VIP: an integrated pipeline for metagenomics of virus identification and discovery

Gut virome: New key players in the pathogenesis of inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic inflammatory illness of the intestine. While the mechanism underlying the pathogenesis of IBD is not fully understood, it is believed that a complex combination of host immunological response, environmental exposure, particularly the gut microbiota, and genetic susceptibility represents the major determinants. The gut virome is a group of viruses found in great frequency in the gastrointestinal tract of humans. The gut virome varies greatly among individuals and is influenced by factors including lifestyle, diet, health and disease conditions, geography, and urbanization. The majority of research has focused on the significance of gut bacteria in the progression of IBD, although viral populations represent an important component of the microbiome. We conducted this review to highlight the viral communities in the gut and their expected roles in the etiopathogenesis of IBD regarding published research to date.

Virome capture sequencing for comprehensive HPV genotyping in cervical samples

ObjectiveThis study aims to explore HPV genotyping in the cervical specimen using VirCapSeq by comparing the results with the reverse blot hybridization assay (REBA).MethodsA secondary cross-sectional data of HPV genotypes in 35 cervical specimens was obtained from VirCapSeq and REBA methods. The .FASTQ files were downloaded from the NCBI Sequence Read Archive (SRA) (accession number PRJNA766412) and HPV genotyping was bioinformatically analyzed by mapping the sequences to the PaVE database. HPV genotypes detected by REBA and NGS were compared. All specimens were stratified by histology into cervical intraepithelial neoplasia grades 1 (CIN1) and 2/3 (CIN2/3).ResultsNGS via VirCapSeq detected HPV DNA in 100% of the samples, whereas the REBA (hybridization-based) assay diagnosed HPV DNA in 85.71%. While the limitation of the conventional methods for HPV genotyping is the use of primers or probes, NGS detected a broader range. The results showed that mixed infections were detected in all samples by NGS, with HPV16 and HPV52 being the most abundant genotypes.ConclusionsHPV genome abundance, coverage, and diversity were associated with detection discrepancies between the methods, highlighting the enhanced sensitivity and diagnostic capabilities of NGS. These findings underscore the potential of NGS technologies for comprehensive HPV genotyping, advancing cervical cancer screening, and epidemiological studies. Future research should address cost barriers and expand cohort sizes to validate these findings.

VITALdb: to select the best viroinformatics tools for a desired virus or application

The recent pandemics of viral diseases, COVID-19/mpox (humans) and lumpy skin disease (cattle), have kept us glued to viral research. These pandemics along with the recent human metapneumovirus outbreak have exposed the urgency for early diagnosis of viral infections, vaccine development, and discovery of novel antiviral drugs and therapeutics. To support this, there is an armamentarium of virus-specific computational tools that are currently available. VITALdb (VIroinformatics Tools and ALgorithms database) is a resource of ~360 viroinformatics tools encompassing all major viruses (SARS-CoV-2, influenza virus, human immunodeficiency virus, papillomavirus, herpes simplex virus, hepatitis virus, dengue virus, Ebola virus, Zika virus, etc.) and several diverse applications [structural and functional annotation, antiviral peptides development, subspecies characterization, recognition of viral recombination, inhibitors identification, phylogenetic analysis, virus-host prediction, viral metagenomics, detection of mutation(s), primer designing, etc.]. Resources, tools, and other utilities mentioned in this article will not only facilitate further developments in the realm of viroinformatics but also provide tremendous fillip to translate fundamental knowledge into applied research. Most importantly, VITALdb is an inevitable tool for selecting the best tool(s) to carry out a desired task and hence will prove to be a vital database (VITALdb) for the scientific community. Database URL: https://compbio.iitr.ac.in/vitaldb.

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.

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.

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.

Communicating computational workflows in a regulatory environment

The volume of nucleic acid sequence data has exploded recently, amplifying the challenge of transforming data into meaningful information. Processing data can require an increasingly complex ecosystem of customized tools, which increases difficulty in communicating analyses in an understandable way yet is of sufficient detail to enable informed decisions or repeats. This can be of particular interest to institutions and companies communicating computations in a regulatory environment. BioCompute Objects (BCOs; an instance of pipeline documentation that conforms to the IEEE 2791-2020 standard) were developed as a standardized mechanism for analysis reporting. A suite of BCOs is presented, representing interconnected elements of a computation modeled after those that might be found in a regulatory submission but are shared publicly - in this case a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines.

