RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
- Van den Berge, Koen Bioinformatics Institute Ghent and Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
- Hembach, Katharina M. Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland;
- Soneson, Charlotte Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland;
- Tiberi, Simone Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland;
- Clement, Lieven Bioinformatics Institute Ghent and Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
- Love, Michael I. Department of Biostatistics and Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514, USA
- Patro, Rob Department of Computer Science, Stony Brook University, Stony Brook, New York 11794, USA
- Robinson, Mark D. Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland;
Published in:
- Annual Review of Biomedical Data Science. - Annual Reviews. - 2019, vol. 2, no. 1, p. 139-173
English
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.
- Language
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- English
- Open access status
- green
- Identifiers
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- DOI 10.1146/annurev-biodatasci-072018-021255
- ISSN 2574-3414
- Persistent URL
- https://sonar.ch/global/documents/153018
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