The impact of quality control in RNA-seq experiments

Abstract

High throughput mRNA sample sequencing, known as RNA-seq, is as a powerful approach to detect differentially expressed genes starting from millions of short sequence reads. Although several workflows have been proposed to analyze RNA-seq data, the experiment quality control as a whole is not usually considered, thus potentially biasing the results and/or causing information lost. Experiment quality control refers to the analysis of the experiment as a whole, prior to any analysis. It not only inspects the presence of technical effects, but also if general biological assumptions are fulfilled. In this sense, multivariate approaches are crucial for this task.