Discovering and exploring the hidden diversity of human gut viruses using highly enriched virome samples

Viruses are an abundant and crucial component of the human microbiome, but accurately discovering them via metagenomics is still challenging. Currently, the available viral reference genomes poorly represent the diversity in microbiome samples, and expanding such a set of viral references is difficult. As a result, many viruses are still undetectable through metagenomics even when considering the power of de novo metagenomic assembly and binning, as viruses lack universal markers. Here, we describe a novel approach to catalog new viral members of the human gut microbiome and show how the resulting resource improves metagenomic analyses. We retrieved >3,000 viral-like particles (VLP) enriched metagenomic samples (viromes), evaluated the efficiency of the enrichment in each sample to leverage the viromes of highest purity, and applied multiple analysis steps involving assembly and comparison with hundreds of thousands of metagenome-assembled genomes to discover new viral genomes. We reported over 162,000 viral sequences passing quality control from thousands of gut metagenomes and viromes. The great majority of the retrieved viral sequences (~94.4%) were of unknown origin, most had a CRISPR spacer matching host bacteria, and four of them could be detected in >50% of a set of 18,756 gut metagenomes we surveyed. We included the obtained collection of sequences in a new MetaPhlAn 4.1 release, which can quantify reads within a metagenome matching the known and newly uncovered viral diversity. Additionally, we released the viral database for further virome and metagenomic studies of the human microbiome.

A recent gibbon ape leukemia virus germline integration in a rodent from New Guinea

Germline colonization by retroviruses results in the formation of endogenous retroviruses (ERVs). Most colonization's occurred millions of years ago. However, in the Australo-Papuan region (Australia and New Guinea), several recent germline colonization events have been discovered. The Wallace Line separates much of Southeast Asia from the Australo-Papuan region restricting faunal and pathogen dispersion. West of the Wallace Line, gibbon ape leukemia viruses (GALVs) have been isolated from captive gibbons. Two microbat species from China appear to have been infected naturally. East of Wallace's Line, the woolly monkey virus (a GALV) and the closely related koala retrovirus (KoRV) have been detected in eutherians and marsupials in the Australo-Papuan region, often vertically transmitted. The detected vertically transmitted GALV-like viruses in Australo-Papuan fauna compared to sporadic horizontal transmission in Southeast Asia and China suggest the GALV-KoRV clade originates in the former region and further models of early-stage genome colonization may be found. We screened 278 samples, seven bat and one rodent family endemic to the Australo-Papuan region and bat and rodent species found on both sides of the Wallace Line. We identified two rodents (Melomys) from Australia and Papua New Guinea and no bat species harboring GALV-like retroviruses. Melomys leucogaster from New Guinea harbored a genomically complete replication-competent retrovirus with a shared integration site among individuals. The integration was only present in some individuals of the species indicating this retrovirus is at the earliest stages of germline colonization of the Melomys genome, providing a new small wild mammal model of early-stage genome colonization.

Identification of Astrovirus in the virome of the upper and lower respiratory tracts of calves with acute signs of bronchopneumonia

IMPORTANCE

Astroviruses (AstV) are known suspects of enteric disease in humans and livestock. Recently, AstV have been linked to encephalitis in immunocompromised patients and other animals, such as cattle, minks, and swine. In our study, we also identified AstV in the respiratory samples of calves with signs of bronchopneumonia, suggesting that their tropism could be even broader. We obtained one bovine AstV (BAstV) complete genome sequence by next-generation sequencing and showed that respiratory and enteric AstV from different species formed a divergent genetic cluster with AstV isolated from encephalitis cases, indicating that tropism might be strain-specific. These data provide further insight into understanding the biology of these understudied pathogens and suggest BAstV as a potential new candidate for bovine respiratory disease.

