RNA-seq: from computational challenges to biological insights

Expression profiles have been successfully determined by using hybridization- and tagbased technologies, even though such approaches suffer from limits and drawbacks and lack information about rare RNA species, emerging as contributors to pathological phenotypes in humans (1-8). The introduction of next generation sequencing (NGS) technologies, revealing mammalian transcriptomes' complexity, has shown that a small fraction of transcribed sequences (<2%) is represented by mRNA (9). However, the unprecedented level of sensitivity in the data produced by NGS platforms brings with it the power to make several biological observations, at the cost of a considerable effort in the development of new bioinformatics tools and computational strategies to deal with these massive data files. Indeed, for these large-scale analyses, data transferring, processing and handling may represent a computational bottleneck. Another issue is the availability of software required to perform one or more downstream analysis (1). To this purpose, in this paper we describe the computational strategies used to analyze different aspects of a wholetranscriptome. In particular, we illustrate the results of the analysis performed on a dataset obtained from a strand-specific RNA sequenicng of ribosomal-depleted samples, isolated from a cell type impaired in the Down syndrome