Viral Contigs Research Articles

ViraLM: Empowering Virus Discovery through the Genome Foundation Model.

Viruses, with their ubiquitous presence and high diversity, play pivotal roles in ecological systems and public health. Accurate identification of viruses in various ecosystems is essential for comprehending their variety and assessing their ecological influence. Metagenomic sequencing has become a major strategy to survey the viruses in various ecosystems. However, accurate and comprehensive virus detection in metagenomic data remains difficult. Limited reference sequences prevent alignment-based methods from identifying novel viruses. Machine learning-based tools are more promising in novel virus detection but often miss short viral contigs, which are abundant in typical metagenomic data. The inconsistency in virus search results produced by available tools further highlights the urgent need for a more robust tool for virus identification. In this work, we develop ViraLM for identifying novel viral contigs in metagenomic data. By employing the latest genome foundation model as the backbone and training on a rigorously constructed dataset, the model is able to distinguish viruses from other organisms based on the learned genomic characteristics. We thoroughly tested ViraLM on multiple datasets and the experimental results show that ViraLM outperforms available tools in different scenarios. In particular, ViraLM improves the F1-score on short contigs by 22%. The source code of ViraLM is available via: https://github.com/ChengPENG-wolf/ViraLM.

Metagenomic time series reveals a Western English Channel viral community dominated by members with strong seasonal signals.

Marine viruses are key players of ocean biogeochemistry, profoundly influencing microbial community ecology and evolution. Despite their importance, few studies have explored continuous inter-seasonal viral metagenomic time series in marine environments. Viral dynamics are complex, influenced by multiple factors such as host population dynamics and environmental conditions. To disentangle the complexity of viral communities, we developed an unsupervised machine learning framework to classify viral contigs into "chronotypes" based on temporal abundance patterns. Analysing an inter-seasonal monthly time series of surface viral metagenomes from the Western English Channel, we identified chronotypes and compared their functional and evolutionary profiles. Results revealed a consistent annual cycle with steep compositional changes from winter to summer and steadier transitions from summer to winter. Seasonal chronotypes were enriched in potential auxiliary metabolic genes of the ferrochelatases and 2OG-Fe(II) oxygenase orthologous groups compared to non-seasonal types. Chronotypes clustered into four groups based on their correlation profiles with environmental parameters, primarily driven by temperature and nutrients. Viral contigs exhibited a rapid turnover of polymorphisms, akin to Red Queen dynamics. However, within seasonal chronotypes, some sequences exhibited annual polymorphism recurrence, suggesting that a fraction of the seasonal viral populations evolve more slowly. Classification into chronotypes revealed viral genomic signatures linked to temporal patterns, likely reflecting metabolic adaptations to environmental fluctuations and host dynamics. This novel framework enables the identification of long-term trends in viral composition, environmental influences on genomic structure, and potential viral interactions.

Synoptic Variation Drives Genetic Diversity and Transmission Mode of Airborne DNA Viruses in Urban Space.

Airborne viruses are ubiquitous and play critical roles in maintaining ecosystem balance, however, they remain unexplored. Here, it is aimed to demonstrate that highly diverse airborne viromes carry out specific metabolic functions and use different transmission modes under different air quality conditions. A total of 263.5-Gb data are collected from 13 air samples for viral metagenomic analysis. After assembly and curation, a total of 12 484 viral contigs (1.5-184.2 kb) are assigned to 221 genus-level clades belonging to 47 families, 19 orders, and 15 classes. The composition of viral communities is influenced by weather conditions, with the main biomarker being Caudoviricetes. The most dominant viruses in these air samples belong to the dsDNA Caudoviricetes (54.0%) and ssDNA Repensiviricetes (31.2%) classes. Twelve novel candidate viruses are identified at the order/family/genus levels by alignment of complete genomes and core genes. Notably, Caudoviricetes are highly prevalent in cloudy and smoggy air, whereas Repensiviricetesare highly dominant in sunny and rainy air. Diverse auxiliary metabolic genes of airborne viruses are mainly involved in deoxynucleotide synthesis, implying their unique roles in atmosphere ecosystem. These findings deepen the understanding of the meteorological impacts on viral composition, transmission mode, and ecological roles in the air that we breathe.

