VirusSeeker, a computational pipeline for virus discovery and virome composition analysis

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.

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.

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.

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods

Understanding the ecological impacts of viruses on natural and engineered ecosystems relies on the accurate identification of viral sequences from community sequencing data. To maximize viral recovery from metagenomes, researchers frequently combine viral identification tools. However, the effectiveness of this strategy is unknown. Here, we benchmarked combinations of six widely used informatics tools for viral identification and analysis (VirSorter, VirSorter2, VIBRANT, DeepVirFinder, CheckV, and Kaiju), called "rulesets." Rulesets were tested against mock metagenomes composed of taxonomically diverse sequence types and diverse aquatic metagenomes to assess the effects of the degree of viral enrichment and habitat on tool performance. We found that six rulesets achieved equivalent accuracy [Matthews Correlation Coefficient (MCC) = 0.77, Padj ≥ 0.05]. Each contained VirSorter2, and five used our "tuning removal" rule designed to remove non-viral contamination. While DeepVirFinder, VIBRANT, and VirSorter were each found once in these high-accuracy rulesets, they were not found in combination with each other: combining tools does not lead to optimal performance. Our validation suggests that the MCC plateau at 0.77 is partly caused by inaccurate labeling within reference sequence databases. In aquatic metagenomes, our highest MCC ruleset identified more viral sequences in virus-enriched (44%-46%) than in cellular metagenomes (7%-19%). While improved algorithms may lead to more accurate viral identification tools, this should be done in tandem with careful curation of sequence databases. We recommend using the VirSorter2 ruleset and our empirically derived tuning removal rule. Our analysis provides insight into methods for in silico viral identification and will enable more robust viral identification from metagenomic data sets.

IMPORTANCE

The identification of viruses from environmental metagenomes using informatics tools has offered critical insights in microbial ecology. However, it remains difficult for researchers to know which tools optimize viral recovery for their specific study. In an attempt to recover more viruses, studies are increasingly combining the outputs from multiple tools without validating this approach. After benchmarking combinations of six viral identification tools against mock metagenomes and environmental samples, we found that these tools should only be combined cautiously. Two to four tool combinations maximized viral recovery and minimized non-viral contamination compared with either the single-tool or the five- to six-tool ones. By providing a rigorous overview of the behavior of in silico viral identification strategies and a pipeline to replicate our process, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.

Virome profiling of fig wasps (Ceratosolen spp.) reveals virus diversity spanning four realms

We investigated the virome of agaonid fig wasps (Ceratosolen spp.) inside syconia ("fruits") of various Ficus trees fed upon by frugivores such as pteropodid bats in Sub-Saharan Africa. This virome includes representatives of viral families spanning four realms and includes near-complete genome sequences of three novel viruses and fragments of five additional potentially novel viruses evolutionarily associated with insects, fungi, plants, and vertebrates. Our study provides evidence that frugivorous animals are exposed to a plethora of viruses by coincidental consumption of fig wasps, which are obligate pollinators of figs worldwide.

Hecatomb: an integrated software platform for viral metagenomics

BACKGROUND

Modern sequencing technologies offer extraordinary opportunities for virus discovery and virome analysis. Annotation of viral sequences from metagenomic data requires a complex series of steps to ensure accurate annotation of individual reads and assembled contigs. In addition, varying study designs will require project-specific statistical analyses.

FINDINGS

Here we introduce Hecatomb, a bioinformatic platform coordinating commonly used tasks required for virome analysis. Hecatomb means "a great sacrifice." In this setting, Hecatomb is "sacrificing" false-positive viral annotations using extensive quality control and tiered-database searches. Hecatomb processes metagenomic data obtained from both short- and long-read sequencing technologies, providing annotations to individual sequences and assembled contigs. Results are provided in commonly used data formats useful for downstream analysis. Here we demonstrate the functionality of Hecatomb through the reanalysis of a primate enteric and a novel coral reef virome.

CONCLUSION

Hecatomb provides an integrated platform to manage many commonly used steps for virome characterization, including rigorous quality control, host removal, and both read- and contig-based analysis. Each step is managed using the Snakemake workflow manager with dependency management using Conda. Hecatomb outputs several tables properly formatted for immediate use within popular data analysis and visualization tools, enabling effective data interpretation for a variety of study designs. Hecatomb is hosted on GitHub (github.com/shandley/hecatomb) and is available for installation from Bioconda and PyPI.

Does the Human Gut Virome Contribute to Host Health or Disease?

The human gastrointestinal (GI) tract harbors eukaryotic and prokaryotic viruses and their genomes, metabolites, and proteins, collectively known as the "gut virome". This complex community of viruses colonizing the enteric mucosa is pivotal in regulating host immunity. The mechanisms involved in cross communication between mucosal immunity and the gut virome, as well as their relationship in health and disease, remain largely unknown. Herein, we review the literature on the human gut virome's composition and evolution and the interplay between the gut virome and enteric mucosal immunity and their molecular mechanisms. Our review suggests that future research efforts should focus on unraveling the mechanisms of gut viruses in human homeostasis and pathophysiology and on developing virus-prompted precision therapies.

Novel Echarate Virus Variant Isolated from Patient with Febrile Illness, Chanchamayo, Peru

A new phlebovirus variant was isolated from an acute febrile patient in Chanchamayo, Peru. Genome characterization and p-distance analyses based on complete open reading frames revealed that the virus is probably a natural reassortant of the Echarate virus (large and small segments) with a yet-unidentified phlebovirus (M segment).

Metagenomics of gut microbiome for migratory seagulls in Kunming city revealed the potential public risk to human health

BACKGROUND

Seagull as a migratory wild bird has become most popular species in southwest China since 1980s. Previously, we analyzed the gut microbiota and intestinal pathogenic bacteria configuration for this species by using 16S rRNA sequencing and culture methods. To continue in-depth research on the gut microbiome of migratory seagulls, the metagenomics, DNA virome and RNA virome were both investigated for their gut microbial communities of abundance and diversity in this study.

