Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

Frontiers in Plant Science
Sophie LamarreElie Maza

Abstract

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approx...Continue Reading

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Citations

Mar 25, 2020·Journal of Experimental Botany·Bing ChengRobert J Henry
Jan 13, 2019·BMC Genomics·Bingqing Lin, Zhen Pang
Mar 30, 2020·The Journal of Infectious Diseases·Antón Vila-SanjurjoMariana Leguia
Jul 26, 2019·Nature Reviews. Genetics·Rory StarkJames Hadfield
May 27, 2021·The Plant Journal : for Cell and Molecular Biology·Rasha Althiab-AlmasaudChristian Chervin
Nov 5, 2021·Marine Biotechnology·Zituo YangGen Hua Yue

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Methods Mentioned

BETA
RNA-Seq
given

Software Mentioned

DESeq2 R package
edgeR
DESeq
edgeR R
count
Bioconductor
QuasiSeq
R
Cuffdiff
HTSeq

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