A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials

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 () and Rui Zhang ()
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Duo Wang: Beijing Hospital/National Center of Gerontology
Yaqing Liu: Fudan University
Yuanfeng Zhang: Beijing Hospital/National Center of Gerontology
Qingwang Chen: Fudan University
Yanxi Han: Beijing Hospital/National Center of Gerontology
Wanwan Hou: Fudan University
Cong Liu: Beijing Hospital/National Center of Gerontology
Ying Yu: Fudan University
Ziyang Li: Central South University
Ziqiang Li: Beijing Hospital/National Center of Gerontology
Jiaxin Zhao: Beijing Hospital/National Center of Gerontology
Leming Shi: Fudan University
Yuanting Zheng: Fudan University
Jinming Li: Beijing Hospital/National Center of Gerontology
Rui Zhang: Beijing Hospital/National Center of Gerontology

Nature Communications, 2024, vol. 15, issue 1, 1-21

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50420-y

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