RESULTS

The metagenomics results showed 99.72% of total species was bacteria, followed by viruses, fungi, archaea and eukaryota. In particular, Shigella sonnei, Escherichia albertii, Klebsiella pneumonia, Salmonella enterica and Shigella flexneri were the top distributed taxa at species level. PCoA, NMDS, and statistics indicated some drug resistant genes, such as adeL, evgS, tetA, PmrF, and evgA accumulated as time went by from November to January of the next year, and most of these genes were antibiotic efflux. DNA virome composition demonstrated that Caudovirales was the most abundance virus, followed by Cirlivirales, Geplafuvirales, Petitvirales and Piccovirales. Most of these phages corresponded to Enterobacteriaceae and Campylobacteriaceae bacterial hosts respectively. Caliciviridae, Coronaviridae and Picornaviridae were the top distributed RNA virome at family level of this migratory animal. Phylogenetic analysis indicated the sequences of contigs of Gammacoronavirus and Deltacoronavirus had highly similarity with some coronavirus references.

CONCLUSIONS

In general, the characteristics of gut microbiome of migratory seagulls were closely related to human activities, and multiomics still revealed the potential public risk to human health.

Gauge your phage: benchmarking of bacteriophage identification tools in metagenomic sequencing data

BACKGROUND

The prediction of bacteriophage sequences in metagenomic datasets has become a topic of considerable interest, leading to the development of many novel bioinformatic tools. A comparative analysis of ten state-of-the-art phage identification tools was performed to inform their usage in microbiome research.

METHODS

Artificial contigs generated from complete RefSeq genomes representing phages, plasmids, and chromosomes, and a previously sequenced mock community containing four phage species, were used to evaluate the precision, recall, and F1 scores of the tools. We also generated a dataset of randomly shuffled sequences to quantify false-positive calls. In addition, a set of previously simulated viromes was used to assess diversity bias in each tool's output.

RESULTS

VIBRANT and VirSorter2 achieved the highest F1 scores (0.93) in the RefSeq artificial contigs dataset, with several other tools also performing well. Kraken2 had the highest F1 score (0.86) in the mock community benchmark by a large margin (0.3 higher than DeepVirFinder in second place), mainly due to its high precision (0.96). Generally, k-mer-based tools performed better than reference similarity tools and gene-based methods. Several tools, most notably PPR-Meta, called a high number of false positives in the randomly shuffled sequences. When analysing the diversity of the genomes that each tool predicted from a virome set, most tools produced a viral genome set that had similar alpha- and beta-diversity patterns to the original population, with Seeker being a notable exception.

CONCLUSIONS

This study provides key metrics used to assess performance of phage detection tools, offers a framework for further comparison of additional viral discovery tools, and discusses optimal strategies for using these tools. We highlight that the choice of tool for identification of phages in metagenomic datasets, as well as their parameters, can bias the results and provide pointers for different use case scenarios. We have also made our benchmarking dataset available for download in order to facilitate future comparisons of phage identification tools. Video Abstract.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

Genomic Characterization of a Relative of Mumps Virus in Lesser Dawn Bats of Southeast Asia

The importance of genomic surveillance on emerging diseases continues to be highlighted with the ongoing SARS-CoV-2 pandemic. Here, we present an analysis of a new bat-borne mumps virus (MuV) in a captive colony of lesser dawn bats (Eonycteris spelaea). This report describes an investigation of MuV-specific data originally collected as part of a longitudinal virome study of apparently healthy, captive lesser dawn bats in Southeast Asia (BioProject ID PRJNA561193) which was the first report of a MuV-like virus, named dawn bat paramyxovirus (DbPV), in bats outside of Africa. More in-depth analysis of these original RNA sequences in the current report reveals that the new DbPV genome shares only 86% amino acid identity with the RNA-dependent RNA polymerase of its closest relative, the African bat-borne mumps virus (AbMuV). While there is no obvious immediate cause for concern, it is important to continue investigating and monitoring bat-borne MuVs to determine the risk of human infection.

Metagenomics analysis reveals potential pathways and drivers of piglet gut phage-mediated transfer of ARGs

The growing prevalence of antibiotic-resistant pathogens has led to a better understanding of the underlying processes that lead to this expansion. Intensive pig farms are considered one of the hotspots for antibiotic resistance gene (ARG) transmission. Phages, as important mobile carriers of ARGs, are widespread in the animal intestine. However, our understanding of phage-associated ARGs in the pig intestine and their underlying drivers is limited. Here, metagenomic sequencing and analysis of viral DNA and total DNA of different intestinal (ileum, cecum and feces) contents in healthy piglets and piglets with diarrhea were separately conducted. We found that phages in piglet ceca are the main repository for ARGs and mobile genetic element (MGE) genes. Phage-associated MGEs are important factors affecting the maintenance and transfer of ARGs. Interestingly, the colocalization of ARGs and MGE genes in piglet gut phages does not appear to be randomly selected but rather related to a specific phage host (Streptococcus). In addition, in the feces of piglets with diarrhea, the abundance of phages carrying ARGs and MGE genes was significantly increased, as was the diversity of polyvalent phages (phages with broad host ranges), which would facilitate the transfection and wider distribution of ARGs in the bacterial community. Moreover, the predicted host spectrum of polyvalent phages in diarrheal feces tended to be potential enteropathogenic genera, which greatly increased the risk of enteropathogens acquiring ARGs. Notably, we also found ARG-homologous genes in the sequences of piglet intestinal mimiviruses, suggesting that the piglet intestinal mimiviruses are a potential repository of ARGs. In conclusion, this study greatly expands our knowledge of the piglet gut microbiome, revealing the underlying mechanisms of maintenance and dissemination of piglet gut ARGs and providing a reference for the prevention and control of ARG pollution in animal husbandry.

Enhanced Viral Metagenomics with Lazypipe 2

Viruses are the main agents causing emerging and re-emerging infectious diseases. It is therefore important to screen for and detect them and uncover the evolutionary processes that support their ability to jump species boundaries and establish themselves in new hosts. Metagenomic next-generation sequencing (mNGS) is a high-throughput, impartial technology that has enabled virologists to detect either known or novel, divergent viruses from clinical, animal, wildlife and environmental samples, with little a priori assumptions. mNGS is heavily dependent on bioinformatic analysis, with an emerging demand for integrated bioinformatic workflows. Here, we present Lazypipe 2, an updated mNGS pipeline with, as compared to Lazypipe1, significant improvements in code stability and transparency, with added functionality and support for new software components. We also present extensive benchmarking results, including evaluation of a novel canine simulated metagenome, precision and recall of virus detection at varying sequencing depth, and a low to extremely low proportion of viral genetic material. Additionally, we report accuracy of virus detection with two strategies: homology searches using nucleotide or amino acid sequences. We show that Lazypipe 2 with nucleotide-based annotation approaches near perfect detection for eukaryotic viruses and, in terms of accuracy, outperforms the compared pipelines. We also discuss the importance of homology searches with amino acid sequences for the detection of highly divergent novel viruses.

