Journal article

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

L Tong, PY Wu, JH Phan, HR Hassazadeh, WD Jones, L Shi, M Fischer, CE Mason, S Li, J Xu, W Shi, J Wang, J Thierry-Mieg, D Thierry-Mieg, F Hertwig, F Berthold, B Hero, Y Liao, GK Smyth, D Kreil Show all

Scientific Reports | Published : 2020

Open access

Abstract

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 metr..

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University of Melbourne Researchers