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Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease

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Lung Innate Immunity and Inflammation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1809))

Abstract

Airway epithelial cells (AECs) play a central role in the pathogenesis of many lung diseases. Consequently, advancements in our understanding of the underlying causes of lung diseases, and the development of novel treatments, depend on continued detailed study of these cells. Generation and analysis of high-throughput gene expression data provide an indispensable tool for carrying out the type of broad-scale investigations needed to identify the key genes and molecular pathways that regulate, distinguish, and predict distinct pulmonary pathologies. Of the available technologies for generating genome-wide expression data, RNA sequencing (RNA-seq) has emerged as the most powerful. Hence many researchers are turning to this approach in their studies of lung disease. For the relatively uninitiated, computational analysis of RNA-seq data can be daunting, given the large number of methods and software packages currently available. The aim of this chapter is to provide a broad overview of the major steps involved in processing and analyzing RNA-seq data, with a special focus on methods optimized for data generated from AECs. We take the reader from the point of obtaining sequence reads from the lab to the point of making biological inferences with expression data. Along the way, we discuss the statistical and computational considerations one typically confronts during different phases of analysis and point to key methods, software packages, papers, online guides, and other resources that can facilitate successful RNA-seq analysis.

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Authors and Affiliations

  1. Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA

    Nathan D. Jackson, Lando Ringel & Max A. Seibold

  2. Department of Pediatrics, National Jewish Health, Denver, CO, USA

    Max A. Seibold

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  1. Nathan D. Jackson
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  2. Lando Ringel
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  3. Max A. Seibold
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Correspondence to Max A. Seibold .

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Editors and Affiliations

  1. Department of Immunology and Microbiology University of Colorado School of Medicine, Department of Biomedical Research and Center for Genes Environment and Health, National Jewish Health Denver, CO, USA, Aurora,, Colorado, USA

    Scott Alper

  2. Division of Pulmonary Sciences and Critical Care Medicine Department of Medicine University of Colorado Denver, Division of Pulmonary, Critical Care and Sleep Medicine Department of Medicine National Jewish Health Denver, CO, USA, Aurora, Colorado, USA

    William J. Janssen

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Jackson, N.D., Ringel, L., Seibold, M.A. (2018). Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease. In: Alper, S., Janssen, W. (eds) Lung Innate Immunity and Inflammation. Methods in Molecular Biology, vol 1809. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8570-8_15

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  • DOI: https://doi.org/10.1007/978-1-4939-8570-8_15

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