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RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis


Van den Berge, Koen; Hembach, Katharina M; Soneson, Charlotte; Tiberi, Simone; Clement, Lieven; Love, Michael I; Patro, Rob; Robinson, Mark D (2019). RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis. Annual Review of Biomedical Data Science, 2(1):139-173.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:20 July 2019
Deposited On:31 Jan 2020 16:08
Last Modified:30 Mar 2020 13:53
Publisher:Annual Reviews
ISSN:2574-3414
OA Status:Green
Publisher DOI:https://doi.org/10.1146/annurev-biodatasci-072018-021255

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