Evaluation of computational phage detection tools for metagenomic datasets

Introduction

As new computational tools for detecting phage in metagenomes are being rapidly developed, a critical need has emerged to develop systematic benchmarks.

Methods

In this study, we surveyed 19 metagenomic phage detection tools, 9 of which could be installed and run at scale. Those 9 tools were assessed on several benchmark challenges. Fragmented reference genomes are used to assess the effects of fragment length, low viral content, phage taxonomy, robustness to eukaryotic contamination, and computational resource usage. Simulated metagenomes are used to assess the effects of sequencing and assembly quality on the tool performances. Finally, real human gut metagenomes and viromes are used to assess the differences and similarities in the phage communities predicted by the tools.

Results

We find that the various tools yield strikingly different results. Generally, tools that use a homology approach (VirSorter, MARVEL, viralVerify, VIBRANT, and VirSorter2) demonstrate low false positive rates and robustness to eukaryotic contamination. Conversely, tools that use a sequence composition approach (VirFinder, DeepVirFinder, Seeker), and MetaPhinder, have higher sensitivity, including to phages with less representation in reference databases. These differences led to widely differing predicted phage communities in human gut metagenomes, with nearly 80% of contigs being marked as phage by at least one tool and a maximum overlap of 38.8% between any two tools. While the results were more consistent among the tools on viromes, the differences in results were still significant, with a maximum overlap of 60.65%. Discussion: Importantly, the benchmark datasets developed in this study are publicly available and reusable to enable the future comparability of new tools developed.

Advances in the field of phage-based therapy with special emphasis on computational resources

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

Altered vaginal eukaryotic virome is associated with different cervical disease status

Viruses are important components of the human body. Growing evidence suggests that they are engaged in the physiology and disease status of the host. Even though the vaginal microbiome is involved in human papillomavirus (HPV) infection and cervical cancer (CC) progression, little is known about the role of the vaginal virome. In this pilot exploratory study, using unbiased viral metagenomics, we aim to investigate the vaginal eukaryotic virome in women with different levels of cervical lesions, and examine their associations with different cervical disease status. An altered eukaryotic virome was observed in women with different levels of lesions and Lactobacillus profiles. Anelloviruses and papillomaviruses are the most commonly detected eukaryotic viruses of the vaginal virome. Higher abundance and richness of anelloviruses and papillomaviruses were associated with low-grade squamous intraepithelial lesion (LSIL) and CC. Besides, higher anellovirus abundance was also associated with lactobacillus-depleted microbiome profiles and bacterial community state (CST) type IV. Furthermore, increased correlations between Anelloviridae and Papillomaviridae occurred in the women with increased cervical disease severity level from LSIL to CC. These data suggest underlying interactions between different microbes as well as the host physiology. Higher abundance and diversity of both anelloviruses and papillomaviruses shared by LSIL and CC suggest that anellovirus may be used as a potential adjunct biomarker to predict the risk of HPV persistent infection and/or CC. Future studies need to focus on the clinical relevance of anellovirus abundance with cervical disease status, and the evaluation of their potential as a new adjunct biomarker for the prediction and prognoses of CC.

Viroscope: Plant viral diagnosis from high-throughput sequencing data using biologically-informed genome assembly coverage

High-throughput sequencing (HTS) methods are transforming our capacity to detect pathogens and perform disease diagnosis. Although sequencing advances have enabled accessible and point-of-care HTS, data analysis pipelines have yet to provide robust tools for precise and certain diagnosis, particularly in cases of low sequencing coverage. Lack of standardized metrics and harmonized detection thresholds confound the problem further, impeding the adoption and implementation of these solutions in real-world applications. In this work, we tackle these issues and propose biologically-informed viral genome assembly coverage as a method to improve diagnostic certainty. We use the identification of viral replicases, an essential function of viral life cycles, to define genome coverage thresholds in which biological functions can be described. We validate the analysis pipeline, Viroscope, using field samples, synthetic and published datasets, and demonstrate that it provides sensitive and specific viral detection. Furthermore, we developed Viroscope.io a web-service to provide on-demand HTS data viral diagnosis to facilitate adoption and implementation by phytosanitary agencies to enable precise viral diagnosis.

Phage-prokaryote coexistence strategy mediates microbial community diversity in the intestine and sediment microhabitats of shrimp culture pond ecosystem

Emerging evidence supports that the phage-prokaryote interaction drives ecological processes in various environments with different phage life strategies. However, the knowledge of phage-prokaryote interaction in the shrimp culture pond ecosystem (SCPE) is still limited. Here, the viral and prokaryotic community profiles at four culture stages in the intestine of Litopenaeus vannamei and cultural sediment microhabitats of SCPE were explored to elucidate the contribution of phage-prokaryote interaction in modulating microbial communities. The results demonstrated that the most abundant viral families in the shrimp intestine and sediment were Microviridae, Circoviridae, Inoviridae, Siphoviridae, Podoviridae, Myoviridae, Parvoviridae, Herelleviridae, Mimiviridae, and Genomoviridae, while phages dominated the viral community. The dominant prokaryotic genera were Vibrio, Formosa, Aurantisolimonas, and Shewanella in the shrimp intestine, and Formosa, Aurantisolimonas, Algoriphagus, and Flavobacterium in the sediment. The viral and prokaryotic composition of the shrimp intestine and sediment were significantly different at four culture stages, and the phage communities were closely related to the prokaryotic communities. Moreover, the phage-prokaryote interactions can directly or indirectly modulate the microbial community composition and function, including auxiliary metabolic genes and closed toxin genes. The interactional analysis revealed that phages and prokaryotes had diverse coexistence strategies in the shrimp intestine and sediment microhabitats of SCPE. Collectively, our findings characterized the composition of viral communities in the shrimp intestine and cultural sediment and revealed the distinct pattern of phage-prokaryote interaction in modulating microbial community diversity, which expanded our cognization of the phage-prokaryote coexistence strategy in aquatic ecosystems from the microecological perspective and provided theoretical support for microecological prevention and control of shrimp culture health management.

