Next Generation Sequencing (NGS) technologies are increasingly being used for gene expression pro?filing as a replacement for microarrays. The expression level given by these technologies is the number of reads in the library mapping to a given feature (gene, exon, transcript, etc.), i.e., the read counts. Most of the statistical methods for assessment of differential expression using count data rely on parametric assumptions about the distribution of the counts (Poisson, Negative Binomial, …). Moreover, many of them need replicates to work and tend to have problems to evaluate differential expression in features with low counts.