Harden, J. J. | Search bioRxiv

bioRxiv2025

AVITI library prep miniaturization and combining with Illumina data for phylogenomic and population genomic analyses

Recent advancements in next generation sequencing approaches allow for expansion of evolutionary research into the discovery of genetic patterns and processes underlying diversification across scales. The increased popularity of the Element Bioscience AVITI platform, partially due to the high sequencing accuracy and low cost of reagents, is becoming a viable alternative approach for generating massive amounts of comparative sequencing data across diverse organismal lineages. Using a data set of five accessions from the monocot genus Costus, we tested miniaturization conditions for generating robust, cost-effective libraries and made comparisons of data generated by AVITI and Illumina sequencing platforms to investigate the potential for combining data for population genomic and phylogenomic analyses. Our results show that the AVITI and Illumina data sets are highly congruent in terms of inferring overlapping SNPs, with only a small fraction picked up by only one of the two platforms. The rates of duplication in miniaturized libraries were much higher than in full volume libraries and in the Illumina libraries, resulting in missing SNPs and less sequence coverage when volumes are reduced. For all generated libraries, most downstream evolutionary analyses, including clustering algorithms (such as PCA) and phylogenetic inference, yielded similar results. However, Structure analyses were less consistent across datasets, with data from the most miniaturized libraries being assigned to the wrong clusters. The AVITI platform should be seen as a cost-effective approach for generating genomic data for comparison across taxonomic lineages, even for ongoing projects where Illumina data already exists.

evolutionary biology↗

bioRxiv2025

The power to resolve relationships: identifying incongruence and precision of reduced representation and genome-wide data in phylogenomics and population genomics

Target capture of ultraconserved elements (UCEs) and taxon-specific probes are widely used reduced-representation methods in phylogenomics and, increasingly, in population genomics for their ability to retrieve hundreds to thousands of homologous loci across divergent taxa. Meanwhile, declining costs and improved computational methods have made genome resequencing more accessible for non-model species, enabling the generation of datasets that can address evolutionary and ecological questions from micro- to macroevolutionary scales. Whether target capture approaches to likewise generate datasets that can address questions across broad hierarchical scales remains unclear. Here, we assess the efficacy of data collection (i.e., single nucleotide polymorphism (SNP) retention), predicted genetic variation across samples (i.e., heterozygosity), and phylogenetic congruence between data generated using reduced-representation methods and genome resequencing, leveraging publicly available datasets from plants and animals. We found that SNP retention varied by locus type, with genome-wide datasets retaining the highest proportion of SNPs and UCEs the lowest proportion. Heterozygosity also differed, with Benchmarking Universal Single-Copy Orthologs (BUSCOs) producing the lowest estimates, followed by UCEs; the inclusion of supercontig flanking regions raised heterozygosity values moderately. Across all phylogenetic trees, UCE datasets had the lowest bootstrap support, followed by BUSCOs and single copy orthologous genes. Population structure analyses frequently underestimated the number of ancestral populations in reduced-representation datasets, often identifying fewer populations than genome-wide datasets and assigning samples to different clusters. These discrepancies underscore the challenges of relying solely on reduced-representation methods for robust inferences of genetic diversity, phylogenetic relationships, and population structure.

evolutionary biology↗