RNA sequencing data: hitchhiker's guide to expression analysis [post]

Koen Van Den Berge, Katharina Hembach, Charlotte Soneson, Simone Tiberi, Lieven Clement, Michael I Love, Rob Patro, Mark Robinson
2018 unpublished
Gene expression is the fundamental level at which the result 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
more » ... l situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.
doi:10.7287/peerj.preprints.27283 fatcat:puvpdje4sjapnjfyj7e74fv2dq