Background

Sequencing of RNA (RNA-seq) has – with the emergence of next-generation sequencing – become a powerful tool to measure the presence and quantity of RNA in a given cell population or even within a single cell. Since its initial uses (Bainbridge et al., 2006; Cheung et al., 2006; Emrich et al., 2007; Weber et al., 2007; Nagalakshmi et al., 2008), RNA-seq has seen a large variety of applications: from gene expression analysis by quantitating the relative amounts of RNA sequence reads to the discovery of novel transcripts or splice variants, ribosome profiling, or the detection of single nucleotide polymorphisms. Even though a manifold of specific RNA-seq uses exists, the basic workflow remains the same: RNA molecules are extracted, amplified, sequenced, aligned to the genome of the host organism, and, subsequently, the data is analyzed. Sample requirements are relatively low; typically, 1 μg down to 10 ng input RNA is sufficient for downstream amplification and library generation. For single-cell RNA-seq, as little as 10 pg is required since the low amount of input material is amplified prior to library generation (Haque et al., 2017). RNA-seq can be applied to any population of extracted RNA, independent of the source organism. Due to the wide applicability of RNA-seq, sample preparation kits that vary in complexity are commercially available (from RNA extraction to whole RNA-seq library generation, including the computational analysis).

An integral part of RNA-seq is sequencing of the extracted RNA population. Most commonly, sequencing is performed by the detection of fluorescently labeled nucleic acids bound to the surface of flowcells, e.g., using platforms such as Illumina and PAC Biosystem. To this end, the RNA fragments are converted into a cDNA library and amplified, and flowcell adapters are introduced. During each sequencing cycle, DNA polymerases attach fluorescently labeled nucleotides to the flowcell-bound library molecules, which are then detected by the sequencer, typically generating read lengths of 150–300 bp to several Kbp (for Illumina and PAC Biosystem, respectively). More recently emerging is sequencing by passage of nucleic acids through protein nanopores embedded in membranes (e.g., by Nanoporetech) (Logsdon et al., 2020), allowing for the sequencing of much longer fragments (up to Mbp). At the time of writing, the most commonly available sequencers (e.g., Illumina or PAC Biosystem) cost around $100 k for the instrument alone, whereas table-top sequencers using the nanopore technology are considerably cheaper (~$10 k), promising wider applicability in the near future. Due to the considerable cost of the most commonly available sequencing systems, resources are often shared among labs or institutes and managed by trained professionals that ensure the acquisition and integrity of high-quality sequencing data.

While it extends beyond the scope of this manuscript to describe all the applications of RNA-seq, this protocol aims to provide a workflow for RNA-seq expression analysis that can be used as a reference backbone, which the reader can adapt to their specific needs (e.g., RNA extraction from a different source or the addition of splice-aware alignment steps for genomes of higher eukaryotes). RNA-seq expression analysis is a powerful and commonly used tool to identify genes that are up- or downregulated in a stressed sample (e.g., in the presence of genomic mutations, UV light, drugs, chemical or nutrient stress) as compared with a relaxed sample (e.g., wild-type cell population). A gene is “upregulated” or “downregulated,” respectively, when more or less of its RNA is measured (i.e., expressed in the cell) under the stressed conditions as compared with the wild type.

Here, the workflow for RNA-seq expression analysis in S. cerevisiae is described, from cell growth to RNA extraction, library generation, data processing, and analysis. This protocol focuses on using commonly available lab resources wherever possible and utilizes open source and free-of-cost software packages provided by the bioinformatics community. This workflow has proven to be robust and useful for the analysis of gene expression profiles in libraries of histone point mutants in yeast (Braberg et al., 2020).