Unveiling the potential role of virus-encoded polyphosphate kinases in enhancing phosphorus removal in activated sludge systems

While microbial phosphate removal in activated sludge (AS) systems has been extensively studied, the role of viruses in this process remains largely unexplored. In this study, we identified 149 viral auxiliary metabolic genes associated with phosphorus cycling from 2,510 viral contigs (VCs) derived from AS systems. Notably, polyphosphate kinase 1 (ppk1) and polyphosphate kinase 2 (ppk2) genes, which are primarily responsible for phosphate removal, were found in five unclassified VCs. These genes exhibited conserved protein structures and active catalytic sites, indicating a pivotal role of viruses in enhancing phosphorus removal. Phylogenetic analysis demonstrated a close relationship between viral ppk genes and their bacterial counterparts, suggesting the occurrence of horizontal gene transfer. Furthermore, experimental assays validated that viral ppk genes enhanced host phosphate removal capabilities. VCs carrying ppk genes were observed across diverse ecological and geographical contexts, suggesting their potential to bolster host functions in varied environmental and nutrient settings, spanning natural and engineered systems. These findings uncover a previously underappreciated mechanism by which viruses enhance phosphate removal in wastewater treatment plants. Overall, our study highlights the potential for leveraging virus-encoded genes to improve the efficiency of biological phosphorus removal processes, offering new insights into the microbial ecology of AS systems and the role of viruses in biogeochemical cycling.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs

BackgroundMetagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read.ResultsHere, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3–40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes.ConclusionPenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities.C22dm7AjjRZTtiuc33W1snVideo

VirTAXA: enhancing RNA virus taxonomic classification with remote homology search and tree-based validation.

RNA viruses are ubiquitous across a broad spectrum of ecosystems. Therefore, beyond their significant implications for public health, RNA viruses are also key players in ecological processes. High-through sequencing has accelerated the discovery of RNA viruses. Nevertheless, many of these viruses lack taxonomic annotation, posing a challenge to functional inference and evolutionary study. In particular, virus classification at the genus level remains difficult due to the limited reference data and ambiguous boundaries between some closely related genera. We introduce VirTAXA, a robust classification tool that combines remote homology search and tree-based validation to enhance the genus-level taxonomic classification of RNA viruses. VirTAXA is able to predict the genus label of an assembled viral contig and provide evidence type for each prediction. It achieves comparable accuracy to state-of-the-art methods while assigning genus labels to a greater number of sequences. Specifically, on the Global Ocean RNA metatranscriptomic data, VirTAXA can assign genus labels for 18% more contigs than the second-best classification tool. Furthermore, we demonstrated that VirTAXA can be conveniently extended to other types of viruses. The source code and data of VirTAXA are available via https://github.com/JudithEllyn/VirTAXA.

Viral metagenomic survey of Caspian seals.

Viral diseases of pinnipeds cause substantial mortality and morbidity and can influence population demography. Viral metagenomic studies can therefore play an important role in pinniped health assessments and disease surveillance relevant to both individual species and in a "One Health" context. This study used a metagenomic approach with high throughput sequencing to make the first assessment of viral diversity in Caspian seals (Pusa caspica), the only marine mammal species endemic to the Caspian Sea. Sequencing libraries from 35 seals sampled 2009-2020 were analysed, finding sequences from the viral families Circoviridae, Parvoviridae, Herpesviridae, Papillomaviridae, Picornaviridae, Caliciviridae, Cruciviridae, Anelloviridae, Smacoviridae, and Orthomyxoviridae, with additional detection of Adenoviridae via PCR. The similarity of viral contigs from Caspian seal to sequences recovered from other pinnipeds ranged from 63.74% (San Miguel sea lion calicivirus) to 78.79% (Seal anellovirus 4). Some findings represent novel viral species, but overall, the viral repertoire of Caspian seals is similar to available viromes from other pinnipeds. Among the sequences recovered were partial contigs for influenza B, representing only the second such molecular identification in marine mammals. This work provides a foundation for further studies of viral communities in Caspian seals, the diversity of viromes in pinnipeds more generally, and contributes data relevant for disease risk assessments in marine mammals.

