Vaccination is the most successful and cost-effective method to prevent infectious diseases. However, many vaccine antigens have poor in vivo immunogenic potential and need adjuvants to enhance immune response. The application of systems biology to immunity and vaccinology has yielded crucial insights about how vaccines and adjuvants work. We have previously characterized two safe and powerful delivery systems derived from non-pathogenic prokaryotic organisms: E2 and fd filamentous bacteriophage systems. They elicit an in vivo immune response inducing CD8+ T-cell responses, even in absence of adjuvants or stimuli for dendritic cells' maturation. Nonetheless, a systematic and comparative analysis of the complex gene expression network underlying such activation is missing. Therefore, we compared the transcriptomes of ex vivo isolated bone marrow-derived dendritic cells exposed to these antigen delivery systems. Significant differences emerged, especially for genes involved in innate immunity, co-stimulation, and cytokine production. Results indicate that E2 drives polarization toward the Th2 phenotype, mainly mediated by Irf4, Ccl17, and Ccr4 over-expression. Conversely, fd-scalphaDEC-205 triggers Th1 T cells' polarization through the induction of Il12b, Il12rb, Il6, and other molecules involved in its signal transduction. The data analysis was performed using RNASeqGUI, hence, addressing the increasing need of transparency and reproducibility of computational analysis.

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