VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data

Method evaluation for viruses in activated sludge: Concentration, sequencing, and identification

Activated sludge (AS) in wastewater treatment plants is one of the largest artificial microbial ecosystems on earth and it makes enormous contributions to human societies. Viruses are an important component in AS with a high abundance. However, their communities and functionalities have not been as widely explored as those of other microorganisms, such as bacteria. This gap is mainly due to technical challenges in effective viral concentration, extraction, and sequencing. In this study, we compared four kinds of concentration methods, two sequencing approaches, and four identification bioinformatic tools to evaluate the whole analysis workflow for viruses in AS. Results showed flocculation, filtration, and resuspension (FFR) could get the longest DNA lengths and ultracentrifugation obtained the highest DNA yields for viruses in AS. Based on the results of present study, FFR and tangential flow filtration with the membrane pore size of 100 kDa were most recommended to concentrate viruses in AS samples with huge volumes. Besides, different concentration methods could get different viral catalogs and thus multiple methods should be combined to get the whole picture of viruses in the system. In addition, geNomad was the most recommended identification tool for viruses in the present study and the long-read sequencing could improve the assembly statistics of viruses when compared with the short-read sequencing. For the 8192 viral operational taxonomic units in this study, 95.1 % of them were phages and belonged to the same lineage at the order level of Caudovirales. Virulent phages dominated the AS system and Pseudomonadota were the main host. Taken together, this study provides new insights into methods selection for virus research of AS.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, viruses and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or more minor classes from bacteria and archaea in metagenome assemblies. However, these classifiers often overlook the issue of class imbalance, leading to low precision in identifying the minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

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.

The phageome of patients with ulcerative colitis treated with donor fecal microbiota reveals markers associated with disease remission

Bacteriophages are influential within the human gut microbiota, yet they remain understudied relative to bacteria. This is a limitation of studies on fecal microbiota transplantation (FMT) where bacteriophages likely influence outcome. Here, using metagenomics, we profile phage populations - the phageome - in individuals recruited into two double-blind randomized trials of FMT in ulcerative colitis. We leverage the trial designs to observe that phage populations behave similarly to bacterial populations, showing temporal stability in health, dysbiosis in active disease, modulation by antibiotic treatment and by FMT. We identify a donor bacteriophage putatively associated with disease remission, which on genomic analysis was found integrated in a bacterium classified to Oscillospiraceae, previously isolated from a centenarian and predicted to produce vitamin B complex except B12. Our study provides an in-depth assessment of phage populations during different states and suggests that bacteriophage tracking has utility in identifying determinants of disease activity and resolution.

Improving viral annotation with artificial intelligence

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

CAPTVRED: an automated pipeline for viral tracking and discovery from capture-based metagenomics samples

Summary

Target Enrichment Sequencing or Capture-based metagenomics has emerged as an approach of interest for viral metagenomics in complex samples. However, these datasets are usually analyzed with standard downstream Bioinformatics analyses. CAPTVRED (Capture-based metagenomics Analysis Pipeline for tracking ViRal species from Environmental Datasets), has been designed to assess the virome present in complex samples, specially focused on those obtained by Target Enrichment Sequencing approach. This work aims to provide a user-friendly tool that complements this sequencing approach for the total or partial virome description, especially from environmental matrices. It includes a setup module which allows preparation and adjustment of the pipeline to any capture panel directed to a set of species of interest. The tool also aims to reduce time and computational cost, as well as to provide comprehensive, reproducible, and accessible results while being easy to costume, set up, and install.

Availability and implementation

Source code and test datasets are freely available at github repository: https://github.com/CompGenLabUB/CAPTVRED.git.

VirID: Beyond Virus Discovery-An Integrated Platform for Comprehensive RNA Virus Characterization

RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus-host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identify RNA viruses of all types, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks.

Another piece of puzzle for the human microbiome: the gut virome under dietary modulation

The virome is the most abundant and highly variable microbial consortium in the gut. Because of difficulties in isolating and culturing gut viruses and the lack of reference genomes, the virome has remained a relatively elusive aspect of the human microbiome. In recent years, studies on the virome have accumulated growing evidence showing that the virome is diet-modulated and widely involved in regulating health. Here, we review the responses of the gut virome to dietary intake and the potential health implications, presenting changes in the gut viral community and preferences of viral members to particular diets. We further discuss how viral-bacterial interactions and phage lifestyle shifts shape the gut microbiota. We also discuss the specific functions conferred by diet on the gut virome and bacterial community in the context of horizontal gene transfer, as well as the import of new viral members along with the diet. Collating these studies will expand our understanding of the dietary regulation of the gut virome and inspire dietary interventions and health maintenance strategies targeting the gut microbiota.