Massive expansion of the pig gut virome based on global metagenomic mining

The pig gut virome plays a vital role in the gut microbial ecosystem of pigs. However, a comprehensive understanding of their diversity and a reference database for the virome are currently lacking. To address this gap, we established a Pig Virome Database (PVD) that comprised of 5,566,804 viral contig sequences from 4650 publicly available gut metagenomic samples using a pipeline designated “metav”. By clustering sequences, we identified 48,299 viral operational taxonomic units (vOTUs) genomes of at least medium quality, of which 92.83% of which were not found in existing major databases. The majority of vOTUs were identified as Caudoviricetes (72.21%). The PVD database contained a total of 2,362,631 protein-coding genes across the above medium-quality vOTUs genomes that can be used to explore the functional potential of the pig gut virome. These findings highlight the extensive diversity of viruses in the pig gut and provide a pivotal reference dataset for forthcoming research concerning the pig gut virome.

Complete genome sequences of two tombusvirus-like viruses identified in Echinacea purpurea seeds.

Echinacea is an herbaceous plant originating from North America that is cultivated for gardening and landscaping because of its showy flowers. Using high-throughput sequencing, we identified two viral contigs from echinacea seeds that were related to the family Tombusviridae. These two viruses were similar to oat chlorotic stunt virus (OCSV) and other unassigned tombusviruses; therefore, we tentatively named them Echinacea-associated tombusviruses 1 and 2 (EaTV1 and EaTV2, respectively). The EaTVs represent putative readthrough sites and have no poly(A) tails, aligning with the common features of family Tombusviridae. The EaTVs are included in a monophyletic group of OCSV and several unassigned tombusviruses. Because OCSV is the only member of Avenavirus to date, EaTVs are tentative members of Avenavirus, or they are close sister species to OCSV with several unassigned tombusviruses. RNA-dependent RNA polymerases and coat proteins were well conserved among EaTVs and unassigned tombusviruses; however, their similarities were not correlated, implying divergent and complex evolution.

Predicting novel mosquito-associated viruses from metatranscriptomic dark matter.

The exponential growth of metatranscriptomic studies dedicated to arboviral surveillance in mosquitoes has yielded an unprecedented volume of unclassified sequences referred to as the virome dark matter. Mosquito-associated viruses are classified based on their host range into Mosquito-specific viruses (MSV) or Arboviruses. While MSV replication is restricted to mosquito cells, Arboviruses infect both mosquito vectors and vertebrate hosts. We developed the MosViR pipeline designed to identify complex genomic discriminatory patterns for predicting novel MSV or Arboviruses from viral contigs as short as 500 bp. The pipeline combines the predicted probability score from multiple predictive models, ensuring a robust classification with Area Under ROC (AUC) values exceeding 0.99 for test datasets. To assess the practical utility of MosViR in actual cases, we conducted a comprehensive analysis of 24 published mosquito metatranscriptomic datasets. By mining this metatranscriptomic dark matter, we identified 605 novel mosquito-associated viruses, with eight putative novel Arboviruses exhibiting high probability scores. Our findings highlight the limitations of current homology-based identification methods and emphasize the potentially transformative impact of the MosViR pipeline in advancing the classification of mosquito-associated viruses. MosViR offers a powerful and highly accurate tool for arboviral surveillance and for elucidating the complexities of the mosquito RNA virome.

Crop age is the main driver affecting alfalfa mosaic virus: The predominant virus in the alfalfa virome

