Uncovering the Complexity of Transcriptomes with RNA-Seq

Abstract

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.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

“…[4][5][6]43 Human genetics research has drawn particular benefit by the introduction of NGS platforms and, particularly of RNA-Seq, which has significantly improved the way of looking at cell transcriptome in physiological and pathological conditions. 8,9 It is reasonable to believe that massive analysis of transcriptomes, as well as large-scale NGS studies, will become a routine in the next future, within just a few years, and that not only cancer and ND research will benefit this technology. However, as previously discussed there are still challenges to face.…”

“…NGS has shifted the bottleneck from the generation of large-scale experimental data to their management and computational analysis. 95 As discussed in Costa et al, 8 all NGS downstream analyses are difficult, if not impossible, without an appropriate information technology infrastructure. Indeed, the handling of terabytes of sequencing data -not huge in general for today's standards and not a serious problem for large sequencing centers and core facilities -is a novel problem to deal with for most of the research groups.…”

“…Moreover, hybridization-based platforms, which indirectly quantify gene expression suffer from background and cross-hybridization issues, and the limited dynamic range makes difficult to confidently detect and quantify low-abundance transcripts, as well as very high-abundance ones. 8,9 Sequencing-based approaches, SAGE and CAGE, allow quantitative analysis of gene expression by counting the number of tags (corresponding to the number of mRNA transcripts) rather than measuring signal intensities as in hybridization-based approaches. 10 These technologies have been successfully employed to simultaneously study the expression levels of thousand genes, leading to promising results for Down syndrome (DS), 11 cardiovascular diseases 12 and diabetes.…”

“…15 In addition, methodological refinements (ribodepletion, small-and microRNA isolation and purification) allow to select specific RNA species before RNA-Seq experiments, providing a more comprehensive view of the transcriptional landscape. However, along with the undoubted progress made by the introduction of NGS, not previously encountered issues have been also raised (reviewed in Costa et al 8 ).…”

“…25,26 Microarray analysis, widely used for ND and neuropsychiatric disorders, provided much information about the transcriptional profiles in pathological states, [27][28][29] although discordant results have been often reported. The lack of convergence could be attributed to microarray drawbacks (discussed in 8,9,15 ), as well as to the variable quality/integrity of RNAs strictly influenced by pH, 25 which may dramatically alter the binding to the nucleotide probes, affecting the measure of gene expression levels. As ND patients have prolonged agonal state in brain tissue (strongly correlated with pH alterations), differences in RNA integrity may -at some extent -account for aberrant gene expression profiles.…”