Systematic comparison of RNA-seq pipelines for absolute and relative gene expression quantification. Systematic comparison of RNA-seq pipelines for absolute and relative gene expression quantification

At present, it is admitted that RNA-seq is a more powerful and adaptable technique than hybridization arrays. Nevertheless, as RNA-seq needs a more complex data analysis, it has generated a lot of research on algorithms and workflows. This has resulted in an exponential increase of the options at each step of the analysis. Consequently, there is no clear consensus on the appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines on 18 samples from 2 human cell lines were evaluated. Absolute gene expression quantification was assessed by non-parametric statistics to measure precision and accuracy. Relative gene expression performance was estimated testing 19 differential expression methods. These results were contrasted in parallel with the microarray HTA 2.0 data from Affymetrix using the same set of samples. All procedures were validated by qRT-PCR on 32 genes in all samples. In addition, this study proposes a new statistical approach for precision and accuracy evaluation on real RNA-seq data. It also weights up the advantages and disadvantages of the algorithms and pipelines tested and gives a guide to select the appropriate pipeline to analyse RNA-seq and microarray data. Overall design: Poly A+ RNA from KMS12-BM and JJN3 cells untreated or treated with amiloride or TG003 (0.1 mM, 0.4 mM and 0.4 mM, respectively) for 24 h was isolated and prepared for microarray hibridization.