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dc.contributor.authorTong, L
dc.contributor.authorWu, P-Y
dc.contributor.authorPhan, JH
dc.contributor.authorHassazadeh, HR
dc.contributor.authorSEQC Consortium,
dc.contributor.authorTong, W
dc.contributor.authorWang, MD
dc.date.accessioned2020-12-09T22:23:45Z
dc.date.available2020-12-09T22:23:45Z
dc.date.issued2020-10-21
dc.identifierpii: 10.1038/s41598-020-74567-y
dc.identifier.citationTong, L., Wu, P. -Y., Phan, J. H., Hassazadeh, H. R., SEQC Consortium, , Tong, W. & Wang, M. D. (2020). Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction.. Sci Rep, 10 (1), pp.17925-. https://doi.org/10.1038/s41598-020-74567-y.
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/11343/252951
dc.description.abstractTo 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.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleImpact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction.
dc.typeJournal Article
dc.identifier.doi10.1038/s41598-020-74567-y
melbourne.affiliation.departmentMedical Biology (W.E.H.I.)
melbourne.source.titleScientific Reports
melbourne.source.volume10
melbourne.source.issue1
melbourne.source.pages17925-
dc.rights.licenseCC BY
melbourne.elementsid1478206
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578822
melbourne.contributor.authorShi, Wei
melbourne.contributor.authorLiao, Yang
dc.identifier.eissn2045-2322
melbourne.accessrightsOpen Access


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