ViWrap: A modular pipeline to identify, bin, classify, and predict viral-host relationships for viruses from metagenomes

Viruses are increasingly being recognized as important components of human and environmental microbiomes. However, viruses in microbiomes remain difficult to study because of the difficulty in culturing them and the lack of sufficient model systems. As a result, computational methods for identifying and analyzing uncultivated viral genomes from metagenomes have attracted significant attention. Such bioinformatics approaches facilitate the screening of viruses from enormous sequencing datasets originating from various environments. Though many tools and databases have been developed for advancing the study of viruses from metagenomes, there is a lack of integrated tools enabling a comprehensive workflow and analyses platform encompassing all the diverse segments of virus studies. Here, we developed ViWrap, a modular pipeline written in Python. ViWrap combines the power of multiple tools into a single platform to enable various steps of virus analysis, including identification, annotation, genome binning, species- and genus-level clustering, assignment of taxonomy, prediction of hosts, characterization of genome quality, comprehensive summaries, and intuitive visualization of results. Overall, ViWrap enables a standardized and reproducible pipeline for both extensive and stringent characterization of viruses from metagenomes, viromes, and microbial genomes. Our approach has flexibility in using various options for diverse applications and scenarios, and its modular structure can be easily amended with additional functions as necessary. ViWrap is designed to be easily and widely used to study viruses in human and environmental systems. ViWrap is publicly available via GitHub (https://github.com/AnantharamanLab/ViWrap). A detailed description of the software, its usage, and interpretation of results can be found on the website.

Evolutionarily Young African Rhinoceros Gammaretroviruses

High-throughput sequences were generated from DNA and cDNA from four Southern white rhinoceros (Ceratotherium simum simum) located in the Taronga Western Plain Zoo in Australia. Virome analysis identified reads that were similar to Mus caroli endogenous gammaretrovirus (McERV). Previous analysis of perissodactyl genomes did not recover gammaretroviruses. Our analysis, including the screening of the updated white rhinoceros (Ceratotherium simum) and black rhinoceros (Diceros bicornis) draft genomes identified high-copy orthologous gammaretroviral ERVs. Screening of Asian rhinoceros, extinct rhinoceros, domestic horse, and tapir genomes did not identify related gammaretroviral sequences in these species. The newly identified proviral sequences were designated SimumERV and DicerosERV for the white and black rhinoceros retroviruses, respectively. Two long terminal repeat (LTR) variants (LTR-A and LTR-B) were identified in the black rhinoceros, with different copy numbers associated with each (n = 101 and 373, respectively). Only the LTR-A lineage (n = 467) was found in the white rhinoceros. The African and Asian rhinoceros lineages diverged approximately 16 million years ago. Divergence age estimation of the identified proviruses suggests that the exogenous retroviral ancestor of the African rhinoceros ERVs colonized their genomes within the last 8 million years, a result consistent with the absence of these gammaretroviruses from Asian rhinoceros and other perissodactyls. The black rhinoceros germ line was colonized by two lineages of closely related retroviruses and white rhinoceros by one. Phylogenetic analysis indicates a close evolutionary relationship with ERVs of rodents including sympatric African rats, suggesting a possible African origin of the identified rhinoceros gammaretroviruses. IMPORTANCE Rhinoceros genomes were thought to be devoid of gammaretroviruses, as has been determined for other perissodactyls (horses, tapirs, and rhinoceros). While this may be true of most rhinoceros, the African white and black rhinoceros genomes have been colonized by evolutionarily young gammaretroviruses (SimumERV and DicerosERV for the white and black rhinoceros, respectively). These high-copy endogenous retroviruses (ERVs) may have expanded in multiple waves. The closest relative of SimumERV and DicerosERV is found in rodents, including African endemic species. Restriction of the ERVs to African rhinoceros suggests an African origin for the rhinoceros gammaretroviruses.

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.

