Transcriptome studies have shown the pervasive nature of transcription, demonstrating almost all the genes undergo alternative splicing. Accurately annotating all transcripts of a gene is crucial. It is needed to understand the impact of mutations on phenotypes, to shed light on genetic and epigenetic regulation of mRNAs and more generally to widen our knowledge about cell functionality and tissue diversity. RNA-sequencing (RNA-Seq), and the other applications of the next-generation sequencing, provides precious data to improve annotations' accuracy, simultaneously creating issues related to the variety, complexity and the size of produced data. In this 'scenario', the lack of user-friendly resources, easily accessible to researchers with low skills in bioinformatics, makes difficult to retrieve complete information about one or few genes without browsing a jungle of databases. Concordantly, the increasing amount of data from 'omics' technologies imposes to develop integrated databases merging different data formats coming from distinct but complementary sources. In light of these considerations, and given the wide interest in studying Down syndrome-a genetic condition due to the trisomy of human chromosome 21 (HSA21)-we developed an integrated relational database and a web interface, named ALE-HSA21 (AnaLysis of Expression on HSA21), accessible at http://bioinfo.na.iac.cnr.it/ALE-HSA21. This comprehensive and user-friendly web resource integrates-for all coding and noncoding transcripts of chromosome 21-existing gene annotations and transcripts identified de novo through RNA-Seq analysis with predictive computational analysis of regulatory sequences. Given the role of noncoding RNAs and untranslated regions of coding genes in key regulatory mechanisms, ALE-HSA21 is also an interesting web-based platform to investigate such processes. The 'transcript-centric' and easily-accessible nature of ALE-HSA21 makes this resource a valuable tool to rapidly retrieve data at the isoform level, rather than at gene level, useful to investigate any disease, molecular pathway or cell process involving chromosome 21 genes.

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

Background: Pathological angiogenesis represents a critical issue in the progression of many diseases. Down syndrome is postulated to be a systemic anti-angiogenesis disease model, possibly due to increased expression of anti-angiogenic regulators on chromosome 21. The aim of our study was to elucidate some features of circulating endothelial progenitor cells in the context of this syndrome. Methods: Circulating endothelial progenitors of Down syndrome affected individuals were isolated, in vitro cultured and analyzed by confocal and transmission electron microscopy. ELISA was performed to measure SDF-1 alpha plasma levels in Down syndrome and euploid individuals. Moreover, qRT-PCR was used to quantify expression levels of CXCL12 gene and of its receptor in progenitor cells. The functional impairment of Down progenitors was evaluated through their susceptibility to hydroperoxide-induced oxidative stress with BODIPY assay and the major vulnerability to the infection with human pathogens. The differential expression of crucial genes in Down progenitor cells was evaluated by microarray analysis. Results: We detected a marked decrease of progenitors' number in young Down individuals compared to euploid, cell size increase and some major detrimental morphological changes. Moreover, Down syndrome patients also exhibited decreased SDF-1 alpha plasma levels and their progenitors had a reduced expression of SDF-1 alpha encoding gene and of its membrane receptor. We further demonstrated that their progenitor cells are more susceptible to hydroperoxide-induced oxidative stress and infection with Bartonella henselae. Further, we observed that most of the differentially expressed genes belong to angiogenesis, immune response and inflammation pathways, and that infected progenitors with trisomy 21 have a more pronounced perturbation of immune response genes than infected euploid cells. Conclusions: Our data provide evidences for a reduced number and altered morphology of endothelial progenitor cells in Down syndrome, also showing the higher susceptibility to oxidative stress and to pathogen infection compared to euploid cells, thereby confirming the angiogenesis and immune response deficit observed in Down syndrome individuals.

In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.