Novel technologies to characterize and engineer the microbiome in inflammatory bowel disease

We present an overview of recent experimental and computational advances in technology used to characterize the microbiome, with a focus on how these developments improve our understanding of inflammatory bowel disease (IBD). Specifically, we present studies that make use of flow cytometry and metabolomics assays to provide a functional characterization of microbial communities. We also describe computational methods for strain-level resolution, temporal series, mycobiome and virome data, co-occurrence networks, and compositional data analysis. In addition, we review novel techniques to therapeutically manipulate the microbiome in IBD. We discuss the benefits and drawbacks of these technologies to increase awareness of specific biases, and to facilitate a more rigorous interpretation of results and their potential clinical application. Finally, we present future lines of research to better characterize the relation between microbial communities and IBD pathogenesis and progression.

A comprehensive review and meta-analysis of recent advances in biotechnology for plant virus research and significant accomplishments in human health and the pharmaceutical industry

Secondary metabolites made by plants and used through their metabolic routes are today's most reliable and cost-effective way to make pharmaceuticals and improve health. The concept of genetic engineering is used for molecular pharming. As more people use plants as sources of nanotechnology systems, they are adding to this. These systems are made up of viruses-like particles (VLPs) and virus nanoparticles (VNPs). Due to their superior ability to be used as plant virus expression vectors, plant viruses are becoming more popular in pharmaceuticals. This has opened the door for them to be used in research, such as the production of medicinal peptides, antibodies, and other heterologous protein complexes. This is because biotechnological approaches have been linked with new bioinformatics tools. Because of the rise of high-throughput sequencing (HTS) and next-generation sequencing (NGS) techniques, it has become easier to use metagenomic studies to look for plant virus genomes that could be used in pharmaceutical research. A look at how bioinformatics can be used in pharmaceutical research is also covered in this article. It also talks about plant viruses and how new biotechnological tools and procedures have made progress in the field.

PathoLive—Real-Time Pathogen Identification from Metagenomic Illumina Datasets

Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda.

ViBE: a hierarchical BERT model to identify eukaryotic viruses using metagenome sequencing data

Viruses are ubiquitous in humans and various environments and continually mutate themselves. Identifying viruses in an environment without cultivation is challenging; however, promoting the screening of novel viruses and expanding the knowledge of viral space is essential. Homology-based methods that identify viruses using known viral genomes rely on sequence alignments, making it difficult to capture remote homologs of the known viruses. To accurately capture viral signals from metagenomic samples, models are needed to understand the patterns encoded in the viral genomes. In this study, we developed a hierarchical BERT model named ViBE to detect eukaryotic viruses from metagenome sequencing data and classify them at the order level. We pre-trained ViBE using read-like sequences generated from the virus reference genomes and derived three fine-tuned models that classify paired-end reads to orders for eukaryotic deoxyribonucleic acid viruses and eukaryotic ribonucleic acid viruses. ViBE achieved higher recall than state-of-the-art alignment-based methods while maintaining comparable precision. ViBE outperformed state-of-the-art alignment-free methods for all test cases. The performance of ViBE was also verified using real sequencing datasets, including the vaginal virome.

Revolutionized virome research using systems microbiology approaches

Currently, both pathogenic and commensal viruses are continuously being discovered and acknowledged as ubiquitous components of microbial communities. The advancements of systems microbiological approaches have changed the face of virome research. Here, we focus on viral metagenomic approach to study virus community and their interactions with other microbial members as well as their hosts. This review also summarizes challenges, limitations, and benefits of the current virome approaches. Potentially, the studies of virome can be further applied in various biological and clinical fields.

The Chronic Wound Phageome: Phage Diversity and Associations with Wounds and Healing Outcomes

Two leading impediments to chronic wound healing are polymicrobial infection and biofilm formation. Recent studies have characterized the bacterial fraction of these microbiomes and have begun to elucidate compositional correlations to healing outcomes. However, the factors that drive compositional shifts are still being uncovered. The virome may play an important role in shaping bacterial community structure and function. Previous work on the skin virome determined that it was dominated by bacteriophages, viruses that infect bacteria. To characterize the virome, we enrolled 20 chronic wound patients presenting at an outpatient wound care clinic in a microbiome survey, collecting swab samples from healthy skin and chronic wounds (diabetic, venous, arterial, or pressure) before and after a single, sharp debridement procedure. We investigated the virome using a virus-like particle enrichment procedure, shotgun metagenomic sequencing, and a k-mer-based, reference-dependent taxonomic classification method. Taxonomic composition, diversity, and associations with covariates are presented. We find that the wound virome is highly diverse, with many phages targeting known pathogens, and may influence bacterial community composition and functionality in ways that impact healing outcomes.

IMPORTANCE Chronic wounds are an increasing medical burden. These wounds are known to be rich in microbial content, including both bacteria and bacterial viruses (phages). The viruses may play an important role in shaping bacterial community structure and function. We analyzed the virome and bacterial composition of 20 patients with chronic wounds. The viruses found in wounds are highly diverse compared to normal skin, unlike the bacterial composition, where diversity is decreased. These data represent an initial look at this relatively understudied component of the chronic wound microbiome and may help inform future phage-based interventions.

VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data

Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

Stochastic neutral drifts seem prevalent in driving human virome assembly: neutral, near-neutral and non-neutral theoretic analyses

It is estimated that human body is inhabited by approximately 380 trillions of viruses, which exist in the form of viral communities and are collectively termed as human virome. How virome is assembled and what kind of forces maintain the composition and diversity of viral communities is still an open question. The question is of obvious importance because of its implications to human health and diseases. Here we address the question by harnessing the power of Hubbell’s unified neutral theory of biodiversity (UNTB) in terms of three neutral models including standard Hubbell’s neutral model (HNM), Sloan’s near-neutral model (SNM) and Harris et al. (2017) multi-site neutral model (MSN), further augmented by Ning et al. (2019) normalized stochasticity ratio (NSR) and Hammal et al. (2015) power analysis for the neutral test (PNT). With the five models applied to 179 virome samples, we aim to obtain robust findings given both Type-I and Type-II errors are addressed and possible alternative, non-neutral processes are detected. It was found that stochastic neutral drifts seem prevalent: approximately 65–92% at metacommunity/landscape scales and 67–80% at virus species scale. The non-neutral selection is approximately 26–28% at community scale and 23% at species scale. The false negative rate is about 2–3%, which suggested rather limited confounding effects of non-neutral process on neutrality tests. We postulate that prevalence of neutrality in human virome is likely due to extremely simple structure of viruses (stands of DNA/RNA) and their inter-species homogeneities, forming the foundation of species equivalence—the hallmark of neutral theory.

