Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures

Abstract

RNA-seq is a modular experimental and computational approach that aims in identifying and quantifying RNA molecules. The modularity of the RNA-seq technology enables adaptation of the protocol to develop new ways to explore RNA biology, but this modularity also brings forth the importance of methodological thoroughness. Liberty of approach comes with the responsibility of choices, and such choices must be informed. Here, we present an approach that identifies gene group specific quantification biases in currently used RNA-seq software and references by processing sequenced datasets using a wide variety of RNA-seq computational pipelined, and by decomposing these expression datasets using an independent component analysis matrix factorisation method. By exploring the RNA-seq pipeline using a systemic approach, we highlight the yet inadequately characterized central importance of genome annotations in quantification results. We also show that the different choices in RNA-seq methodology are not independent, through interactions between genome annotations and quantification software. Genes were mainly found to be affected by differences in their sequence, by overlapping genes and genes with similar sequence. Our approach offers an explanation for the observed biases by identifying the common features used differently by the software and references, therefore providing leads for the betterment of RNA-seq methodology.

RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow

“…If the user provides more than one reference genome, all of the genomes are concatenated and one index is built and used for mapping. We have chosen as mapping software, because it has been continuously developed and maintained for several years [ 26 ]. In addition, a recent study on A. thaliana showed no major differences between RNA-Seq mapping tools in the context of DEG detection [ 27 ], which was also in accordance with a large RNA-Seq pipeline evaluation study recently performed by Corchete et al [ 17 ].…”