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

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 metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performa

SUBMITTER: Tong L 

PROVIDER: S-EPMC7578822 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature