2019/04/30 by Koen Van den Berge, Katharina Hembach, Charlotte Soneson +5 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · #Gene expression and cancer classification #Single-cell and spatial transcriptomics #Molecular Biology Techniques and Applications
paper · doi:10.1146/annurev-biodatasci-072018-021255
Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.