The restriction site‐associated DNA (RADseq) family of protocols involves digesting DNA and sequencing the region flanking the cut site, thus providing a cost and time‐efficient way for obtaining thousands of genomic markers. However, when working with non‐model taxa with few genomic resources, optimization of RADseq wet‐lab and bioinformatic tools may be challenging, often resulting in allele dropout—that is when a given RADseq locus is not sequenced in one or more individuals resulting in missing data. Additionally, as datasets include divergent taxa, rates of dropout will increase since restriction sites may be lost due to mutation. Mitigating the impacts of allele dropout is crucial, as missing data may lead to incorrect inferences in population genetics and phylogenetics. Here, we demonstrate a simple pipeline for the optimization of RADseq datasets which involves partitioning datasets into subgroups, namely by reducing and analysing the dataset at a population or species level. By running the software Stacks at a subgroup level, we were able to reliably identify and remove individuals with high levels of missing data (bad apples) likely stemming from artefacts in library preparation, DNA quality or sequencing artefacts. Removal of the bad apples generally led to an increase in loci and decrease in missing data in the final datasets. The biological interpretability of the data, as measured by the number of retrieved loci and missing data, was considerably increased.

Library preparation protocols for most sequencing technologies involve PCR amplification of the template DNA, which open the possibility that a given template DNA molecule is sequenced multiple times. Reads arising from this phenomenon, known as PCR duplicates, inflate the cost of sequencing and can jeopardize the reliability of affected experiments. Despite the pervasiveness of this artefact, our understanding of its causes and of its impact on downstream statistical analyses remains essentially empirical. Here, we develop a general quantitative model of amplification distortions in sequencing data sets, which we leverage to investigate the factors controlling the occurrence of PCR duplicates. We show that the PCR duplicate rate is determined primarily by the ratio between library complexity and sequencing depth, and that amplification noise (including in its dependence on the number of PCR cycles) only plays a secondary role for this artefact. We confirm our predictions using new and published RAD‐seq libraries and provide a method to estimate library complexity and amplification noise in any data set containing PCR duplicates. We discuss how amplification‐related artefacts impact downstream analyses, and in particular genotyping accuracy. The proposed framework unites the numerous observations made on PCR duplicates and will be useful to experimenters of all sequencing technologies where DNA availability is a concern.

The repeated, rapid and often pronounced patterns of evolutionary divergence observed in insular plants, or the ‘plant island syndrome’, include changes in leaf phenotypes, growth, as well as the acquisition of a perennial lifestyle. Here, we sequence and describe the genome of the critically endangered, Galápagos-endemic speciesScalesia atractyloidesArnot., obtaining a chromosome-resolved, 3.2-Gbp assembly containing 43,093 candidate gene models. Using a combination of fossil transposable elements,k-mer spectra analyses and orthologue assignment, we identify the two ancestral genomes, and date their divergence and the polyploidization event, concluding that the ancestor of all extantScalesiaspecies was an allotetraploid. There are a comparable number of genes and transposable elements across the two subgenomes, and while their synteny has been mostly conserved, we find multiple inversions that may have facilitated adaptation. We identify clear signatures of selection across genes associated with vascular development, growth, adaptation to salinity and flowering time, thus finding compelling evidence for a genomic basis of the island syndrome in one of Darwin’s giant daisies.