RNA-seq has represented a pivotal breakthrough in transcriptomics. Among the successful factors of this technology, two features have had the highest impact: the capability of measuring the whole transcriptome in a single run, and the possibility of quantifying the absolute expression level of a target in a given experimental condition.

Whole transcriptome sequencing enabled researchers to adopt an explorative paradigm rather than focusing on pre-determined sets of promising genes to study. Absolute quantification, as opposed to differential analysis, instead, has opened to the possibility of reusing the same data in different experimental contexts. These two facts are the main pillars at the base of the success of several collaborative sharing projects.

Combining data, however, poses the problem of correcting biases due to heterogeneous experimental settings, batch effects and other forms of artifact. As a result, normalization has gained a crucial role in RNA-seq analyses.

Contrary to established laboratory practices, such as qRT-PCR, deciding whether a gene changes its expression profile according to different experimental conditions (or different tissues) is complicated by the fact that differential expression is computed in-silico through statistical software suites that can provide highly discordant results.
The reward of standardizing analysis protocols as well as RNA-seq data will be that of endowing the research community with powerful instruments for understanding the complexity of transcription and, in turn, facilitating the development of personalized expression-based panels of biomarkers to employ at every stage of the therapeutic pathway.

The main goal of this Research Topic is that of dissecting the RNA-seq process: from data production and validation to the analysis and extraction of new knowledge, elucidating weaknesses and opportunities and proposing new approaches and protocols. To this end, either in-silico data analyses and in-vitro experiments can contribute to improve protocols and, in turn, lead RNA-seq to become a mature technology.

Contributions welcomed include, but are not limited to
• Methods for data processing;
• Algorithms for differential analysis;
• Computational or functional analyses;
• Discovery and characterization of novel non-coding RNAs;
• Panels of biomarkers;
• Annotated databases and resources;
• Experimental comparison of established techniques.

Keywords: RNA-seq, Differential expression analysis, Transcript Identification, Biomarkers prediction, Software tools

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.