RNA‐seq: Applications and Best Practices

Abstract

RNA-sequencing (RNA-seq) is the state-of-the-art technique for transcriptome analysis that takes advantage of high-throughput next-generation sequencing. Although being a powerful approach, RNA-seq imposes major challenges throughout its steps with numerous caveats. There are currently many experimental options available, and a complete comprehension of each step is critical to make right decisions and avoid getting into inconclusive results. A complete workflow consists of: (1) experimental design; (2) sample and library preparation; (3) sequencing; and (4) data analysis. RNA-seq enables a wide range of applications such as the discovery of novel genes, gene/transcript quantification, and differential expression and functional analysis. This chapter will encompass the main aspects from sample preparation to downstream data analysis. It will be discussed how to obtain high-quality samples, replicates amount, library preparation, sequencing platforms and coverage, focusing on best recommended practices based on specialized literature. Basic techniques and well-known algorithms are presented and discussed, guiding both beginners and experienced users in the implementation of reliable experiments.Sanger sequencing [6], but with advances in next-generation sequencing technology (NGS), transcriptomic studies have evolved considerably and RNA-seq [7,8] became the state-of-art for transcriptome analysis.RNA-seq consists of the direct sequencing of transcripts by NGS. Several NGS platforms [9][10][11] are commercially available nowadays. In general, an RNA set of interest is converted to a library of complementary DNA (cDNA) fragments and sequenced in a high-throughput manner. Compared to ESTs, RNA-seq provides better resolution and representativeness, whereas when compared to microarrays, the independence of reference sequences facilitates the discovery of novel genes and isoforms [8].RNA-seq experiments harbors challenges from the experimental design to data analysis. Since a complete comprehension of each step is critical to make right decision, this chapter will encompass essential principles required for a successful RNA-seq experiment, focusing on best recommended practices based on specialized and recent literature. Basic techniques and well-known algorithms are presented and discussed, guiding both beginners and experienced users in the implementation of reliable experiments. Experimental designIn order to obtain a successful RNA-seq experiment, it is critical to have a good experimental design. Despite its importance, a proper planning is not always done. There are many experimental options available, and to fully comprehend each step, it is essential to make right decisions, avoiding inconclusive results. These choices depend on extrinsic (e.g., cost, time, samples availability) and intrinsic (e.g., experimental design complexity, transcriptional variability among tissues, samples and organisms) factors. The amount of available resources is usually the main extrinsic limiting factor driving researchers' decisio...

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“…Nevertheless, other (the so-called third-generation) sequencing technologies such as nanopore sequencing (e.g., by Oxford Nanopore Technologies, ONT) and single molecule real-time (SMRT) sequencing (e.g., by Pacific Biosciences, pacbio) are gaining popularity [75], and the length of these reads (complete RNA molecules converted to cDNA) might, in the near future when throughput increases and error rate decreases, become very attractive to those interested in homoeolog (duplicates created in a WGD event) discrimination. Extensive reviews on RNA-Seq technology, RNA-Seq applications, and differential expression analysis can be found elsewhere [74,75,76,77].…”