Assembly and Annotation of Viral Metagenomes from Short-Read Sequencing Data

Viral metagenomics enables the detection, characterization, and quantification of viral sequences present in shotgun-sequenced datasets of purified virus-like particles and whole metagenomes. Next generation sequencing (Illumina) derived short single or paired-end read runs are a principal platform for metagenomics, and assembly of short reads allows for the identification of distinguishing viral signatures and complex genomic features for taxonomy and functional annotation. Here we describe the identification and characterization of viral genome sequences, bacteriophages, and eukaryotic viruses, from a cohort of human stool samples, using multiple methods. Following the purification of virus-like particles, sequencing, quality refinement, and genome assembly, we begin the protocol with raw short reads deposited in an open-source nucleotide archive. We highlight the use of VIBRANT, an automated computational tool for the characterization of microbial viruses and their viral community function. Finally, we also describe an alternative assembly-free option of mapping reads to established databases of reference genomes and previously characterized metagenome-assembled viral genomes.

ViroProfiler: a containerized bioinformatics pipeline for viral metagenomic data analysis

Bacteriophages play central roles in the maintenance and function of most ecosystems by regulating bacterial communities. Yet, our understanding of their diversity remains limited due to the lack of robust bioinformatics standards. Here we present ViroProfiler, an in-silico workflow for analyzing shotgun viral metagenomic data. ViroProfiler can be executed on a local Linux computer or cloud computing environments. It uses the containerization technique to ensure computational reproducibility and facilitate collaborative research. ViroProfiler is freely available at https://github.com/deng-lab/viroprofiler.

Long-term host-pathogen evolution of endogenous beta- and gammaretroviruses in mouse lemurs with little evidence of recent retroviral introgression

Madagascar's flora and fauna have evolved in relative isolation since the island split from the African and Indian continents. When the last common ancestors of lemurs left Africa between 40 and 70 million years ago, they carried a subset of the viral diversity of the mainland population within them, which continued to evolve throughout the lemur radiation. Relative to other primate radiations, we know very little about the past or present viral diversity of lemurs, particularly mouse lemurs. Using high-throughput sequencing, we identified two gammaretroviruses and three betaretroviruses in the genomes of four species of wild mouse lemurs. The two gammaretroviruses and two betaretroviruses have not previously been described. One betaretrovirus was previously identified. All identified viruses are present in both Lorisiformes and Lemuriformes but absent from haplorrhine primates. The estimated ages of these viruses are consistent with the estimated divergence dates of the host lineages, suggesting they colonized the lemur genome after the Haplorrhine-Strepsirrhine split, but before the Lorisiformes-Lemuriformes split and before the colonization of Madagascar. The viral phylogenies connect multiple lineages of retroviruses from non-lemur and non-Madagascar-native species, suggesting substantial cross-species transmission occurred deep in the primate clade prior to its geographic dispersal. These phylogenies provide novel insights into known retroviral clades. They suggest that the origin of gammaretroviruses in rodents or bats may be premature and that the Jaagsiekte sheep virus clade may be older and more broadly distributed among mammals than previously thought.

Next Generation Sequencing Approaches to Characterize the Respiratory Tract Virome

The COVID-19 pandemic and heightened perception of the risk of emerging viral infections have boosted the efforts to better understand the virome or complete repertoire of viruses in health and disease, with a focus on infectious respiratory diseases. Next-generation sequencing (NGS) is widely used to study microorganisms, allowing the elucidation of bacteria and viruses inhabiting different body systems and identifying new pathogens. However, NGS studies suffer from a lack of standardization, in particular, due to various methodological approaches and no single format for processing the results. Here, we review the main methodological approaches and key stages for studies of the human virome, with an emphasis on virome changes during acute respiratory viral infection, with applications for clinical diagnostics and epidemiologic analyses.

