Journal article

RNA sequencing data: hitchhiker's guide to expression analysis

  • Van Den Berge, Koen Bioinformatics Institute and Department of Applied Mathematics, Computer Science and Statistics, University of Ghent, Ghent, Belgium
  • Hembach, Katharina Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
  • Soneson, Charlotte Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
  • Tiberi, Simone Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
  • Clement, Lieven Bioinformatics Institute and Department of Applied Mathematics, Computer Science and Statistics, University of Ghent, Ghent, Belgium
  • Love, Michael I Department of Biostatistics and Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States
  • Patro, Rob Department of Computer Science, Stony Brook University, Stony Brook, United States
  • Robinson, Mark ORCID Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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English 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 clinical 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.
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  • English
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https://sonar.ch/global/documents/31176
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