Towards integrated cross-sectoral surveillance of pathogens and antimicrobial resistance: Needs, approaches, and considerations for linking surveillance to action

Pathogenic and antimicrobial-resistant (AMR) microorganisms are continually transmitted between human, animal, and environmental reservoirs, contributing to the high burden of infectious disease and driving the growing global AMR crisis. The sheer diversity of pathogens, AMR mechanisms, and transmission pathways connecting these reservoirs create the need for comprehensive cross-sectoral surveillance to effectively monitor risks. Current approaches are often siloed by discipline and sector, focusing independently on parts of the whole. Here we advocate that integrated surveillance approaches, developed through transdisciplinary cross-sector collaboration, are key to addressing the dual crises of infectious diseases and AMR. We first review the areas of need, challenges, and benefits of cross-sectoral surveillance, then summarise and evaluate the major detection methods already available to achieve this (culture, quantitative PCR, and metagenomic sequencing). Finally, we outline how cross-sectoral surveillance initiatives can be fostered at multiple scales of action, and present key considerations for implementation and the development of effective systems to manage and integrate this information for the benefit of multiple sectors. While methods and technologies are increasingly available and affordable for comprehensive pathogen and AMR surveillance across different reservoirs, it is imperative that systems are strengthened to effectively manage and integrate this information.

Mechanisms of horizontal gene transfer and viral contribution to the fate of intracellular and extracellular antibiotic resistance genes in anaerobic digestion supplemented with conductive materials under ammonia stress

The addition of conductive materials (CMs) is an effective strategy for mitigating ammonia inhibition during anaerobic digestion (AD). However, the introduction of CMs can result in increased antibiotic resistance genes (ARGs) pollution, potentially facilitated by enhanced horizontal gene transfer (HGT). The complex dynamics of intracellular and extracellular ARGs (iARGs/eARGs) and the mechanisms underlying their transfer, mediated by CMs, in ammonia-stressed AD systems remain unclear. In this study, we investigated the effects of three commonly used CMs-nano magnetite (Mag), nano zero-valent iron (nZVI), and granular activated carbon (GAC)-on the fate of iARGs and eARGs during the AD of waste activated sludge under ammonia stress. The results revealed an unexpected enrichment of iARGs by 1.5 %-10.9 % and a reduction of eARGs by 14.1 %-25.2 % in CM-supplemented AD. This discrepancy in the dynamics of iARGs and eARGs may be attributed to changes in microbial hosts and the horizontal transfer of ARGs. Notably, CMs activated prophages within antibiotic-resistant bacteria (ARB) and their symbiotic partners involved in vitamin B12 provision, leading to the lysis of ARB and the subsequent release of eARGs for transformation. Additionally, the abundance of potentially mobile ARGs, which co-occurred with mobile genetic elements, increased by 56.6 %-134.5 % with CM addition, highlighting an enhanced potential for the HGT of ARGs. Specifically, Mag appeared to promote both transformation and conjugation processes, while nZVI only promoted conjugation. Moreover, none of the three CMs had any discernible impact on transduction. GAC proved superior to both nano Mag and nZVI in controlling the enrichment of iARGs, reducing eARGs, and limiting HGTs simultaneously. Overall, these findings provide novel insights into the role of viruses and the mechanisms of ARG spread in CM-assisted AD, offering valuable information for developing strategies to mitigate ARG pollution in practical applications.

Decoupling of strain- and intrastrain-level interactions of microbiomes in a sponge holobiont

Holobionts are highly organized assemblages of eukaryotic hosts, cellular microbial symbionts, and viruses, whose interactions and evolution involve complex biological processes. It is largely unknown which specific determinants drive similarity or individuality in genetic diversity between holobionts. Here, we combine short- and long-read sequencing and DNA-proximity-linkage technologies to investigate intraspecific diversity of the microbiomes, including host-resolved viruses, in individuals of a model marine sponge. We find strong impacts of the sponge host and the cellular hosts of viruses on strain-level organization of the holobiont, whereas substantial overlap in nucleotide diversity between holobionts suggests frequent exchanges of microbial cells and viruses at intrastrain level in the local sponge population. Immune-evasive arms races likely restricted virus-host co-evolution at the intrastrain level, generated holobiont-specific genome variations, and linked virus-host genetics through recombination. Our work shows that a decoupling of strain- and intrastrain-level interactions is a key factor in the genetic diversification of holobionts.

