Next-generation biology: Sequencing and data analysis approaches for non-model organisms

Abstract

As sequencing technologies become more affordable, it is now realistic to propose studying the evolutionary history of virtually any organism on a genomic scale. However, when dealing with non-model organisms it is not always easy to choose the best approach given a specific biological question, a limited budget, and challenging sample material. Furthermore, although recent advances in technology offer unprecedented opportunities for research in non-model organisms, they also demand unprecedented awareness from the researcher regarding the assumptions and limitations of each method. In this review we present an overview of the current sequencing technologies and the methods used in typical high-throughput data analysis pipelines. Subsequently, we contextualize high-throughput DNA sequencing technologies within their applications in non-model organism biology. We include tips regarding managing unconventional sample material, comparative and population genetic approaches that do not require fully assembled genomes, and advice on how to deal with low depth sequencing data.

Detailed insights into pan‐European population structure and inbreeding in wild and hatchery Pacific oysters (Crassostrea gigas) revealed by genome‐wide SNP data

“…One of the most commonly used approaches for generating large SNP datasets for nonmodel organisms is to use genotyping by sequencing methods such as restriction site‐associated DNA (RAD) sequencing (Baird et al, ), which allows concurrent SNP identification and genotyping via high‐throughput sequencing of flanking regions of restriction enzyme digestion sites dispersed throughout the genome. These methods have democratized the study of population genomics but are not without their disadvantages (da Fonseca et al, ) such as the need for extensive bioinformatic processing, high rates of missing data, and the issue of uncertainty in genotype calling, which can affect downstream analyses (Shafer et al, ). A convenient alternative where available is therefore to use a medium‐ or high‐density SNP array, in which the probe sequences of many tens or hundreds of thousands of SNPs are “printed” onto a slide against which the genomic DNA is hybridized.…”

Adding Complexity to Complexity: Gene Family Evolution in Polyploids

“…While this has certainly been true, it is really the comparative genomics of non-model organisms that has led to a complete revolution in understanding (e.g., Seeb et al, 2011;da Fonseca et al, 2016). One unexpected finding was that whole genome duplication (WGD) has been an important process contributing to the genomic history of all eukaryotes, including those with relatively small genomes, such as the yeast Saccharomyces cerevisiae (Wolfe and Shields, 1997) and the model plant Arabidopsis thaliana (Blanc and Wolfe, 2004).…”

“…Nevertheless, despite recognition that duplicated genes are critical for understanding genome structure and function (Van de Peer et al, 2017), the practicalities of assembling duplicates in genomic resequencing studies, resolving orthology, and interpreting their potentially redundant effects on phenotypes remains a substantial challenge (da Fonseca et al, 2016). Retention of duplicate genes following genomic or tandem duplication is non-random (Adams, 2007) and is both constrained and promoted by achieving appropriate levels of expression (e.g., Gout and Lynch, 2015;Mattenberger et al, 2017;Rodrigo and Fares, 2018).…”

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.…”