Subclinical hepatitis E virus infection in laboratory ferrets in the UK

Ferrets are widely used for experimental modelling of viral infections. However, background disease in ferrets could potentially confound intended experimental interpretation. Here we report the detection of a subclinical infection of ferret hepatitis E virus (FRHEV) within a colony sub-group of female laboratory ferrets that had been enrolled on an experimental viral infection study (non-hepatitis). Lymphoplasmacytic cuffing of periportal spaces was identified on histopathology but was negative for the RNA and antigens of the administered virus. Follow-up viral metagenomic analysis conducted on liver specimens revealed sequences attributed to FRHEV and these were confirmed by reverse-transcriptase polymerase chain reaction. Further genomic analysis revealed contiguous sequences spanning 79–95 % of the FRHEV genome and that the sequences were closely related to those reported previously in Europe. Using in situ hybridization by RNAScope, we confirmed the presence of HEV-specific RNA in hepatocytes. The HEV open reading frame 2 (ORF2) protein was also detected by immunohistochemistry in the hepatocytes and the biliary canaliculi. In conclusion, the results of our study provide evidence of background infection with FRHEV in laboratory ferrets. As this infection can be subclinical, we recommend routine monitoring of ferret populations using virological and liver function tests to avoid incorrect causal attribution of any liver disease detected in in vivo studies.

Cervical Microbiome in Women Infected with HPV16 and High-Risk HPVs

Human papillomavirus type 16 (HPV16) and/or high-risk (Hr-) HPV are the main causes of cervical cancer. Another element that may contribute to the development of cervical cancer is the microbiota. To date, no study has investigated the entire cervical microbiome, which consists of bacteria, fungi, and viruses. In this study, cervical samples with different histopathology (CIN1, CIN2, and CIN3), with or without HPV16 and Hr-HPVs infection, were enrolled. From bacterial community analysis, 115 bacterial species were found and separated into 2 distinct categories based on Lactobacillus abundance: Lactobacilli-dominated (LD) and non-Lactobacilli-dominated (NLD) groups. The LD group had significantly less bacterial diversity than the NLD group. In addition, the variety of bacteria was contingent on the prevalence of HPV infection. Among distinct histological groups, an abundance of L. iners (>60% of total Lactobacillus spp.) was discovered in both groups. A few fungi, e.g., C. albicans, were identified in the fungal community. The viral community analysis revealed that the presence of HPV considerably reduced the diversity of human viruses. Taken together, when we analyzed all our results collectively, we discovered that HPV infection was a significant determinant in the diversity of bacteria and human viruses in the cervix.

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.

Human Virome in Cervix Controlled by the Domination of Human Papillomavirus

Although other co-viral infections could also be considered influencing factors, cervical human papillomavirus (HPV) infection is the main cause of cervical cancer. Metagenomics have been employed in the NGS era to study the microbial community in each habitat. Thus, in this investigation, virome capture sequencing was used to examine the virome composition in the HPV-infected cervix. Based on the amount of HPV present in each sample, the results revealed that the cervical virome of HPV-infected individuals could be split into two categories: HPV-dominated (HD; ≥60%) and non-HPV-dominated (NHD; <60%). Cervical samples contained traces of several human viral species, including the molluscum contagiosum virus (MCV), human herpesvirus 4 (HHV4), torque teno virus (TTV), and influenza A virus. When compared to the HD group, the NHD group had a higher abundance of several viruses. Human viral diversity appears to be influenced by HPV dominance. This is the first proof that the diversity of human viruses in the cervix is impacted by HPV abundance. However, more research is required to determine whether human viral variety and the emergence of cancer are related.

Target Enrichment Metagenomics Reveals Human Pegivirus-1 in Pediatric Hematopoietic Stem Cell Transplantation Recipients

Human pegivirus-1 (HPgV-1) is a lymphotropic human virus, typically considered nonpathogenic, but its infection can sometimes cause persistent viremia both in immunocompetent and immunosuppressed individuals. In a viral discovery research program in hematopoietic stem cell transplant (HSCT) pediatric patients, HPgV-1 was detected in 3 out of 14 patients (21.4%) using a target enrichment next-generation sequencing method, and the presence of the viruses was confirmed by agent-specific qRT-PCR assays. For the first time in this patient cohort, complete genomes of HPgV-1 were acquired and characterized. Phylogenetic analyses indicated that two patients had HPgV-1 genotype 2 and one had HPgV-1 genotype 3. Intra-host genomic variations were described and discussed. Our results highlight the necessity to screen HSCT patients and blood and stem cell donors to reduce the potential risk of HPgV-1 transmission.

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.

