Abstract
RNA-Seq examines global gene expression to provide insights into cellular processes, and it can be particularly informative when comparing contrasting physiological states or strains. Although relatively routine in many laboratories, there are many steps involved in performing a transcriptomics experiment to ensure representative and high-quality results are generated for analysis. In this chapter, we present the application of widely used bioinformatic methodologies to assess, trim, and filter RNA-seq reads for quality using FastQC and Trim Galore, respectively. High-quality reads are mapped using Bowtie2 and differentially expressed genes across different groups were estimated using the DEseq2 R-Bioconductor package. In addition, we describe the various steps to perform the sample-wise data quality assessment by generating exploratory plots through the DESeq2 package. Simple steps to calculate the significant differentially expressed genes, up- and down-regulated genes, and exporting the data and images are also included. A Venn diagram is a useful method to compare the differentially expressed genes across various comparisons and steps to generate the Venn diagram from DESeq2 results are provided. Finally, the output from DESeq2 is compared to published results from EdgeR. The Clostridium autoethanogenum data are published and publicly available.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57
Hör J, Gorski SA, Vogel J (2018) Bacterial RNA biology on a genome scale. Mol Cell 70:785–799
Anders S, McCarthy DJ, Chen Y et al (2013) Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 8:1765
Ozsolak F, Milos PM (2010) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87
Manga P, Klingeman DM, Lu T-YS et al (2016) Replicates, read numbers, and other important experimental design considerations for microbial RNA-seq identified using Bacillus thuringiensis datasets. Front Microbiol 7:794
Dillies M-A, on behalf of The French StatOmique Consortium, Rau A et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683
Gierliński M, Cole C, Schofield P et al (2015) Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment. Bioinformatics 31:3625–3630
Mi G, Di Y, Schafer DW (2015) Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data. PLoS One 10:1–16
Miller CA, Hampton O, Coarfa C et al (2011) ReadDepth: a parallel R package for detecting copy number alterations from short sequencing reads. PLoS One 6:1–7
Valgepea K, de Souza Pinto Lemgruber R, Meaghan K et al (2017) Maintenance of ATP homeostasis triggers metabolic shifts in gas-fermenting Acetogens. Cell Syst 4:505–515.e5
Liew F, Martin ME, Tappel RC et al (2016) Gas fermentation—a flexible platform for commercial scale production of low-carbon-fuels and chemicals from waste and renewable Feedstocks. Front Microbiol 7:694
Heijstra BD, Leang C, Juminaga A (2017) Gas fermentation: cellular engineering possibilities and scale up. Microb Cell Factories 16:60
FastQC, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with bowtie 2. Nat Methods 9:357–359
Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
Brown SD, Nagaraju S, Utturkar S et al (2014) Comparison of single-molecule sequencing and hybrid approaches for finishing the genome of Clostridium autoethanogenum and analysis of CRISPR systems in industrial relevant clostridia. Biotechnol Biofuels 7:1–18
Acknowledgments
This material by the Clostridium foundry for biosystems design (cBioFAB) is based upon work supported by the U.S. Department of Energy, Office of Biological and Environmental Research in the DOE Office of Science under Award Number DE-SC0018249.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Utturkar, S., Dassanayake, A., Nagaraju, S., Brown, S.D. (2020). Bacterial Differential Expression Analysis Methods. In: Himmel, M., Bomble, Y. (eds) Metabolic Pathway Engineering. Methods in Molecular Biology, vol 2096. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0195-2_8
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0195-2_8
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0194-5
Online ISBN: 978-1-0716-0195-2
eBook Packages: Springer Protocols