AbstractThe alfalfa virome has been understudied. Existing research dealing with viral incidences within the crop primarily concentrate on local factors, rather than considering a broader perspective. In this comprehensive 2‐year study, we define the alfalfa virome and the main local and landscape factors affecting the incidence and annual increase of the Alfalfa mosaic virus (AMV), the most prevalent virus in alfalfa. The study was conducted in commercial alfalfa fields located along the highly productive northeast region of the Iberian Peninsula. For the first time in Europe, next‐generation sequencing revealed the presence of 14 different viruses representing the genera Cytorhabdovirus, Alphapartitivirus, Amalgavirus, Alfamovirus, Luteovirus, Enamovirus and Flavivirus. AMV was the most prevalent species, accounting for 89% of the identified viral contigs. Enzyme‐linked immunosorbent assays showed that the incidence of AMV varied between fields, with the average incidence doubling from 34% in 2019 to 65% in 2020. To assess the effect of local and landscape characteristics on the incidence of AMV and the observed annual increase, we selected different local variables and recorded landscape structure at three different buffer radii (250, 500 and 1000 m) from the centre of each field. Both the incidence and annual increase in AMV were driven mainly by local characteristics. The incidence of AMV showed a significant relationship with crop age and field area, whereas the annual increase was mainly influenced by crop age and the cumulative number of alate morphs of the aphid Acyrthosiphon pisum. Only one landscape composition variable, the percentage of alfalfa, showed a significant relationship with AMV incidence at the 250‐m scale. These results confirm the effect of local variables on the population structure of generalist viruses.

Exploring the Complexity of the Human Respiratory Virome through an In Silico Analysis of Shotgun Metagenomic Data Retrieved from Public Repositories.

Respiratory viruses significantly impact global morbidity and mortality, causing more disease in humans than any other infectious agent. Beyond pathogens, various viruses and bacteria colonize the respiratory tract without causing disease, potentially influencing respiratory diseases' pathogenesis. Nevertheless, our understanding of respiratory microbiota is limited by technical constraints, predominantly focusing on bacteria and neglecting crucial populations like viruses. Despite recent efforts to improve our understanding of viral diversity in the human body, our knowledge of viral diversity associated with the human respiratory tract remains limited. Following a comprehensive search in bibliographic and sequencing data repositories using keyword terms, we retrieved shotgun metagenomic data from public repositories (n = 85). After manual curation, sequencing data files from 43 studies were analyzed using EVEREST (pipEline for Viral assEmbly and chaRactEriSaTion). Complete and high-quality contigs were further assessed for genomic and taxonomic characterization. Viral contigs were obtained from 194 out of the 868 FASTQ files processed through EVEREST. Of the 1842 contigs that were quality assessed, 8% (n = 146) were classified as complete/high-quality genomes. Most of the identified viral contigs were taxonomically classified as bacteriophages, with taxonomic resolution ranging from the superkingdom level down to the species level. Captured contigs were spread across 25 putative families and varied between RNA and DNA viruses, including previously uncharacterized viral genomes. Of note, airway samples also contained virus(es) characteristic of the human gastrointestinal tract, which have not been previously described as part of the lung virome. Additionally, by performing a meta-analysis of the integrated datasets, ecological trends within viral populations linked to human disease states and their biogeographical distribution along the respiratory tract were observed. By leveraging publicly available repositories of shotgun metagenomic data, the present study provides new insights into viral genomes associated with specimens from the human respiratory tract across different disease spectra. Further studies are required to validate our findings and evaluate the potential impact of these viral communities on respiratory tract physiology.

Bacterial dynamics of the plastisphere microbiome exposed to sub-lethal antibiotic pollution

BackgroundAntibiotics and microplastics are two major aquatic pollutants that have been associated to antibiotic resistance selection in the environment and are considered a risk to human health. However, little is known about the interaction of these pollutants at environmental concentrations and the response of the microbial communities in the plastisphere to sub-lethal antibiotic pollution. Here, we describe the bacterial dynamics underlying this response in surface water bacteria at the community, resistome and mobilome level using a combination of methods (next-generation sequencing and qPCR), sequencing targets (16S rRNA gene, pre-clinical and clinical class 1 integron cassettes and metagenomes), technologies (short and long read sequencing), and assembly approaches (non-assembled reads, genome assembly, bacteriophage and plasmid assembly).ResultsOur results show a shift in the microbial community response to antibiotics in the plastisphere microbiome compared to surface water communities and describe the bacterial subpopulations that respond differently to antibiotic and microplastic pollution. The plastisphere showed an increased tolerance to antibiotics and selected different antibiotic resistance bacteria (ARB) and antibiotic resistance genes (ARGs). Several metagenome assembled genomes (MAGs) derived from the antibiotic-exposed plastisphere contained ARGs, virulence factors, and genes involved in plasmid conjugation. These include Comamonas, Chryseobacterium, the opportunistic pathogen Stenotrophomonas maltophilia, and other MAGs belonging to genera that have been associated to human infections, such as Achromobacter. The abundance of the integron-associated ciprofloxacin resistance gene aac(6’)-Ib-cr increased under ciprofloxacin exposure in both freshwater microbial communities and in the plastisphere. Regarding the antibiotic mobilome, although no significant changes in ARG load in class 1 integrons and plasmids were observed in polluted samples, we identified three ARG-containing viral contigs that were integrated into MAGs as prophages.ConclusionsThis study illustrates how the selective nature of the plastisphere influences bacterial response to antibiotics at sub-lethal selective pressure. The microbial changes identified here help define the selective role of the plastisphere and its impact on the maintenance of environmental antibiotic resistance in combination with other anthropogenic pollutants. This research highlights the need to evaluate the impact of aquatic pollutants in environmental microbial communities using complex scenarios with combined stresses.3sW_WiGiki6wh61j1XWikeVideo