The Neglected Gut Microbiome: Fungi, Protozoa, and Bacteriophages in Inflammatory Bowel Disease

The gut microbiome has been implicated in the pathogenesis of inflammatory bowel disease (IBD). Studies suggest that the IBD gut microbiome is less diverse than that of the unaffected population, a phenomenon often referred to as dysbiosis. However, these studies have heavily focused on bacteria, while other intestinal microorganisms-fungi, protozoa, and bacteriophages-have been neglected. Of the nonbacterial microbes that have been studied in relation to IBD, most are thought to be pathogens, although there is evidence that some of these species may instead be harmless commensals. In this review, we discuss the nonbacterial gut microbiome of IBD, highlighting the current biases, limitations, and outstanding questions that can be addressed with high-throughput DNA sequencing methods. Further, we highlight the importance of studying nonbacterial microorganisms alongside bacteria for a comprehensive view of the whole IBD biome and to provide a more precise definition of dysbiosis in patients. With the rise in popularity of microbiome-altering therapies for the treatment of IBD, such as fecal microbiota transplantation, it is important that we address these knowledge gaps to ensure safe and effective treatment of patients.

Rapid screening and identification of viral pathogens in metagenomic data

BACKGROUND

Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified.

RESULTS

In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references).

CONCLUSIONS

Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).

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.

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

INTRODUCTION

Metagenomic sequencing is increasingly being used in clinical settings for difficult to diagnose cases. The performance of viral metagenomic protocols relies to a large extent on the bioinformatic analysis. In this study, the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS) initiated a benchmark of metagenomic pipelines currently used in clinical virological laboratories.

METHODS

Metagenomic datasets from 13 clinical samples from patients with encephalitis or viral respiratory infections characterized by PCR were selected. The datasets were analyzed with 13 different pipelines currently used in virological diagnostic laboratories of participating ENNGS members. The pipelines and classification tools were: Centrifuge, DAMIAN, DIAMOND, DNASTAR, FEVIR, Genome Detective, Jovian, MetaMIC, MetaMix, One Codex, RIEMS, VirMet, and Taxonomer. Performance, characteristics, clinical use, and user-friendliness of these pipelines were analyzed.

RESULTS

Overall, viral pathogens with high loads were detected by all the evaluated metagenomic pipelines. In contrast, lower abundance pathogens and mixed infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Overall sensitivity ranged from 80% (10/13) to 100% (13/13 datasets). Overall positive predictive value ranged from 71-100%. The majority of the pipelines classified sequences based on nucleotide similarity (8/13), only a minority used amino acid similarity, and 6 of the 13 pipelines assembled sequences de novo. No clear differences in performance were detected that correlated with these classification approaches. Read counts of target viruses varied between the pipelines over a range of 2-3 log, indicating differences in limit of detection.

CONCLUSION

A wide variety of viral metagenomic pipelines is currently used in the participating clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use. Future studies should address the selective effects due to the choice of different reference viral databases.

Recommendations for the introduction of metagenomic next-generation sequencing in clinical virology, part II: bioinformatic analysis and reporting

Metagenomic next-generation sequencing (mNGS) is an untargeted technique for determination of microbial DNA/RNA sequences in a variety of sample types from patients with infectious syndromes. mNGS is still in its early stages of broader translation into clinical applications. To further support the development, implementation, optimization and standardization of mNGS procedures for virus diagnostics, the European Society for Clinical Virology (ESCV) Network on Next-Generation Sequencing (ENNGS) has been established. The aim of ENNGS is to bring together professionals involved in mNGS for viral diagnostics to share methodologies and experiences, and to develop application guidelines. Following the ENNGS publication Recommendations for the introduction of mNGS in clinical virology, part I: wet lab procedure in this journal, the current manuscript aims to provide practical recommendations for the bioinformatic analysis of mNGS data and reporting of results to clinicians.

Metagenomic sequencing reveals viral abundance and diversity in mosquitoes from the Shaanxi-Gansu-Ningxia region, China

BACKGROUND

Mosquitoes host and transmit numerous arthropod-borne viruses (arboviruses) that cause disease in both humans and animals. Effective surveillance of virome profiles in mosquitoes is vital to the prevention and control of mosquito-borne diseases in northwestern China, where epidemics occur frequently.

