Disentangled Assembly Graphs Reveal Hidden Eukaryotic Diversity in eDNA Metagenomic Data

Abstract

Genome assembly graphs contain valuable yet frequently overlooked information that can enhance assembly completeness by revealing contig connectivity. Here, we demonstrate how leveraging these information-rich structures enables the discovery of hidden microeukaryotic diversity in environmental DNA shotgun metagenomic datasets. While GetOrganelle has previously been used for organellar genome assembly from isolated tissues, we present its first application to water eDNA metagenomic data, using diatoms as an example. We tested the efficiency of this organellar genome assembly tool on three freshwater eDNA metagenomic datasets with varying diatom abundances, finding that GetOrganelle alone yields fragmented scaffolds due to mixed-species complexity. By implementing manual disentanglement of assembly graphs, we successfully recovered complete organellar genomes from these assemblies. From high-abundance bloom samples, we recovered complete plastomes of Stephanodiscus hantzschii with 99.9% pairwise identity across distant geographical locations (USA and Czech Republic). From a lower abundance non-bloom sample, we reconstructed a potentially novel Cyclotella plastome with only 94.0% identity to its closest available reference, Cyclotella atomus. Our assembly quality assessment confirmed effective manual disentanglement even at low diatom abundances. By integrating sequence similarity, gene order conservation and phylogenetic analysis, we achieved robust species-level resolution and resolved previous taxonomic uncertainties. Our findings demonstrate that mining eDNA metagenomic data with GetOrganelle reveals previously hidden microeukaryotic diversity and provides higher taxonomic resolution than traditional binning methods. This approach proves especially valuable for microeukaryotes, where reference organellar genomes remain underrepresented in existing databases.