Unveiling the genomic diversity and ecological impact of phage communities in hospital wastewater

Phages are pivotal in shaping microbial communities and biogeochemical cycles, while our understanding of the diversity, functions potential, and resistance gene carriage of phages in hospital wastewater (HWW) remains limited. We collected influent and effluent samples from the 3 hospital wastewater treatment plants (HWTPs) to assess the diversity and fate of phages, the interactions between phages and hosts, and the presence of resistance genes and auxiliary metabolic genes (AMGs) encoded by phages. Compared to influent, effluent showed reduced phage abundance and altered composition, with decreases in Microviridae and Inoviridae. The gene-sharing network highlights that many phages in HWW are not classified in known viral genera, suggesting HWW as a rich source of new viruses. There was a significant association between phages and microorganisms, with approximately 32.57 % of phages expected to be capable of infecting microbial hosts, characterized primarily by lytic activity. A total of 8 unique antibiotic resistance genes, 13 unique metal resistance genes, and 5 mobile genetic elements were detected in 3 HWTPs phageomes. Phage AMGs have the potential to influence carbon, nitrogen, phosphorus, and sulfur metabolism, impacting biogeochemical cycles. This study reveals the genomic diversity and ecological role of phages in HWTPs, highlighting their environmental and ecosystem impact.

Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.

Antarctic lake viromes reveal potential virus associated influences on nutrient cycling in ice-covered lakes

The McMurdo Dry Valleys (MDVs) of Antarctica are a mosaic of extreme habitats which are dominated by microbial life. The MDVs include glacial melt holes, streams, lakes, and soils, which are interconnected through the transfer of energy and flux of inorganic and organic material via wind and hydrology. For the first time, we provide new data on the viral community structure and function in the MDVs through metagenomics of the planktonic and benthic mat communities of Lakes Bonney and Fryxell. Viral taxonomic diversity was compared across lakes and ecological function was investigated by characterizing auxiliary metabolic genes (AMGs) and predicting viral hosts. Our data suggest that viral communities differed between the lakes and among sites: these differences were connected to microbial host communities. AMGs were associated with the potential augmentation of multiple biogeochemical processes in host, most notably with phosphorus acquisition, organic nitrogen acquisition, sulfur oxidation, and photosynthesis. Viral genome abundances containing AMGs differed between the lakes and microbial mats, indicating site specialization. Using procrustes analysis, we also identified significant coupling between viral and bacterial communities (p = 0.001). Finally, host predictions indicate viral host preference among the assembled viromes. Collectively, our data show that: (i) viruses are uniquely distributed through the McMurdo Dry Valley lakes, (ii) their AMGs can contribute to overcoming host nutrient limitation and, (iii) viral and bacterial MDV communities are tightly coupled.

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.

W2V-repeated index: Prediction of enhancers and their strength based on repeated fragments

Enhancers are crucial in gene expression regulation, dictating the specificity and timing of transcriptional activity, which highlights the importance of their identification for unravelling the intricacies of genetic regulation. Therefore, it is critical to identify enhancers and their strengths. Repeated sequences in the genome are repeats of the same or symmetrical fragments. There has been a great deal of evidence that repetitive sequences contain enormous amounts of genetic information. Thus, We introduce the W2V-Repeated Index, designed to identify enhancer sequence fragments and evaluates their strength through the analysis of repeated K-mer sequences in enhancer regions. Utilizing the word2vector algorithm for numerical conversion and Manta Ray Foraging Optimization for feature selection, this method effectively captures the frequency and distribution of K-mer sequences. By concentrating on repeated K-mer sequences, it minimizes computational complexity and facilitates the analysis of larger K values. Experiments indicate that our method performs better than all other advanced methods on almost all indicators.

Polysaccharides induce deep-sea Lentisphaerae strains to release chronic bacteriophages

Viruses are ubiquitous in nature and play key roles in various ecosystems. Notably, some viruses (e.g. bacteriophage) exhibit alternative life cycles, such as chronic infections without cell lysis. However, the impact of chronic infections and their interactions with the host organisms remains largely unknown. Here, we found for the first time that polysaccharides induced the production of multiple temperate phages infecting two deep-sea Lentisphaerae strains (WC36 and zth2). Through physiological assays, genomic analysis, and transcriptomics assays, we found these bacteriophages were released via a chronic style without host cell lysis, which might reprogram host polysaccharide metabolism through the potential auxiliary metabolic genes. The findings presented here, together with recent discoveries made on the reprogramming of host energy-generating metabolisms by chronic bacteriophages, shed light on the poorly explored marine virus-host interaction and bring us closer to understanding the potential role of chronic viruses in marine ecosystems.

DeepMineLys: Deep mining of phage lysins from human microbiome

Vast shotgun metagenomics data remain an underutilized resource for novel enzymes. Artificial intelligence (AI) has increasingly been applied to protein mining, but its conventional performance evaluation is interpolative in nature, and these trained models often struggle to extrapolate effectively when challenged with unknown data. In this study, we present a framework (DeepMineLys [deep mining of phage lysins from human microbiome]) based on the convolutional neural network (CNN) to identify phage lysins from three human microbiome datasets. When validated with an independent dataset, our method achieved an F1-score of 84.00%, surpassing existing methods by 20.84%. We expressed 16 lysin candidates from the top 100 sequences in E. coli, confirming 11 as active. The best one displayed an activity 6.2-fold that of lysozyme derived from hen egg white, establishing it as the most potent lysin from the human microbiome. Our study also underscores several important issues when applying AI to biology questions. This framework should be applicable for mining other proteins.

Metagenome sequence data mining for viral interaction studies: Review on progress and prospects

Metagenomics has been greatly accelerated by the development of next-generation sequencing (NGS) technologies, which allow scientists to discover and describe novel microorganisms without the need for conventional culture techniques. Examining integrative bioinformatics methods used in viral interaction research, this study highlights metagenomic data from various contexts. Accurate viral identification depends on high-purity genetic material extraction, appropriate NGS platform selection, and sophisticated bioinformatics tools like VirPipe and VirFinder. The efficiency and precision of metagenomic analysis are further improved with the advent of AI-based techniques. The diversity and dynamics of viral communities are demonstrated by case studies from a variety of environments, emphasizing the seasonal and geographical variations that influence viral populations. In addition to speeding up the discovery of new viruses, metagenomics offers thorough understanding of virus-host interactions and their ecological effects. This review provides a promising framework for comprehending the complexity of viral communities and their interactions with hosts, highlighting the transformational potential of metagenomics and bioinformatics in viral research.

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

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.

SegVir: Reconstruction of Complete Segmented RNA Viral Genomes from Metatranscriptomes

Segmented RNA viruses are a complex group of RNA viruses with multisegment genomes. Reconstructing complete segmented viruses is crucial for advancing our understanding of viral diversity, evolution, and public health impact. Using metatranscriptomic data to identify known and novel segmented viruses has sped up the survey of segmented viruses in various ecosystems. However, the high genetic diversity and the difficulty in binning complete segmented genomes present significant challenges in segmented virus reconstruction. Current virus detection tools are primarily used to identify nonsegmented viral genomes. This study presents SegVir, a novel tool designed to identify segmented RNA viruses and reconstruct their complete genomes from complex metatranscriptomes. SegVir leverages both close and remote homology searches to accurately detect conserved and divergent viral segments. Additionally, we introduce a new method that can evaluate the genome completeness and conservation based on gene content. Our evaluations on simulated datasets demonstrate SegVir's superior sensitivity and precision compared to existing tools. Moreover, in experiments using real data, we identified some virus segments missing in the NCBI database, underscoring SegVir's potential to enhance viral metagenome analysis. The source code and supporting data of SegVir are available via https://github.com/HubertTang/SegVir.

Exploring viral diversity and metagenomics in livestock: insights into disease emergence and spillover risks in cattle

Cattle have a significant impact on human societies in terms of both economics and health. Viral infections pose a relevant problem as they directly or indirectly disrupt the balance within cattle populations. This has negative consequences at the economic level for producers and territories, and also jeopardizes human health through the transmission of zoonotic diseases that can escalate into outbreaks or pandemics. To establish prevention strategies and control measures at various levels (animal, farm, region, or global), it is crucial to identify the viral agents present in animals. Various techniques, including virus isolation, serological tests, and molecular techniques like PCR, are typically employed for this purpose. However, these techniques have two major drawbacks: they are ineffective for non-culturable viruses, and they only detect a small fraction of the viruses present. In contrast, metagenomics offers a promising approach by providing a comprehensive and unbiased analysis for detecting all viruses in a given sample. It has the potential to identify rare or novel infectious agents promptly and establish a baseline of healthy animals. Nevertheless, the routine application of viral metagenomics for epidemiological surveillance and diagnostics faces challenges related to socioeconomic variables, such as resource availability and space dedicated to metagenomics, as well as the lack of standardized protocols and resulting heterogeneity in presenting results. This review aims to provide an overview of the current knowledge and prospects for using viral metagenomics to detect and identify viruses in cattle raised for livestock, while discussing the epidemiological and clinical implications.

Prophage-DB: A comprehensive database to explore diversity, distribution, and ecology of prophages

Background

Viruses that infect prokaryotes (phages) constitute the most abundant group of biological agents, playing pivotal roles in microbial systems. They are known to impact microbial community dynamics, microbial ecology, and evolution. Efforts to document the diversity, host range, infection dynamics, and effects of bacteriophage infection on host cell metabolism are extremely underexplored. Phages are classified as virulent or temperate based on their life cycles. Temperate phages adopt the lysogenic mode of infection, where the genome integrates into the host cell genome forming a prophage. Prophages enable viral genome replication without host cell lysis, and often contribute novel and beneficial traits to the host genome. Current phage research predominantly focuses on lytic phages, leaving a significant gap in knowledge regarding prophages, including their biology, diversity, and ecological roles.

Results

Here we develop and describe Prophage-DB, a database of prophages, their proteins, and associated metadata that will serve as a resource for viral genomics and microbial ecology. To create the database, we identified and characterized prophages from genomes in three of the largest publicly available databases. We applied several state-of-the-art tools in our pipeline to annotate these viruses, cluster and taxonomically classify them, and detect their respective auxiliary metabolic genes. In total, we identify and characterize over 350,000 prophages and 35,000 auxiliary metabolic genes. Our prophage database is highly representative based on statistical results and contains prophages from a diverse set of archaeal and bacterial hosts which show a wide environmental distribution.

Conclusion

Prophages are particularly overlooked in viral ecology and merit increased attention due to their vital implications for microbiomes and their hosts. Here, we created Prophage-DB to advance our comprehension of prophages in microbiomes through a comprehensive characterization of prophages in publicly available genomes. We propose that Prophage-DB will serve as a valuable resource for advancing phage research, offering insights into viral taxonomy, host relationships, auxiliary metabolic genes, and environmental distribution.

Effects of vegetation cover and aquaculture pollution on viral assemblages in mangroves sediments

Mangrove forests, a critical coastal ecosystem, face numerous anthropogenic threats, particularly from aquaculture activities. Despite the acknowledged significance of viruses in local and global biogeochemical cycles, there is limited knowledge regarding the community structure, genomic diversity, and ecological roles of viruses in mangrove forests ecosystems, especially regarding their responses to aquaculture. In this study, we identified 17,755 viral operational taxonomic units (vOTUs) from nine sediments viromes across three distinct ecological regions of the mangrove forests ecosystem: mangrove, bare flat, and aquaculture regions. Viral assemblages varied among three regions, and the pathogenic viruses associated with marine animals, such as the white spot syndrome virus (WSSV) from Nimaviridae, were identified in this study. The relative abundance of Nimaviridae in the bare flat region was higher than in other regions. Furthermore, viruses in distinct mangrove forests sediments regions have adapted to their environments by adopting distinct survival strategies and encoding various auxiliary metabolic genes involved in carbon metabolism and antibiotic resistance. These adaptations may have profound impacts on biogeochemical cycles. This study provides the first insights into the effects of vegetation cover and aquaculture on the community structure and ecological roles of viruses in mangrove forests sediments. These findings are crucial for understanding the risks posed by anthropogenic threats to mangrove forests ecosystems and informing effective management strategies.

Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family

PURPOSE

In this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is achieved through a deep neural network model that relies on convolutional neural networks (CNNs). Since viruses within the same family share similar genetic and structural characteristics, the classification process becomes more challenging, necessitating more robust models. With the rapid evolution of viral genomes and the increasing need for timely classification, we aimed to provide a robust and efficient tool that could increase the accuracy of viral identification and classification processes. Contribute to advancing research in viral genomics and assist in surveilling emerging viral strains.

METHODS

Based on a one-dimensional deep CNN, the proposed tool is capable of training and testing on the Coronaviridae family, including SARS-CoV-2. Our model's performance was assessed using various metrics, including F1-score and AUROC. Additionally, artificial mutation tests were conducted to evaluate the model's generalization ability across sequence variations. We also used the BLAST algorithm and conducted comprehensive processing time analyses for comparison.

RESULTS

DeepVirusClassifier demonstrated exceptional performance across several evaluation metrics in the training and testing phases. Indicating its robust learning capacity. Notably, during testing on more than 10,000 viral sequences, the model exhibited a more than 99% sensitivity for sequences with fewer than 2000 mutations. The tool achieves superior accuracy and significantly reduced processing times compared to the Basic Local Alignment Search Tool algorithm. Furthermore, the results appear more reliable than the work discussed in the text, indicating that the tool has great potential to revolutionize viral genomic research.

CONCLUSION

DeepVirusClassifier is a powerful tool for accurately classifying viral sequences, specifically focusing on SARS-CoV-2 and other subtypes within the Coronaviridae family. The superiority of our model becomes evident through rigorous evaluation and comparison with existing methods. Introducing artificial mutations into the sequences demonstrates the tool's ability to identify variations and significantly contributes to viral classification and genomic research. As viral surveillance becomes increasingly critical, our model holds promise in aiding rapid and accurate identification of emerging viral strains.

VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes

Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.

A global atlas of soil viruses reveals unexplored biodiversity and potential biogeochemical impacts

Historically neglected by microbial ecologists, soil viruses are now thought to be critical to global biogeochemical cycles. However, our understanding of their global distribution, activities and interactions with the soil microbiome remains limited. Here we present the Global Soil Virus Atlas, a comprehensive dataset compiled from 2,953 previously sequenced soil metagenomes and composed of 616,935 uncultivated viral genomes and 38,508 unique viral operational taxonomic units. Rarefaction curves from the Global Soil Virus Atlas indicate that most soil viral diversity remains unexplored, further underscored by high spatial turnover and low rates of shared viral operational taxonomic units across samples. By examining genes associated with biogeochemical functions, we also demonstrate the viral potential to impact soil carbon and nutrient cycling. This study represents an extensive characterization of soil viral diversity and provides a foundation for developing testable hypotheses regarding the role of the virosphere in the soil microbiome and global biogeochemistry.

Metagenomic analysis reveals ecological and functional signatures of oral phageome associated with severe early childhood caries

OBJECTIVES

Severe early childhood caries (S-ECC) is highly prevalent, affecting children's oral health. S-ECC development is closely associated with the complex oral microbial microbiome and its microorganism interactions, such as the imbalance of bacteriophages and bacteria. Till now, little is known about oral phageome on S-ECC. Therefore, this study aimed to investigate the potential role of the oral phageome in the pathogenesis of S-ECC.

METHODS

Unstimulated saliva (2 mL) was collected from 20 children with and without S-ECC for metagenomics analysis. Metagenomics sequencing and bioinformatic analysis were performed to determine the two groups' phageome diversity, taxonomic and functional annotations. Statistical analysis and visualization were performed with R and SPSS Statistics software.

RESULTS

85.7 % of the extracted viral sequences were predicted from phages, in which most phages were classified into Myoviridae, Siphoviridae, and Podoviridae. Alpha diversity decreased, and Beta diversity increased in the S-ECC phageome compared to the healthy group. The abundance of Podoviridae phages increased, and the abundance of Inoviridae, Herelleviridae, and Streptococcus phages decreased in the S-ECC group. Functional annotation revealed increased annotation on glycoside hydrolases and nucleotide metabolism, decreased glycosyl transferases, carbohydrate-binding modules, and biogenic metabolism in the S-ECC phageome.

CONCLUSIONS

Metagenomic analysis revealed reduced Streptococcus phages and significant changes in functional annotations within the S-ECC phageome. These findings suggest a potential weakening of the regulatory influence of oral bacteria, which may indicate the development of innovative prevention and treatment strategies for S-ECC. These implications deserve further investigation and hold promise for advancing our understanding and management of S-ECC.

CLINICAL SIGNIFICANCE

The findings of this study indicate that oral phageomes are associated with bacterial genomes and metabolic processes, affecting the development of S-ECC. The reduced modulatory effect of the oral phageome in counteracting S-ECC's cariogenic activity suggests a new avenue for the prevention and treatment of S-ECC.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling

BACKGROUND

Pan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.

RESULTS

Here, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage's utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage's capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.

CONCLUSIONS

Entourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

Distinct characteristics of the gut virome in patients with osteoarthritis and gouty arthritis

BACKGROUND/PURPOSE(S)

The gut microbiota and its metabolites play crucial roles in pathogenesis of arthritis, highlighting gut microbiota as a promising avenue for modulating autoimmunity. However, the characterization of the gut virome in arthritis patients, including osteoarthritis (OA) and gouty arthritis (GA), requires further investigation.

METHODS

We employed virus-like particle (VLP)-based metagenomic sequencing to analyze gut viral community in 20 OA patients, 26 GA patients, and 31 healthy controls, encompassing a total of 77 fecal samples.

RESULTS

Our analysis generated 6819 vOTUs, with a considerable proportion of viral genomes differing from existing catalogs. The gut virome in OA and GA patients differed significantly from healthy controls, showing variations in diversity and viral family abundances. We identified 157 OA-associated and 94 GA-associated vOTUs, achieving high accuracy in patient-control discrimination with random forest models. OA-associated viruses were predicted to infect pro-inflammatory bacteria or bacteria associated with immunoglobulin A production, while GA-associated viruses were linked to Bacteroidaceae or Lachnospiraceae phages. Furthermore, several viral functional orthologs displayed significant differences in frequency between OA-enriched and GA-enriched vOTUs, suggesting potential functional roles of these viruses. Additionally, we trained classification models based on gut viral signatures to effectively discriminate OA or GA patients from healthy controls, yielding AUC values up to 0.97, indicating the clinical utility of the gut virome in diagnosing OA or GA.

CONCLUSION

Our study highlights distinctive alterations in viral diversity and taxonomy within gut virome of OA and GA patients, offering insights into arthritis etiology and potential treatment and prevention strategies.

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 500 bp 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.

VIBES: a workflow for annotating and visualizing viral sequences integrated into bacterial genomes

Bacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists. Some prophages have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES (Viral Integrations in Bacterial genomES), a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab-separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES's primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1072 Pseudomonas spp. genomes.

Tools and methodology to in silico phage discovery in freshwater environments

Freshwater availability is essential, and its maintenance has become an enormous challenge. Due to population growth and climate changes, freshwater sources are becoming scarce, imposing the need for strategies for its reuse. Currently, the constant discharge of waste into water bodies from human activities leads to the dissemination of pathogenic bacteria, negatively impacting water quality from the source to the infrastructure required for treatment, such as the accumulation of biofilms. Current water treatment methods cannot keep pace with bacterial evolution, which increasingly exhibits a profile of multidrug resistance to antibiotics. Furthermore, using more powerful disinfectants may affect the balance of aquatic ecosystems. Therefore, there is a need to explore sustainable ways to control the spreading of pathogenic bacteria. Bacteriophages can infect bacteria and archaea, hijacking their host machinery to favor their replication. They are widely abundant globally and provide a biological alternative to bacterial treatment with antibiotics. In contrast to common disinfectants and antibiotics, bacteriophages are highly specific, minimizing adverse effects on aquatic microbial communities and offering a lower cost-benefit ratio in production compared to antibiotics. However, due to the difficulty involving cultivating and identifying environmental bacteriophages, alternative approaches using NGS metagenomics in combination with some bioinformatic tools can help identify new bacteriophages that can be useful as an alternative treatment against resistant bacteria. In this review, we discuss advances in exploring the virome of freshwater, as well as current applications of bacteriophages in freshwater treatment, along with current challenges and future perspectives.

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

Characterizations of gut bacteriome, mycobiome, and virome of healthy individuals living in sea-level and high-altitude areas

BACKGROUND

The contribution of gut microbiota to human high-altitude adaptation remains inadequately understood.

METHODS

Here a comparative analysis of gut microbiota was conducted between healthy individuals living at sea level and high altitude using deep whole-metagenome shotgun sequencing, to investigate the adaptive mechanisms of gut microbiota in plateau inhabitants.

RESULTS

The results showed the gut bacteriomes in high-altitude individuals exhibited greater within-sample diversity and significant alterations in both bacterial compositional and functional profiles when compared to those of sea-level individuals, indicating the potential selection of unique bacteria associated with high-altitude environments. The strain-level investigation revealed enrichment of Collinsella aerofaciens and Akkermansia muciniphila in high-altitude populations. The characteristics of gut virome and gut mycobiome were also investigated. Compared to sea-level subjects, high-altitude subjects exhibited a greater diversity in their gut virome, with an increased number of viral operational taxonomic units (vOTUs) and unique annotated genes. Finally, correlation analyses revealed 819 significant correlations between 42 bacterial species and 375 vOTUs, while no significant correlations were observed between bacteria and fungi or between fungi and viruses.

CONCLUSION

The findings have significantly contributed to an enhanced comprehension of the mechanisms underlying the high-altitude geographic adaptation of the human gut microbiota.

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

There are an estimated 300,000 mammalian viruses from which infectious diseases in humans may arise. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify putative novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

The bacteriome-coupled phage communities continuously contract and shift to orchestrate the traditional rice vinegar fermentation

Amounts of microbiome studies have uncovered the microbial communities of traditional food fermentations, while in which the phageome development with time is poorly understood. Here, we conducted a study to decipher both phageome and bacteriome of the traditional rice vinegar fermentation. The vinegar phageomes showed significant differences in the alpha diversity, network density and clustering coefficient over time. Peduoviridae had the highest relative abundance. Moreover, the phageome negatively correlated to the cognate bacteriome in alpha diversity, and undergone constantly contracting and shifting across the temporal scale. Nevertheless, 257 core virial clusters (VCs) persistently occurred with time whatever the significant impacts imposed by the varied physiochemical properties. Glycoside hydrolase (GH) and glycosyltransferase (GT) families genes displayed the higher abundances across all samples. Intriguingly, diversely structuring of toxin-antitoxin systems (TAs) and CRISPR-Cas arrays were frequently harbored by phage genomes. Their divergent organization and encoding attributes underlie the multiple biological roles in modulation of network and/or contest of phage community as well as bacterial host community. This phageome-wide mapping will fuel the current insights of phage community ecology in other traditional fermented ecosystems that are challenging to decipher.

A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction

Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative 'genome graph' can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual's infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.

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

BACKGROUND

As 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.

RESULTS

We 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.

CONCLUSIONS

Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Developmental Dynamics of the Gut Virome in Tibetan Pigs at High Altitude: A Metagenomic Perspective across Age Groups

Tibetan pig is a geographically isolated pig breed that inhabits high-altitude areas of the Qinghai-Tibetan plateau. At present, there is limited research on viral diseases in Tibetan pigs. This study provides a novel metagenomic exploration of the gut virome in Tibetan pigs (altitude ≈ 3000 m) across three critical developmental stages, including lactation, nursery, and fattening. The composition of viral communities in the Tibetan pig intestine, with a dominant presence of Microviridae phages observed across all stages of development, in combination with the previous literature, suggest that it may be associated with geographical locations with high altitude. Functional annotation of viral operational taxonomic units (vOTUs) highlights that, among the constantly increasing vOTUs groups, the adaptability of viruses to environmental stressors such as salt and heat indicates an evolutionary response to high-altitude conditions. It shows that the lactation group has more abundant viral auxiliary metabolic genes (vAMGs) than the nursery and fattening groups. During the nursery and fattening stages, this leaves only DNMT1 at a high level. which may be a contributing factor in promoting gut health. The study found that viruses preferentially adopt lytic lifestyles at all three developmental stages. These findings not only elucidate the dynamic interplay between the gut virome and host development, offering novel insights into the virome ecology of Tibetan pigs and their adaptation to high-altitude environments, but also provide a theoretical basis for further studies on pig production and epidemic prevention under extreme environmental conditions.

A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments

BACKGROUND

Ruminants are important livestock animals that have a unique digestive system comprising multiple stomach compartments. Despite significant progress in the study of microbiome in the gastrointestinal tract (GIT) sites of ruminants, we still lack an understanding of the viral community of ruminants. Here, we surveyed its viral ecology using 2333 samples from 10 sites along the GIT of 8 ruminant species.

RESULTS

We present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the GITs of ruminants including 64,922 non-redundant phage genomes. We characterized the distributions of the phage genomes in different ruminants and GIT sites and found that most phages were organism-specific. We revealed that ~ 60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phages, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic archaea.

CONCLUSIONS

The URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Phages have great potential for controlling pathogenic bacterial/archaeal species and reducing methane emissions. Our findings provide insights into the virome ecology research of the ruminant GIT and offer a starting point for future research on phage therapy in ruminants. Video Abstract.

Phage-inclusive profiling of human gut microbiomes with Phanta

Due to technical limitations, most gut microbiome studies have focused on prokaryotes, overlooking viruses. Phanta, a virome-inclusive gut microbiome profiling tool, overcomes the limitations of assembly-based viral profiling methods by using customized k-mer-based classification tools and incorporating recently published catalogs of gut viral genomes. Phanta's optimizations consider the small genome size of viruses, sequence homology with prokaryotes and interactions with other gut microbes. Extensive testing of Phanta on simulated data demonstrates that it quickly and accurately quantifies prokaryotes and viruses. When applied to 245 fecal metagenomes from healthy adults, Phanta identifies ~200 viral species per sample, ~5× more than standard assembly-based methods. We observe a ~2:1 ratio between DNA viruses and bacteria, with higher interindividual variability of the gut virome compared to the gut bacteriome. In another cohort, we observe that Phanta performs equally well on bulk versus virus-enriched metagenomes, making it possible to study prokaryotes and viruses in a single experiment, with a single analysis.

Efficient Recovery of Complete Gut Viral Genomes by Combined Short- and Long-Read Sequencing

Current metagenome assembled human gut phage catalogs contained mostly fragmented genomes. Here, comprehensive gut virome detection procedure is developed involving virus-like particle (VLP) enrichment from ≈500 g feces and combined sequencing of short- and long-read. Applied to 135 samples, a Chinese Gut Virome Catalog (CHGV) is assembled consisting of 21,499 non-redundant viral operational taxonomic units (vOTUs) that are significantly longer than those obtained by short-read sequencing and contained ≈35% (7675) complete genomes, which is ≈nine times more than those in the Gut Virome Database (GVD, ≈4%, 1,443). Interestingly, the majority (≈60%, 13,356) of the CHGV vOTUs are obtained by either long-read or hybrid assemblies, with little overlap with those assembled from only the short-read data. With this dataset, vast diversity of the gut virome is elucidated, including the identification of 32% (6,962) novel vOTUs compare to public gut virome databases, dozens of phages that are more prevalent than the crAssphages and/or Gubaphages, and several viral clades that are more diverse than the two. Finally, the functional capacities are also characterized of the CHGV encoded proteins and constructed a viral-host interaction network to facilitate future research and applications.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

VirusPredictor: XGBoost-based software to predict virus-related sequences in human data

MOTIVATION

Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data.

RESULTS

We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150-350 (Illumina short reads), 850-950 (Sanger sequencing data), and 2000-5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to >0.98 when query sequences increased from 150-350 to >850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients' unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions.

AVAILABILITY AND IMPLEMENTATION

www.dllab.org/software/VirusPredictor.html.