RNA-Seq: Data Analysis Methods and Applications

Dear Colleagues,

Over the past decade, RNA-Seq has become the method of choice in a wide variety of life science applications because of the feasibility to combine in a specific high-throughput sequencing assay, both to identify transcripts and quantify their expression. Furthermore, as next generation sequencing technologies evolve, RNA-Seq offers more exciting opportunities involving, but not limited to, the ability to explore gene expression at a single cell resolution (single cell RNA-seq). Despite the extensive popularity of RNA-seq approaches, several challenges are associated with the followed workflow in data exploration from the initial experimental design, quality control, data normalization and correction, transcriptome assembly and read alignment/mapping to downstream statistical and bioinformatics data analysis involved in cell-level gene expression quantification, clustering and dimension reduction, differential transcript/gene expression, and functional analysis or integration of RNA-seq data with data from other multi-omics methodologies.

This Special Issue on "RNA-Seq: Data Analysis Methods and Applications" aims to attract original contributions that address these challenges in the context of different application purposes and provide the latest achievements, advancements, or improvements in the state-of-the-art-relevant to this active field of system biology. Literature reviews, as well as innovative studies and protocols, on bulk and single-cell RNA-Seq topics will be considered for publication.

We look forward to receiving your article.

Dr. Heleni Loutrari
Mr. Panagiotis Agioutantis
Guest Editors

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