Editorial: RNA-Seq Analysis: Methods, Applications and Challenges

RNA-seq has revolutionized the research community approach to studying gene expression. In fact, this technology has opened up the possibility of quantifying the expression level of all genes at once, allowing an ex post (rather than ex ante) selection of candidates that could be interesting for a certain study. The continuous drop in costs and the independence of library preparation protocols from the model species, have convinced the stakeholders to invest in this technology, by creating consortia able to produce large disease-specific datasets that, in turn, fostered transcriptomic research at a population level. Among many others, a virtuous example in this sense is The Cancer Genome Atlas. In a short time RNA-seq has moved from a technology to merely quantify the expression of genes to a powerful tool to: discover new transcripts (via de novo transcriptome assembly), characterize alternative splicing variants or new cell types (through single cell RNA sequencing). Leveraging on RNA-seq for daily diagnostic activities is no longer a dream but a consolidated reality.