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Design and Analysis of RNA Sequencing Data

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Book cover Next Generation Sequencing and Data Analysis

Part of the book series: Learning Materials in Biosciences ((LMB))

What you will learn

In this chapter, we introduce the concept of RNA-Seq analyses. First, we start to provide an overview of a typical RNA-Seq experiment that includes extraction of sample RNA, enrichment, and cDNA library preparation. Next, we review tools for quality control and data pre-processing followed by a standard workflow to perform RNA-Seq analyses. For this purpose, we discuss two common RNA-Seq strategies, that is a reference-based alignment and a de novo assembly approach. We learn how to do basic downstream analyses of RNA-Seq data, including quantification of expressed genes, differential gene expression (DE) between different groups as well as functional gene analysis. Eventually, we provide a best-practice example for a reference-based RNA-Seq analysis from beginning to end, including all necessary tools and steps on GitHub: https://github.com/grimmlab/BookChapter-RNA-Seq-Analyses.

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Acknowledgements

We are grateful to Dr. Philipp Torkler (Senior Bioinformatics Scientist, Exosome Diagnostics, a Bio-Techne brand, Munich, Germany) for critically reading this text. We thank for correcting our mistakes and suggesting relevant improvements to the original manuscript.

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Authors and Affiliations

  1. Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany

    Richa Bharti & Dominik G. Grimm

  2. Weihenstephan-Triesdorf University of Applied Sciences, Straubing, Germany

    Richa Bharti & Dominik G. Grimm

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  1. Richa Bharti
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Correspondence to Dominik G. Grimm .

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  1. Faculty of Applied Informatics, Deggendorf Institute of Technology, Deggendorf, Germany

    Melanie Kappelmann-Fenzl

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Bharti, R., Grimm, D.G. (2021). Design and Analysis of RNA Sequencing Data. In: Kappelmann-Fenzl, M. (eds) Next Generation Sequencing and Data Analysis. Learning Materials in Biosciences. Springer, Cham. https://doi.org/10.1007/978-3-030-62490-3_11

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