号进行量化，以测量精度和准确性。通过测试 17 种差异表达方法来评估差异基因表达性能。该程序通过 qRT-PCR 在相同样品中进行了验证。这项研究权衡了所测试的算法和流程的优缺点，为应用于 RNA-seq 数据分析的不同方法和程序提供了全面的指导，既用于原始表达信号的量化，又用于差异基因表达的量化。
RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
4.4
作者：
Choi J
通讯作者：
Choi J
影响因子：
46.9
作者：
Canales, Roger D.;Luo, Yuling;Goodsaid, Federico M.
通讯作者：
Goodsaid, Federico M.
影响因子：
3.7
作者：
Borozan I;Watt SN;Ferretti V
通讯作者：
Ferretti V
影响因子：
4.4
作者：
Guo Y;Li CI;Ye F;Shyr Y
通讯作者：
Shyr Y