Transkingdom Analysis of the Female Reproductive Tract Reveals Bacteriophages form Communities

The female reproductive tract (FRT) microbiome plays a vital role in maintaining vaginal health. Viruses are key regulators of other microbial ecosystems, but little is known about how the FRT viruses (virome), particularly bacteriophages that comprise the phageome, impact FRT health and dysbiosis. We hypothesize that bacterial vaginosis (BV) is associated with altered FRT phageome diversity, transkingdom interplay, and bacteriophage discriminate taxa. Here, we conducted a retrospective, longitudinal analysis of vaginal swabs collected from 54 BV-positive and 46 BV-negative South African women. Bacteriome analysis revealed samples clustered into five distinct bacterial community groups (CGs), and further, bacterial alpha diversity was significantly associated with BV. Virome analysis on a subset of baseline samples showed FRT bacteriophages clustering into novel viral state types (VSTs), a viral community clustering system based on virome composition and abundance. Distinct BV bacteriophage signatures included increased alpha diversity along with discriminant Bacillus, Burkholderia, and Escherichia bacteriophages. Bacteriophage-bacteria transkingdom associations were also identified between Bacillus and Burkholderia viruses and BV-associated bacteria, providing key insights for future studies elucidating the transkingdom interactions driving BV-associated microbiome perturbations. In this cohort, bacteriophage-bacterial associations suggest complex interactions, which may play a role in the establishment and maintenance of BV.

Metagenome-Assembled Viral Genomes Analysis Reveals Diversity and Infectivity of the RNA Virome of Gerbillinae Species

Rodents are a known reservoir for extensive zoonotic viruses, and also possess a propensity to roost in human habitation. Therefore, it is necessary to identify and catalogue the potentially emerging zoonotic viruses that are carried by rodents. Here, viral metagenomic sequencing was used for zoonotic virus detection and virome characterization on 32 Great gerbils of Rhombomys opimus, Meriones meridianus, and Meiiones Unguiculataus species in Xinjiang, Northwest China. In total, 1848 viral genomes that are potentially pathogenic to rodents and humans, as well as to other wildlife, were identified namely Retro-, Flavi-, Pneumo-, Picobirna-, Nairo-, Arena-, Hepe-, Phenui-, Rhabdo-, Calici-, Reo-, Corona-, Orthomyxo-, Peribunya-, and Picornaviridae families. In addition, a new genotype of rodent Hepacivirus was identified in heart and lung homogenates of seven viscera pools and phylogenetic analysis revealed the closest relationship to rodent Hepacivirus isolate RtMm-HCV/IM2014 that was previously reported to infect rodents from Inner Mongolia, China. Moreover, nine new genotype viral sequences that corresponded to Picobirnaviruses (PBVs), which have a bi-segmented genome and belong to the family Picobirnaviridae, comprising of three segment I and six segment II sequences, were identified in intestines and liver of seven viscera pools. In the two phylogenetic trees that were constructed using ORF1 and ORF2 of segment I, the three segment I sequences were clustered into distinct clades. Additionally, phylogenetic analysis showed that PBV sequences were distributed in the whole tree that was constructed using the RNA-dependent RNA polymerase (RdRp) gene of segment II with high diversity, sharing 68.42-82.67% nucleotide identities with other genogroup I and genogroup II PBV strains based on the partial RdRp gene. By RNA sequencing, we found a high degree of biodiversity of Retro-, Flavi-, Pneumo-, and Picobirnaridae families and other zoonotic viruses in gerbils, indicating that zoonotic viruses are a common presence in gerbils from Xinjiang, China. Therefore, further research is needed to determine the zoonotic potential of these viruses that are carried by other rodent species from different ecosystems and wildlife in general.

LABRADOR-A Computational Workflow for Virus Detection in High-Throughput Sequencing Data

High-throughput sequencing (HTS) allows detection of known and unknown viruses in samples of broad origin. This makes HTS a perfect technology to determine whether or not the biological products, such as vaccines are free from the adventitious agents, which could support or replace extensive testing using various in vitro and in vivo assays. Due to bioinformatics complexities, there is a need for standardized and reliable methods to manage HTS generated data in this field. Thus, we developed LABRADOR—an analysis pipeline for adventitious virus detection. The pipeline consists of several third-party programs and is divided into two major parts: (i) direct reads classification based on the comparison of characteristic profiles between reads and sequences deposited in the database supported with alignment of to the best matching reference sequence and (ii) de novo assembly of contigs and their classification on nucleotide and amino acid levels. To meet the requirements published in guidelines for biologicals’ safety we generated a custom nucleotide database with viral sequences. We tested our pipeline on publicly available HTS datasets and showed that LABRADOR can reliably detect viruses in mixtures of model viruses, vaccines and clinical samples.

The gut virome in inflammatory bowel diseases

Dysbiosis of the microbiome has been extensively studied in inflammatory bowel diseases (IBD). The roles of bacteria and fungi have been studied in detail, but viral communities, an important component of the microbiome, have been less thoroughly investigated. Metagenomics provided a way to fill this gap by using DNA sequencing to enumerate all viruses in a sample, termed the 'virome'. Such methods have now been employed in several studies to assess associations between viral communities and IBD, yielding several commonly seen properties, including an increase in tailed bacteriophage (Caudovirales) and a decrease in the spherical Microviridae. Numerous studies of single human viruses have been carried out, but no one virus has emerged as tightly associated, focusing attention on whole virome communities and further factors. This review provides an overview of research on the human virome in IBD, with emphasis on (1) dynamics of the gut virome, (2) candidate mechanisms of virome alterations with disease, (3) methods for studying the virome, and (4) potentially actionable implications of virome data.

Contamination Issue in Viral Metagenomics: Problems, Solutions, and Clinical Perspectives

We describe the most common internal and external sources and types of contamination encountered in viral metagenomic studies and discuss their negative impact on sequencing results, particularly for low-biomass samples and clinical applications. We also propose some basic recommendations for reducing the background noise in viral shotgun metagenomic (SM) studies, which would limit the bias introduced by various classes of contaminants. Regardless of the specific viral SM protocol, contamination cannot be totally avoided; in particular, the issue of reagent contamination should always be addressed with high priority. There is an urgent need for the development and validation of standards for viral metagenomic studies especially if viral SM protocols will be more widely applied in diagnostics.