Symbiotic virus-bacteria interactions in biological treatment of coking wastewater manipulating bacterial physiological activities

Biological treatment is commonly used in coking wastewater (CWW) treatment. Prokaryotic microbial communities in CWW treatment have been comprehensively studied. However, viruses, as the critical microorganisms affecting microbial processes and thus engineering parameters, still remain poorly understood in CWW treatment context. Employing viromics sequencing, the composition and function of the viral community in CWW treatment were discovered, revealing novel viral communities and key auxiliary metabolic functions. Caudovirales appeared to be the predominant viral order in the oxic-hydrolytic-oxic (OHO) CWW treatment combination, showing relative abundances of 62.47 %, 56.64 % and 92.20 % in bioreactors O1, H and O2, respectively. At the family level, Myoviridae, Podoviridae and Siphoviridae mainly prevailed in bioreactors O1 and H while Phycodnaviridae dominated in O2. A total of 56.23–92.24% of novel viral contigs defied family-level characterization in this distinct CWW habitat. The virus-host prediction results revealed most viruses infecting the specific functional taxa Pseudomonas, Acidovorax and Thauera in the entire OHO combination, demonstrating the viruses affecting bacterial physiology and pollutants removal from CWW. Viral auxiliary metabolic genes (AMGs) were screened, revealing their involvement in the metabolism of contaminants and toxicity tolerance. In the bioreactor O1, AMGs were enriched in detoxification and phosphorus ingestion, where glutathione S-transferase (GSTs) and beta-ketoadipyl CoA thiolase (fadA) participated in biodegradation of polycyclic aromatic hydrocarbons and phenols, respectively. In the bioreactors H and O2, the AMGs focused on cell division and epicyte formation of the hosts, where GDPmannose 4,6-dehydratase (gmd) related to lipopolysaccharides biosynthesis was considered to play an important role in the growth of nitrifiers. The diversities of viruses and AMGs decreased along the CWW treatment process, pointing to a reinforced virus-host adaptive strategy in stressful operation environments. In this study, the symbiotic virus-bacteria interaction patterns were proposed with a theoretical basis for promoting CWW biological treatment efficiency. The findings filled the gaps in the virus-bacteria interactions at the full-scale CWW treatment and provided great value for understanding the mechanism of biological toxicity and sludge activity in industrial wastewater treatment.

Phage phylogeny, molecular signaling, and auxiliary antimicrobial resistance in aerobic and anaerobic membrane bioreactors

Phage emit communication signals that inform their lytic and lysogenic life cycles. However, little is known regarding the abundance and diversity of the genes associated with phage communication systems in wastewater treatment microbial communities. This study focused on phage communities within two distinct biochemical wastewater environments, specifically aerobic membrane bioreactors (AeMBRs) and anaerobic membrane bioreactors (AnMBRs) exposed to varying antibiotic concentrations. Metagenomic data from the bench-scale systems were analyzed to explore phage phylogeny, life cycles, and genetic capacity for antimicrobial resistance and quorum sensing. Two dominant phage families, Schitoviridae and Peduoviridae, exhibited redox-dependent dynamics. Schitoviridae prevailed in anaerobic conditions, while Peduoviridae dominated in aerobic conditions. Notably, the abundance of lytic and lysogenic proteins varied across conditions, suggesting the coexistence of both life cycles. Furthermore, the presence of antibiotic resistance genes (ARGs) within viral contigs highlighted the potential for phage to transfer ARGs in AeMBRs. Finally, quorum sensing genes in the virome of AeMBRs indicated possible molecular signaling between phage and bacteria. Overall, this study provides insights into the dynamics of viral communities across varied redox conditions in MBRs. These findings shed light on phage life cycles, and auxiliary genetic capacity such as antibiotic resistance and bacterial quorum sensing within wastewater treatment microbial communities.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes

