Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana

Abstract

Quantification of gene expression is crucial to connect genome sequences with phenotypic and physiological data. RNA-Sequencing (RNA-Seq) has taken a prominent role in the study of transcriptomic reactions of plants to various environmental and genetic perturbations. However, comparative tests of different tools for RNA-Seq read mapping and quantification have been mainly performed on data from animals or humans, which necessarily neglect, for example, the large genetic variability among natural accessions within plant species. Here, we compared seven computational tools for their ability to map and quantify Illumina single-end reads from the Arabidopsis thaliana accessions Columbia-0 (Col-0) and N14. Between 92.4% and 99.5% of all reads were mapped to the reference genome or transcriptome and the raw count distributions obtained from the different mappers were highly correlated. Using the software DESeq2 to determine differential gene expression (DGE) between plants exposed to 20 °C or 4 °C from these read counts showed a large pairwise overlap between the mappers. Interestingly, when the commercial CLC software was used with its own DGE module instead of DESeq2, strongly diverging results were obtained. All tested mappers provided highly similar results for mapping Illumina reads of two polymorphic Arabidopsis accessions to the reference genome or transcriptome and for the determination of DGE when the same software was used for processing.