Abstract
High-throughput transcriptome sequencing (RNASeq) represents one of the most comprehensive and scalable methods to analyze global gene expression. It allows for absolute quantification of gene expression and also enables the discovery of novel transcripts and alternatively spliced isoforms. This chapter provides hand-on tools and a step-by-step procedure to analyze RNASeq data from punctures of two different retinal tissues (retina and RPE-choroid-sclera) at two different locations (periphery and macular region) from eight individuals. The procedure described in this chapter will use various programs from the free, open-source Tuxedo Suite software package to analyze sequencing data and to ascertain genes that are differentially expressed between retina and RPE-choroid-sclera.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL (2016) Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc 11:1650–1667
Tian L, Kazmierkiewicz KL, Bowman AS, Li M, Curcio CA, Stambolian DE (2015) Transcriptome of the human retina, retinal pigmented epithelium and choroid. Genomics 105:253–264
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T et al (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12:115–121
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M et al (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41:D991–D995
Hansen KD, Brenner SE, Dudoit S (2010) Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res 38:e131–e131
Williams CR, Baccarella A, Parrish JZ, Kim CC (2016) Trimming of sequence reads alters RNA-Seq gene expression estimates. BMC Bioinformatics 17:103
Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25
Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127
Acknowledgment
This work was supported in part by a grant from the Deutsche Forschungsgemeinschaft (GR 5065/1-1) and by the institutional budget for Research and Teaching from the Free State of Bavaria (Titel 73).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Grassmann, F. (2019). Conduct and Quality Control of Differential Gene Expression Analysis Using High-Throughput Transcriptome Sequencing (RNASeq). In: Weber, B.H.F., Langmann, T. (eds) Retinal Degeneration. Methods in Molecular Biology, vol 1834. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-8669-9_2
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8669-9_2
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-8668-2
Online ISBN: 978-1-4939-8669-9
eBook Packages: Springer Protocols