BackgroundAs most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance.ResultsWe compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0–97%) and false positive rates (0–30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered.ConclusionsTogether, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Comparative Analysis of Viromes Identified in Multiple Macrofungi.

Macrofungi play important roles in the soil elemental cycle of terrestrial ecosystems. Fungal viruses are common in filamentous fungi, and some of them can affect the growth and development of hosts. However, the composition and evolution of macrofungal viruses are understudied. In this study, ninety strains of Trametes versicolor, Coprinellus micaceus, Amanita strobiliformis, and Trametes hirsuta were collected in China. Four mixed pools were generated by combining equal quantities of total RNA from each strain, according to the fungal species, and then subjected to RNA sequencing. The sequences were assembled, annotated, and then used for phylogenetic analysis. Twenty novel viruses or viral fragments were characterized from the four species of macrofungi. Based on the phylogenetic analysis, most of the viral contigs were classified into ten viral families or orders: Barnaviridae, Benyviridae, Botourmiaviridae, Deltaflexiviridae, Fusariviridae, Hypoviridae, Totiviridae, Mitoviridae, Mymonaviridae, and Bunyavirales. Of these, ambi-like viruses with circular genomes were widely distributed among the studied species. Furthermore, the number and overall abundance of viruses in these four species of macrofungi (Basidiomycota) were found to be much lower than those in broad-host phytopathogenic fungi (Ascomycota: Sclerotinia sclerotiorum, and Botrytis cinerea). By employing metatranscriptomic analysis in this study, for the first time, we demonstrated the presence of multiple mycoviruses in Amanita strobiliformis, Coprinellus micaceus, Trametes hirsute, and Trametes versicolor, significantly contributing to research on mycoviruses in macrofungi.

DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses.

Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e.viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types.

A panoramic view of the virosphere in three wastewater treatment plants by integrating viral-like particle-concentrated and traditional non-concentrated metagenomic approaches.

Wastewater biotreatment systems harbor a rich diversity of microorganisms, and the effectiveness of biotreatment systems largely depends on the activity of these microorganisms. Specifically, viruses play a crucial role in altering microbial behavior and metabolic processes throughout their infection phases, an aspect that has recently attracted considerable interest. Two metagenomic approaches, viral-like particle-concentrated (VPC, representing free viral-like particles) and non-concentrated (NC, representing the cellular fraction), were employed to assess their efficacy in revealing virome characteristics, including taxonomy, diversity, host interactions, lifestyle, dynamics, and functional genes across processing units of three wastewater treatment plants (WWTPs). Our findings indicate that each approach offers unique insights into the viral community and functional composition. Their combined use proved effective in elucidating WWTP viromes. We identified nearly 50,000viral contigs, with Cressdnaviricota and Uroviricota being the predominant phyla in the VPC and NC fractions, respectively. Notably, two pathogenic viral families, Asfarviridae and Adenoviridae, were commonly found in these WWTPs. We also observed significant differences in the viromes of WWTPs processing different types of wastewater. Additionally, various phage-derived auxiliary metabolic genes (AMGs) were active at the RNA level, contributing to the metabolism of the microbial community, particularly in carbon, sulfur, and phosphorus cycling. Moreover, we identified 29 virus-carried antibiotic resistance genes (ARGs) with potential for host transfer, highlighting the role of viruses in spreading ARGs in the environment. Overall, this study provides a detailed and integrated view of the virosphere in three WWTPs through the application of VPC and NC metagenomic approaches. Our findings enhance the understanding of viral communities, offering valuable insights for optimizing the operation and regulation of wastewater treatment systems.