RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

By K. Van den Berge, Katharina M. Hembach, Charlotte Soneson, Simone Tiberi, Lieven Clement, Michael I. Love, Rob Patro, Mark D. Robinson. Annual Review of Biomedical Data Science, 2018

116 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cd39f3ea46da085cc2a272c1ab35944c13d542e5

Citations in the corpus

• edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, unknown): “RNA-seq has become the standard technology for profiling gene and transcript expression [ 1 , 2 ], while other technologies such as ChIP-seq, A T AC-seq, CUT&Tag, bisulfite sequencing (BS-seq) and Hi-C allow high-resolution exploration of the molecular mechanisms by which expression is regulated […”