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With the advent of metagenomics, the importance of microorganisms and how their interactions are relevant to ecosystem resilience, sustainability, and human health has become evident. Cataloging and preserving biodiversity is paramount not only for the Earth’s natural systems but also for discovering solutions to challenges that we face as a growing civilization. Metagenomics pertains to the in silico study of all microorganisms within an ecological community in situ,however, many software suites recover only prokaryotes and have limited to no support for viruses and eukaryotes.

In this study, we introduce theViral Eukaryotic Bacterial Archaeal(VEBA) open-source software suite developed to recover genomes from all domains. To our knowledge,VEBAis the first end-to-end metagenomics suite that can directly recover, quality assess, and classify prokaryotic, eukaryotic, and viral genomes from metagenomes.VEBAimplements a novel iterative binning procedure and hybrid sample-specific/multi-sample framework that yields more genomes than any existing methodology alone.VEBAincludes a consensus microeukaryotic database containing proteins from existing databases to optimize microeukaryotic gene modeling and taxonomic classification.VEBAalso provides a unique clustering-based dereplication strategy allowing for sample-specific genomes and genes to be directly compared across non-overlapping biological samples. Finally,VEBAis the only pipeline that automates the detection of candidate phyla radiation bacteria and implements the appropriate genome quality assessments.VEBA’s capabilities are demonstrated by reanalyzing 3 existing public datasets which recovered a total of 948 MAGs (458 prokaryotic, 8 eukaryotic, and 482 viral) including several uncharacterized organisms and organisms with no public genome representatives.

TheVEBAsoftware suite allows for the in silico recovery of microorganisms from all domains of life by integrating cutting edge algorithms in novel ways.VEBAfully integrates both end-to-end and task-specific metagenomic analysis in a modular architecture that minimizes dependencies and maximizes productivity. The contributions ofVEBAto the metagenomics community includes seamless end-to-end metagenomics analysis but also provides users with the flexibility to perform specific analytical tasks.VEBAallows for the automation of several metagenomics steps and shows that new information can be recovered from existing datasets.