Capturing single-copy nuclear genes, organellar genomes, and nuclear ribosomal DNA from deep genome skimming data for plant phylogenetics: A case study in Vitaceae

With the decreasing cost and availability of many newly developed
bioinformatics pipelines, next-generation sequencing (NGS) has
revolutionized plant systematics in recent years. Genome skimming has been
widely used to obtain high-copy fractions of the genomes, including
plastomes, mitochondrial DNA (mtDNA), and nuclear ribosomal DNA (nrDNA).
In this study, through simulations, we evaluated the optimal (minimum)
sequencing depth and performance for recovering single-copy nuclear genes
(SCNs) from genome skimming data, by subsampling genome resequencing data
and generating 10 datasets with different sequencing coverage in silico.
We tested the performance of four datasets (plastome, nrDNA, mtDNA, and
SCNs) obtained from genome skimming based on phylogenetic analyses of the
Vitis clade at the genus level and Vitaceae at the family level,
respectively. Our results showed that optimal minimum sequencing depth for
high-quality SCNs assembly via genome skimming was about 10× coverage.
Without the steps of synthesizing baits and enrichment experiments,
coupled with incredibly low sequencing costs, we showcase that deep genome
skimming (DGS) is as effective for capturing large datasets of SCNs as the
widely used Hyb-Seq approach, in addition to capturing plastomes, mtDNA,
and entire nrDNA repeats. DGS may serve as an efficient and economical
alternative and may be superior to the popular target enrichment/Hyb-Seq
approach.