The power and promise of RNA‐seq in ecology and evolution

Abstract

Reference is regularly made to the power of new genomic sequencing approaches. Using powerful technology, however, is not the same as having the necessary power to address a research question with statistical robustness. In the rush to adopt new and improved genomic research methods, limitations of technology and experimental design may be initially neglected. Here, we review these issues with regard to RNA sequencing (RNA-seq). RNA-seq adds large-scale transcriptomics to the toolkit of ecological and evolutionary biologists, enabling differential gene expression (DE) studies in nonmodel species without the need for prior genomic resources. High biological variance is typical of field-based gene expression studies and means that larger sample sizes are often needed to achieve the same degree of statistical power as clinical studies based on data from cell lines or inbred animal models. Sequencing costs have plummeted, yet RNA-seq studies still underutilize biological replication. Finite research budgets force a trade-off between sequencing effort and replication in RNAseq experimental design. However, clear guidelines for negotiating this trade-off, while taking into account study-specific factors affecting power, are currently lacking. Study designs that prioritize sequencing depth over replication fail to capitalize on the power of RNA-seq technology for DE inference. Significant recent research effort has gone into developing statistical frameworks and software tools for power analysis and sample size calculation in the context of RNA-seq DE analysis. We synthesize progress in this area and derive an accessible rule-of-thumb guide for designing powerful RNA-seq experiments relevant in eco-evolutionary and clinical settings alike.

Transcriptomic responses to environmental change in fishes: Insights from RNA sequencing

“…Indeed, of the studies included here, nearly half did not include true biological replicates (at best, samples were pooled within groups to create a single library for RNA-seq), consistent with the dearth of replication observed in ecological and evolutionary studies in general (Todd et al 2016). St.…”

“…The application of RNA-seq to study non-model systems is still in its infancy, and care must be taken not to overlook its limitations in the rush to adopt the technology (Costa et al 2013;Todd et al 2016). In this section, we describe some of the experimental, analytical, and conceptual challenges raised specifically by the body of work described herein and highlight some avenues of future exploratory and confirmatory analyses.…”

Advances in the Application of Genetics in Marine Turtle Biology and Conservation

“…In contrast, SNPs typically have lower power per locus relative to microsatellites, but hundreds to thousands of SNP loci can now be rapidly and reliably quantified across large numbers of individuals (Campbell et al, 2015;Ali et al, 2016;Andrews et al, 2016). Additionally, SNPs in coding regions can be used to understand genotype and phenotype linkages and identify loci under selection if adequate genomic and budget resources are available (Hoban et al, 2016;Todd et al, 2016).…”

Transcriptome Analysis for Non-Model Organism: Current Status and Best-Practices

“…Despite huge efort, de novo sequencing of an entire genome is not an easy task, even now, and this also makes 'RNA sequencing (hereafter, RNA-Seq)-based transcriptomic analysis' appealing for non-model organisms that are generally described as having no or limited genomic resources and transcriptomic datasets as well as molecular tools [3][4][5][6]. In the ield of '-omics' disciplines, RNA-Seq is among high-throughput experimental methods and widely used for identifying all functional elements in the genome.…”