标杆管理
计算生物学
RNA序列
核糖核酸
可靠性(半导体)
计算机科学
注释
一致性(知识库)
基因表达
数据挖掘
生物
基因
生物信息学
遗传学
人工智能
转录组
物理
功率(物理)
营销
量子力学
业务
作者
Duo Wang,Yaqing Liu,Yuanfeng Zhang,Qingwang Chen,Yanxi Han,Wanwan Hou,Cong Liu,Ying Yu,Ziyang Li,Ziqiang Li,Jiaxin Zhao,Leming Shi,Yuanting Zheng,Jinming Li,Rui Zhang
标识
DOI:10.1038/s41467-024-50420-y
摘要
Abstract Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of ‘ground truth’, we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.
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