Chapter 21
Experimental and Computational Workflow for RNA
Sequencing in Mycobacterium tuberculosis: From Total RNA
to Differentially Expressed Genes
Shuyi Ma, Richard M. Jones Jr., Natalie S. Gleason,
Jessica Farrow-Johnson, and David R. Sherman
Abstract
RNA sequencing (RNAseq) in bacteria has become a transformative tool for many applications, including
the identification of mechanisms that contribute to pathogenesis, environmental adaptation, and drug
response. The kinds of analysis outputs achievable from RNA-seq depend heavily on several key technical
parameters during the sample preparation, sequencing, and data processing steps. In this chapter, we will
describe the process of preparing Mycobacterium tuberculosis samples into sequencing libraries, selecting the
appropriate sequencing platform, and performing data processing compatible with gene expression quantification. We will also discuss how each parameter could affect outcomes. The protocols described below
produce consistently high yields. This chapter should inform on the technical considerations that impact
sequencing output and enable the reader to decide on the best parameters to implement based on their own
experimental goals.
Key words RNA sequencing, rRNA depletion, Sequencing library preparation, Illumina sequencing,
Sequencing data processing, FASTQ alignment, Gene expression quantification, Differential gene
expression calculation
1 Introduction
Over the last decade, sequencing technologies have developed into
extremely helpful and reliable methods to measure the identity,
organization, and abundance of genomes and transcriptomes
[1]. RNA sequencing (RNAseq) has developed into a common
method to quantify differential gene expression (DGE) for bacteria
and other organisms. It offers several key advantages over other
methods of measuring gene expression, such as microarrays (summarized in Table 1, based on information from [1, 2]).
This chapter will describe the experimental and computational
workflow for generating an RNAseq dataset, with the following
assumptions: that the desired output is to quantify mRNA DGE,
Tanya Parish and Anuradha Kumar (eds.), Mycobacteria Protocols, Methods in Molecular Biology, vol. 2314,
https://doi.org/10.1007/978-1-0716-1460-0_21, © Springer Science+Business Media, LLC, part of Springer Nature 2021
481
Experimental and Computational Workflow for RNA
Sequencing in Mycobacterium tuberculosis: From Total RNA
to Differentially Expressed Genes
Shuyi Ma, Richard M. Jones Jr., Natalie S. Gleason,
Jessica Farrow-Johnson, and David R. Sherman
Abstract
RNA sequencing (RNAseq) in bacteria has become a transformative tool for many applications, including
the identification of mechanisms that contribute to pathogenesis, environmental adaptation, and drug
response. The kinds of analysis outputs achievable from RNA-seq depend heavily on several key technical
parameters during the sample preparation, sequencing, and data processing steps. In this chapter, we will
describe the process of preparing Mycobacterium tuberculosis samples into sequencing libraries, selecting the
appropriate sequencing platform, and performing data processing compatible with gene expression quantification. We will also discuss how each parameter could affect outcomes. The protocols described below
produce consistently high yields. This chapter should inform on the technical considerations that impact
sequencing output and enable the reader to decide on the best parameters to implement based on their own
experimental goals.
Key words RNA sequencing, rRNA depletion, Sequencing library preparation, Illumina sequencing,
Sequencing data processing, FASTQ alignment, Gene expression quantification, Differential gene
expression calculation
1 Introduction
Over the last decade, sequencing technologies have developed into
extremely helpful and reliable methods to measure the identity,
organization, and abundance of genomes and transcriptomes
[1]. RNA sequencing (RNAseq) has developed into a common
method to quantify differential gene expression (DGE) for bacteria
and other organisms. It offers several key advantages over other
methods of measuring gene expression, such as microarrays (summarized in Table 1, based on information from [1, 2]).
This chapter will describe the experimental and computational
workflow for generating an RNAseq dataset, with the following
assumptions: that the desired output is to quantify mRNA DGE,
Tanya Parish and Anuradha Kumar (eds.), Mycobacteria Protocols, Methods in Molecular Biology, vol. 2314,
https://doi.org/10.1007/978-1-0716-1460-0_21, © Springer Science+Business Media, LLC, part of Springer Nature 2021
481