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1. Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

   https://doi.org/10.1093/nar/gkae528  

   Espinoza, Josh_L; Phillips, Allan; Prentice, Melanie B.; Tan, Gene S.; Kamath, Pauline L.; Lloyd, Karen_G; Dupont, Chris L. (June 2024, Nucleic Acids Research)

   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. 

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2. virMine: automated detection of viral sequences from complex metagenomic samples

   https://doi.org/10.7717/peerj.6695  

   Garretto, Andrea; Hatzopoulos, Thomas; Putonti, Catherine (January 2019, PeerJ)

   Metagenomics has enabled sequencing of viral communities from a myriad of different environments. Viral metagenomic studies routinely uncover sequences with no recognizable homology to known coding regions or genomes. Nevertheless, complete viral genomes have been constructed directly from complex community metagenomes, often through tedious manual curation. To address this, we developed the software tool virMine to identify viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities. virMine automates sequence read quality control, assembly, and annotation. Researchers can easily refine their search for a specific study system and/or feature(s) of interest. In contrast to other viral genome detection tools that often rely on the recognition of viral signature sequences, virMine is not restricted by the insufficient representation of viral diversity in public data repositories. Rather, viral genomes are identified through an iterative approach, first omitting non-viral sequences. Thus, both relatives of previously characterized viruses and novel species can be detected, including both eukaryotic viruses and bacteriophages. Here we present virMine and its analysis of synthetic communities as well as metagenomic data sets from three distinctly different environments: the gut microbiota, the urinary microbiota, and freshwater viromes. Several new viral genomes were identified and annotated, thus contributing to our understanding of viral genetic diversity in these three environments. 

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3. Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton

   https://doi.org/10.1128/mbio.01676-23  

   Alexander, Harriet; Hu, Sarah K.; Krinos, Arianna I.; Pachiadaki, Maria; Tully, Benjamin J.; Neely, Christopher J.; Reiter, Taylor (December 2023, mBio)

   Fraser, Claire M. (Ed.)

   ABSTRACT Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are neither designed to consider eukaryotes nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome-assembled genomes (MAGs) from large-scale metagenomic sequence data sets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8–2,000 µm) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand upon the phylogenetic diversity of likely particle- and host-associated taxa. We use these MAGs to demonstrate an approach to infer the putative trophic mode of the recovered eukaryotic MAGs. We also identify ecological cohorts of co-occurring MAGs, which are driven by specific environmental factors and putative host-microbe associations. These data together add to a number of growing resources of environmentally relevant eukaryotic genomic information. Complementary and expanded databases of MAGs, such as those provided through scalable pipelines like EukHeist, stand to advance our understanding of eukaryotic diversity through increased coverage of genomic representatives across the tree of life. IMPORTANCESingle-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers’ efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity. 

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4. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation

   https://doi.org/10.7717/peerj.11447  

   Pratama, Akbar Adjie; Bolduc, Benjamin; Zayed, Ahmed A.; Zhong, Zhi-Ping; Guo, Jiarong; Vik, Dean R.; Gazitúa, Maria Consuelo; Wainaina, James M.; Roux, Simon; Sullivan, Matthew B. (January 2021, PeerJ)

   null (Ed.)

   Background Viruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs). Results The in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k -mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k -mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ∼5% for virome and ∼75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets. For viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ∼95% (whole genomes) down to ∼80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes. Conclusion Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets. 

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5. Evaluating and Improving Small Subunit rRNA PCR Primer Coverage for Bacteria, Archaea, and Eukaryotes Using Metagenomes from Global Ocean Surveys

   https://doi.org/10.1128/mSystems.00565-21  

   McNichol, Jesse; Berube, Paul M.; Biller, Steven J.; Fuhrman, Jed A. (June 2021, mSystems)

   Gilbert, Jack A. (Ed.)

   ABSTRACT Small subunit rRNA (SSU rRNA) amplicon sequencing can quantitatively and comprehensively profile natural microbiomes, representing a critically important tool for studying diverse global ecosystems. However, results will only be accurate if PCR primers perfectly match the rRNA of all organisms present. To evaluate how well marine microorganisms across all 3 domains are detected by this method, we compared commonly used primers with >300 million rRNA gene sequences retrieved from globally distributed marine metagenomes. The best-performing primers compared to 16S rRNA of bacteria and archaea were 515Y/926R and 515Y/806RB, which perfectly matched over 96% of all sequences. Considering cyanobacterial and chloroplast 16S rRNA, 515Y/926R had the highest coverage (99%), making this set ideal for quantifying marine primary producers. For eukaryotic 18S rRNA sequences, 515Y/926R also performed best (88%), followed by V4R/V4RB (18S rRNA specific; 82%)—demonstrating that the 515Y/926R combination performs best overall for all 3 domains. Using Atlantic and Pacific Ocean samples, we demonstrate high correspondence between 515Y/926R amplicon abundances (generated for this study) and metagenomic 16S rRNA (median R 2 = 0.98, n  = 272), indicating amplicons can produce equally accurate community composition data compared with shotgun metagenomics. Our analysis also revealed that expected performance of all primer sets could be improved with minor modifications, pointing toward a nearly completely universal primer set that could accurately quantify biogeochemically important taxa in ecosystems ranging from the deep sea to the surface. In addition, our reproducible bioinformatic workflow can guide microbiome researchers studying different ecosystems or human health to similarly improve existing primers and generate more accurate quantitative amplicon data. IMPORTANCE PCR amplification and sequencing of marker genes is a low-cost technique for monitoring prokaryotic and eukaryotic microbial communities across space and time but will work optimally only if environmental organisms match PCR primer sequences exactly. In this study, we evaluated how well primers match globally distributed short-read oceanic metagenomes. Our results demonstrate that primer sets vary widely in performance, and that at least for marine systems, rRNA amplicon data from some primers lack significant biases compared to metagenomes. We also show that it is theoretically possible to create a nearly universal primer set for diverse saline environments by defining a specific mixture of a few dozen oligonucleotides, and present a software pipeline that can guide rational design of primers for any environment with available meta’omic data. 

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