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Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

journal contribution
posted on 18.12.2020, 03:26 by L Tong, PY Wu, JH Phan, HR Hassazadeh, WD Jones, L Shi, M Fischer, CE Mason, S Li, J Xu, Wei Shi, J Wang, J Thierry-Mieg, D Thierry-Mieg, F Hertwig, F Berthold, B Hero, Yang Liao, GK Smyth, D Kreil, PP Łabaj, D Megherbi, G Schroth, H Fang, W Tong, MD Wang
© 2020, The Author(s). To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.


MDW acknowledges grants from the National Institutes of Health (U54CA119338, R01CA163256, and UL1TR000454), the National Science Foundation (EAGER Award NSF1651360), Children's Healthcare of Atlanta and Georgia Tech Partnership Grant, Giglio Breast Cancer Research Fund, the Centers for Disease Control and Prevention (CDC), and Carol Ann and David D. Flanagan Faculty Fellow Research Fund, Georgia Cancer Coalition (Distinguished Cancer Scholar Award to Professor MDW), Hewlett-Packard, Microsoft Research, and Georgia Tech PACE Computing Resources. PPL and DPK acknowledge support by the Vienna Scientific Cluster (VSC), the Vienna Science and Technology Fund (WWTF), Baxter AG, Austrian Research Centres (ARC) Seibersdorf, and the Austrian Centre of Biopharmaceutical Technology (ACBT). LS acknowledges grants from the National High Technology Research and Development Program of China-863 Program (2015AA020104) and the National Science Foundation of China (31471239). LT acknowledges support from the China Scholarship Council (CSC) under the Grant CSC NO. 201406010343. The authors want to thank all data contributing teams for providing the unique SEQC benchmark datasets, and Ms. Ying Sha at Georgia Institute of Technology for insightful feedback.


Publication Date



Scientific Reports





Article Number





Springer Nature



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