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Abstract The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions. 

With emerging infectious disease outbreaks in human, domestic and wild animal populations on the rise, improvements in pathogen characterization and surveillance are paramount for the protection of human and animal health, as well as the conservation of ecologically and economically important wildlife. Genomics offers a range of suitable tools to meet these goals, with metagenomic sequencing facilitating the characterization of whole microbial communities associated with emerging and endemic disease outbreaks. Here, we use metagenomic sequencing in a case-control study to identify microbes in lung tissue associated with newly observed pneumonia-related fatalities in 34 white-tailed deer (Odocoileus virginianus) in Wisconsin, USA. We identified 20 bacterial species that occurred in more than a single individual. Of these, onlyClostridium novyiwas found to substantially differ (in number of detections) between case and control sample groups; however, this difference was not statistically significant. We also detected several bacterial species associated with pneumonia and/or other diseases in ruminants (Mycoplasma ovipneumoniae,Trueperella pyogenes,Pasteurella multocida,Anaplasma phagocytophilum,Fusobacterium necrophorum); however, these species did not substantially differ between case and control sample groups. On average, we detected a larger number of bacterial species in case samples than controls, supporting the potential role of polymicrobial infections in this system. Importantly, we did not detect DNA of viruses or fungi, suggesting that they are not significantly associated with pneumonia in this system. Together, these results highlight the utility of metagenomic sequencing for identifying disease-associated microbes. This preliminary list of microbes will help inform future research on pneumonia-associated fatalities of white-tailed deer.