Correcting the Estimation of Viral Taxa Distributions in Next-Generation Sequencing Data after Applying Artificial Neural Networks

Estimating the taxonomic composition of viral sequences in a biological samples processed by next-generation sequencing is an important step in comparative metagenomics. Mapping sequencing reads against a database of known viral reference genomes, however, fails to classify reads from novel viruses whose reference sequences are not yet available in public databases. Instead of a mapping approach, and in order to classify sequencing reads at least to a taxonomic level, the performance of artificial neural networks and other machine learning models was studied. Taxonomic and genomic data from the NCBI database were used to sample labelled sequencing reads as training data. The fitted neural network was applied to classify unlabelled reads of simulated and real-world test sets. Additional auxiliary test sets of labelled reads were used to estimate the conditional class probabilities, and to correct the prior estimation of the taxonomic distribution in the actual test set. Among the taxonomic levels, the biological order of viruses provided the most comprehensive data base to generate training data. The prediction accuracy of the artificial neural network to classify test reads to their viral order was considerably higher than that of a random classification. Posterior estimation of taxa frequencies could correct the primary classification results.

Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

Current challenges to virus discovery by meta-transcriptomics

Meta-transcriptomic next-generation sequencing has transformed virus discovery, dramatically expanding our knowledge of the known virosphere. Nevertheless, the use of meta-transcriptomics for virus discovery faces important challenges. As this technology becomes more widely adopted, the proportion of viral sequences in public databases with incorrect (e.g. mis-assignment of host) or limited information (e.g. lacking taxonomic classification) is likely to grow, limiting their utility in bioinformatic pipelines for virus discovery. In addition, we currently lack the bioinformatic tools that can accurately identify viruses showing little or no sequence similarity to database viruses or those that represent likely reagent contaminants. Herein, we outline some of the challenges to effective meta-transcriptomic virus discovery as well as their potential solutions.

Divergent Enteroviruses from Macaques with Chronic Diarrhea

We report the draft genome sequences of five novel members of the family Picornaviridae that were isolated from the stool of rhesus macaques (Macaca mulatta) with chronic diarrhea. The strains were named NOLA-1 through NOLA-5 because the macaques were residents of the Tulane National Primate Research Center.

Integrated diversity and shared species analyses of human viromes

Diversity analysis has been performed routinely on microbiomes, including human viromes. Shared species analysis has been conducted only rarely, but it can be a powerful supplement to diversity analysis. In the present study, we conducted integrated diversity and shared species analyses of human viromes by reanalyzing three published datasets of human viromes with more than 250 samples from healthy vs. diseased individuals and/or rural vs. urban individuals. We found significant differences in the virome diversity measured in the Hill numbers between the healthy and diseased individuals, with diseased individuals exhibiting higher virome diversity than healthy individuals, and rural individual exhibiting higher virome diversity than urban individuals. We applied both "read randomization" and "sample randomization" algorithms to perform shared species analysis. With the more conservative sample randomization algorithm, the observed number of shared species was significantly smaller than the expected shared species in 50% (8 of 16) of the comparisons. These results suggest that integrated diversity and shared species analysis can offer more comprehensive insights in comparing human virome samples than standard diversity analysis alone with potentially powerful applications in differentiating the effects of diseases or other meta-factors.

Virome studies of food production systems: time for 'farm to fork' analyses

The food industry is under increasing pressure to produce high quality, traceable and minimally processed foods that are produced using sustainable approaches and ingredients. In line with the latter, there is an increased pressure for plant-based products to replace animal-derived products. Until recently, research efforts have mainly focused on dairy and meat products owing to their economic importance. The shift towards plant-based diets and food production requires a corresponding shift in research efforts to define the microbial requirements for and composition of (novel) plant-based foods, the (micro)organisms that are beneficial to such production systems, and the abundance and role of (bacterio)phages in shaping the microbial landscape of these foods. In this review, we explore current efforts in the area of virome analysis of foods and food production environments and highlight the need for more unified approaches to understand the contribution of phages in food safety and quality, and to develop novel tools to enhance the traceability of foods.

[Characteristics of gut virome and microbiome in patients with stroke]

OBJECTIVE

To explore the differences in gut virome and microbiome between patients with stroke and healthy volunteers.

OBJECTIVE

Fifteen patients with acute ischemic stroke treated in the Department of Neurology of Nanfang Hospital between February, 2014 and February, 2016 and 15 healthy volunteers matched for age and sex were enrolled in this study. Virome sequencing and 16S rRNA sequencing were performed on stool samples of all the participants, and the composition and structures of the virome and microbiome were compared between the two groups.

OBJECTIVE

No significant difference was found in the overall diversity of virome between the stroke patients and the healthy volunteers (alpha diversity: P=0.320; beta diversity: P=0.169, R2=0.037), but virome composition differed significantly between the two groups. The relative abundance of Bacteroides phage B40_8 and Cronobacter phage CS01 increased significantly in patients with stroke. The structures and composition of the microbiome in patients with stroke also differed significantly from those of the healthy volunteers (alpha diversity: P=0.950; beta diversity: P=0.005, R2=0.117). The relative abundance of Megasphaera increased while that of Bifidobacterium decreased in patients with stroke. Correlation analysis showed that in the virome of stroke patients, the relative abundance of the phage preying Streptococcus was positively correlated with that of their hosts (r=0.550, P=0.036), while in the virome of healthy volunteers, the relative abundance of the phage preying Faecalibacterium (r=0.520, P=0.049), Bilophila (r=0.541, P=0.040) and Roseburia (r=0.526, P=0.046) were positively correlated with that of their respective hosts.

OBJECTIVE

Stroke patients have similar overall diversity of the virome to healthy volunteers but different virome composition and interaction patterns between the virome and microbiome. The gut microbiome also differs between stroke patients and healthy volunteers. The relative abundance of opportunistic pathogens increases but that of symbiotic bacteria decreases in stroke patients.

Spatial heterogeneity analysis of the human virome with Taylor's power law

Spatial heterogeneity is a fundamental characteristic of organisms from viruses to humans. Measuring heterogeneity is challenging, especially for naked-eye invisible viruses, but of obvious importance. For example, spatial heterogeneity of virus distribution may strongly influence infection spreading and outbreaks in the case of pathogenic viruses; the spatial distribution (i.e., the inter-subject heterogeneity) of commensal viruses within/on our bodies can influence the competition, coexistence, and dispersal of viruses within or between our bodies. Taylor's power law (TPL) was first discovered in the 1960s to describe the spatial distributions of plant and/or animal populations, and since then it has been verified by numerous experimental and theoretical studies. Recently, TPL has been extended from population to community level and applied to bacterial communities. Here we report the first comprehensive testing of the TPL fitted to human virome datasets. It was found that the human virome follows the TPL as bacterial communities do. Furthermore, the TPL heterogeneity scaling parameter of human virome is virtually the same as that of the human bacterial microbiome (1.916 vs. 1.926). We postulate that the extreme closeness of human viruses and bacteria in heterogeneity scaling coefficients could be attributed to the fact that most of the viruses that were annotated in this study actually belong to bacteriophages (86% viral OTUs) that "piggyback" on their bacterial hosts, and their distributions are likely host-dependent. The scaling parameter, which measures the inter-subject heterogeneity changes, should be an innate property of human microbiomes including both bacteria and viruses. It is similar to the acceleration coefficient of the gravity (g = 9.8) as specified by Newton's law, which is invariant on the earth. Nevertheless, we caution that our postulation is contingent on an implicit assumption that the proportion of bacteriophages to total virome may not change significantly when more virus species can be identified in future.

k-mer-Based Metagenomics Tools Provide a Fast and Sensitive Approach for the Detection of Viral Contaminants in Biopharmaceutical and Vaccine Manufacturing Applications Using Next-Generation Sequencing

Adventitious agent detection during the production of vaccines and biotechnology-based medicines is of critical importance to ensure the final product is free from any possible viral contamination. Increasing the speed and accuracy of viral detection is beneficial as a means to accelerate development timelines and to ensure patient safety. Here, several rapid viral metagenomics approaches were tested on simulated next-generation sequencing (NGS) data sets and existing data sets from virus spike-in studies done in CHO-K1 and HeLa cell lines. It was observed that these rapid methods had comparable sensitivity to full-read alignment methods used for NGS viral detection for these data sets, but their specificity could be improved. A method that first filters host reads using KrakenUniq and then selects the virus classification tool based on the number of remaining reads is suggested as the preferred approach among those tested to detect nonlatent and nonendogenous viruses. Such an approach shows reasonable sensitivity and specificity for the data sets examined and requires less time and memory as full-read alignment methods.

IMPORTANCE Next-generation sequencing (NGS) has been proposed as a complementary method to detect adventitious viruses in the production of biotherapeutics and vaccines to current in vivo and in vitro methods. Before NGS can be established in industry as a main viral detection technology, further investigation into the various aspects of bioinformatics analyses required to identify and classify viral NGS reads is needed. In this study, the ability of rapid metagenomics tools to detect viruses in biopharmaceutical relevant samples is tested and compared to recommend an efficient approach. The results showed that KrakenUniq can quickly and accurately filter host sequences and classify viral reads and had comparable sensitivity and specificity to slower full read alignment approaches, such as BLASTn, for the data sets examined.

Inter-Individual Diversity Scaling Analysis of the Human Virome With Classic Diversity-Area Relationship (DAR) Modeling

The human virome is a critical component of the human microbiome, and it is believed to hold the richest diversity within human microbiomes. Yet, the inter-individual scaling (changes) of the human virome has not been formally investigated to the best of our knowledge. Here we fill the gap by applying diversity-area relationship (DAR) modeling (a recent extension to the classic species-area law in biodiversity and biogeography research) for analyzing four large datasets of the human virome with three DAR profiles: DAR scaling (z)-measuring the inter-individual heterogeneity in virome diversity, MAD (maximal accrual diversity: D max ) and LGD ratio (ratio of local diversity to global diversity)-measuring the percentage of individual to population level diversity. Our analyses suggest: (i) The diversity scaling parameter (z) is rather resilient against the diseases as indicated by the lack of significant differences between the healthy and diseased treatments. (ii) The potential maximal accrual diversity (D max ) is less resilient and may vary between the healthy and diseased groups or between different body sites. (iii) The LGD ratio of bacterial communities is much smaller than for viral communities, and relates to the comparatively greater heterogeneity between local vs. global diversity levels found for bacterial-biomes.

Towards the validation of high-throughput sequencing (HTS) for routine plant virus diagnostics: measurement of variation linked to HTS detection of citrus viruses and viroids

BACKGROUND

High-throughput sequencing (HTS) has been applied successfully for virus and viroid discovery in many agricultural crops leading to the current drive to apply this technology in routine pathogen detection. The validation of HTS-based pathogen detection is therefore paramount.

METHODS

Plant infections were established by graft inoculating a suite of viruses and viroids from established sources for further study. Four plants (one healthy plant and three infected) were sampled in triplicate and total RNA was extracted using two different methods (CTAB extraction protocol and the Zymo Research Quick-RNA Plant Miniprep Kit) and sent for Illumina HTS. One replicate sample of each plant for each RNA extraction method was also sent for HTS on an Ion Torrent platform. The data were evaluated for biological and technical variation focussing on RNA extraction method, platform used and bioinformatic analysis.

RESULTS

The study evaluated the influence of different HTS protocols on the sensitivity, specificity and repeatability of HTS as a detection tool. Both extraction methods and sequencing platforms resulted in significant differences between the data sets. Using a de novo assembly approach, complemented with read mapping, the Illumina data allowed a greater proportion of the expected pathogen scaffolds to be inferred, and an accurate virome profile was constructed. The complete virome profile was also constructed using the Ion Torrent data but analyses showed that more sequencing depth is required to be comparative to the Illumina protocol and produce consistent results. The CTAB extraction protocol lowered the proportion of viroid sequences recovered with HTS, and the Zymo Research kit resulted in more variation in the read counts obtained per pathogen sequence. The expression profiles of reference genes were also investigated to assess the suitability of these genes as internal controls to allow for the comparison between samples across different protocols.

CONCLUSIONS

This study highlights the need to measure the level of variation that can arise from the different variables of an HTS protocol, from sample preparation to data analysis. HTS is more comprehensive than any assay previously used, but with the necessary validations and standard operating procedures, the implementation of HTS as part of routine pathogen screening practices is possible.

ViroMatch: A Computational Pipeline for the Detection of Viral Sequences from Complex Metagenomic Data

ViroMatch is an automated pipeline that takes metagenomic sequencing reads as input and performs iterative nucleotide and translated nucleotide mapping to identify viral sequences. We provide a Docker image for ViroMatch, so that users will not have to install dependencies.

