RNA sequencing data: biases and normalization

Motivations In recent years, RNA sequencing (RNA-seq) has rapidly become the method of choice for measuring and comparing gene transcription levels. Despite its wide application, it is now clear that this methodology is not free from biases and that a careful normalization procedure is the basis for a correct data interpretation. The most common normalization techniques account for: library size, gene or transcript length and sequence-specific biases such as GC-content effects. The aim of the present work is to investigate biases affecting RNA seq data and their effect on differential expression analysis. In order to reduce biases due to over-simplification of gene transcription models, we consider exon-based counts.