Endogenization of a Prosimian Retrovirus during Lemur Evolution

Studies of viruses that coevolved with lemurs provide an opportunity to understand the basal traits of primate viruses and provide an evolutionary context for host-virus interactions. Germline integration of endogenous retroviruses (ERVs) are fossil evidence of past infections. Hence, characterization of novel ERVs provides insight into the ancient precursors of extant viruses and the evolutionary history of their hosts. Here, we report the discovery of a novel endogenous retrovirus present in the genome of a lemur, Coquerel's sifaka (Propithecus coquereli). Using next-generation sequencing, we identified and characterized the complete genome sequence of a retrovirus, named prosimian retrovirus 1 (PSRV1). Phylogenetic analyses indicate that PSRV1 is a gamma-type betaretrovirus basal to the other primate betaretroviruses and most closely related to simian retroviruses. Molecular clock analysis of PSRV1 long terminal repeat (LTR) sequences estimated the time of endogenization within 4.56 MYA (± 2.4 MYA), placing it after the divergence of Propithecus species. These results indicate that PSRV1 is an important milestone of lemur evolution during the radiation of the Propithecus genus. These findings may have implications for both human and animal health in that the acquisition of a gamma-type env gene within an endogenized betaretrovirus could facilitate a cross-species jump between vertebrate class hosts.

V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data

Motivation

High-throughput sequencing technologies are used increasingly not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations.

Results

To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape.

Availabilityand implementation

V-pipe is freely available at https://github.com/cbg-ethz/V-pipe.

Supplementary information

Supplementary data are available at Bioinformatics online.

The human virome: assembly, composition and host interactions

The human body hosts vast microbial communities, termed the microbiome. Less well known is the fact that the human body also hosts vast numbers of different viruses, collectively termed the 'virome'. Viruses are believed to be the most abundant and diverse biological entities on our planet, with an estimated 10³¹ particles on Earth. The human virome is similarly vast and complex, consisting of approximately 10¹³ particles per human individual, with great heterogeneity. In recent years, studies of the human virome using metagenomic sequencing and other methods have clarified aspects of human virome diversity at different body sites, the relationships to disease states and mechanisms of establishment of the human virome during early life. Despite increasing focus, it remains the case that the majority of sequence data in a typical virome study remain unidentified, highlighting the extent of unexplored viral 'dark matter'. Nevertheless, it is now clear that viral community states can be associated with adverse outcomes for the human host, whereas other states are characteristic of health. In this Review, we provide an overview of research on the human virome and highlight outstanding recent studies that explore the assembly, composition and dynamics of the human virome as well as host-virome interactions in health and disease.

Novel NGS pipeline for virus discovery from a wide spectrum of hosts and sample types

The study of the microbiome data holds great potential for elucidating the biological and metabolic functioning of living organisms and their role in the environment. Metagenomic analyses have shown that humans, along with for example, domestic animals, wildlife and arthropods, are colonized by an immense community of viruses. The current Coronavirus pandemic (COVID-19) heightens the need to rapidly detect previously unknown viruses in an unbiased way. The increasing availability of metagenomic data in this era of next-generation sequencing (NGS), along with increasingly affordable sequencing technologies, highlight the need for reliable and comprehensive methods to manage such data. In this article, we present a novel bioinformatics pipeline called LAZYPIPE for identifying both previously known and novel viruses in host associated or environmental samples and give examples of virus discovery based on it. LAZYPIPE is a Unix-based pipeline for automated assembling and taxonomic profiling of NGS libraries implemented as a collection of C++, Perl, and R scripts.

Relatives of rubella virus in diverse mammals

Since 1814, when rubella was first described, the origins of the disease and its causative agent, rubella virus (Matonaviridae: Rubivirus), have remained unclear1. Here we describe ruhugu virus and rustrela virus in Africa and Europe, respectively, which are, to our knowledge, the first known relatives of rubella virus. Ruhugu virus, which is the closest relative of rubella virus, was found in apparently healthy cyclops leaf-nosed bats (Hipposideros cyclops) in Uganda. Rustrela virus, which is an outgroup to the clade that comprises rubella and ruhugu viruses, was found in acutely encephalitic placental and marsupial animals at a zoo in Germany and in wild yellow-necked field mice (Apodemus flavicollis) at and near the zoo. Ruhugu and rustrela viruses share an identical genomic architecture with rubella virus2,3. The amino acid sequences of four putative B cell epitopes in the fusion (E1) protein of the rubella, ruhugu and rustrela viruses and two putative T cell epitopes in the capsid protein of the rubella and ruhugu viruses are moderately to highly conserved4-6. Modelling of E1 homotrimers in the post-fusion state predicts that ruhugu and rubella viruses have a similar capacity for fusion with the host-cell membrane5. Together, these findings show that some members of the family Matonaviridae can cross substantial barriers between host species and that rubella virus probably has a zoonotic origin. Our findings raise concerns about future zoonotic transmission of rubella-like viruses, but will facilitate comparative studies and animal models of rubella and congenital rubella syndrome.

High-Throughput Sequencing for Deciphering the Virome of Alfalfa (Medicago sativa L.)

Alfalfa (Medicago sativa L.), also known as lucerne, is a major forage crop worldwide. In the United States, it has recently become the third most valuable field crop, with an estimated value of over $9.3 billion. Alfalfa is naturally infected by many different pathogens, including viruses, obligate parasites that reproduce only inside living host cells. Traditionally, viral infections of alfalfa have been considered by breeders, growers, producers and researchers to be diseases of limited importance, although they are widespread in all major cultivation areas. However, over the past few years, due to the rapid development of high-throughput sequencing (HTS), viral metagenomics, bioinformatics tools for interpreting massive amounts of HTS data and the increasing accessibility of public data repositories for transcriptomic discoveries, several emerging viruses of alfalfa with the potential to cause serious yield losses have been described. They include alfalfa leaf curl virus (family Geminiviridae), alfalfa dwarf virus (family Rhabdoviridae), alfalfa enamovirus 1 (family Luteoviridae), alfalfa virus S (family Alphaflexiviridae) and others. These discoveries have called into question the assumed low economic impact of viral diseases in alfalfa and further suggested their possible contribution to the severity of complex infections involving multiple pathogens. In this review, we will focus on viruses of alfalfa recently described in different laboratories on the basis of the above research methodologies.