METHODS

Mosquitoes were collected in the Shaanxi-Gansu-Ningxia region (Shaanxi Province, Gansu Province, and Ningxia Hui Autonomous Region) of China from June to August 2019. Morphological methods were used for taxonomic identification of mosquito species. High-throughput sequencing and metagenomic analysis were used to characterize mosquito viromes.

RESULTS

A total of 22,959 mosquitoes were collected, including Culex pipiens (45.7%), Culex tritaeniorhynchus (40.6%), Anopheles sinensis (8.4%), Aedes (5.2%), and Armigeres subalbatus (0.1%). In total, 3,014,183 (0.95% of clean reads) viral sequences were identified and assigned to 116 viral species (including pathogens such as Japanese encephalitis virus and Getah virus) in 31 viral families, including Flaviviridae, Togaviridae, Phasmaviridae, Phenuiviridae, and some unclassified viruses. Mosquitoes collected in July (86 species in 26 families) showed greater viral diversity than those from June and August. Culex pipiens (69 species in 25 families) and Culex tritaeniorhynchus (73 species in 24 families) carried more viral species than Anopheles sinensis (50 species in 19 families) or Aedes (38 species in 20 families) mosquitoes.

CONCLUSION

Viral diversity and abundance were affected by mosquito species and collection time. The present study elucidates the virome compositions of various mosquito species in northwestern China, improving the understanding of virus transmission dynamics for comparison with those of disease outbreaks.

Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review

For a long time, gut bacteria have been recognized for their important roles in the occurrence and progression of gastrointestinal diseases like colorectal cancer, and the ever-increasing amounts of microbiome data combined with other high-quality clinical and imaging datasets are leading the study of gastrointestinal diseases into an era of biomedical big data. The "omics" technologies used for microbiome analysis continuously evolve, and the machine learning or artificial intelligence technologies are key to extract the relevant information from microbiome data. This review intends to provide a focused summary of recent research and applications of microbiome big data and to discuss the use of artificial intelligence to combat gastrointestinal diseases.

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.

Misclassifications in human papillomavirus databases

We assessed the quality of human papillomavirus (HPV) sequences in GenBank by analyzing the possible presence of chimeras, "wrong-assembled" contigs and errors in taxonomy using an open-source script (HPVChimera_Gb) that compared 25 638 HPV-related nucleotide sequences in GenBank with the 221 numbered HPV types and another 220 complete HPV sequences. There were 110 sequences with taxonomy/naming errors (sequences reported as another HPV type than the one they corresponded to) and 1318 possibly chimeric sequences. Manual analysis found plausible explanations for most of them (e.g. sequence covering an integration site) but 114 sequences appeared to be chimeras (96/114 were already flagged as "unverified" by GenBank) and 13 had taxonomy/naming errors. When comparing all correct HPV sequences in GenBank, there appeared to exist about 800 unique putative HPV types. Systematic and regular work towards eliminating chimeric sequences and taxonomy/naming errors could increase the quality and order in HPV research.

viromeBrowser: A Shiny App for Browsing Virome Sequencing Analysis Results

Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results.

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.

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.

Detection of severe fever with thrombocytopenia syndrome virus and other viruses in cats via unbiased next-generation sequencing

We used unbiased next-generation sequencing (NGS) to detect unknown viruses in cats. Serum or plasma samples were obtained from clinically ill cats with suspected acute viral infections. Nucleic acid was extracted from serum or plasma samples to construct a complementary DNA library for NGS. Comprehensive nucleotide sequencing analyses enabled detection of the genomes of various viruses, including the severe fever with thrombocytopenia syndrome virus, feline immunodeficiency virus, feline morbillivirus, parvovirus, and Torque teno felis virus. Our findings indicate that comprehensive nucleotide analyses of serum or plasma samples can be used to detect infections with unknown viruses in cats.

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.

Coronavirus discovery by metagenomic sequencing: a tool for pandemic preparedness

Introduction

The SARS-CoV-2 pandemic of 2020 is a prime example of the omnipresent threat of emerging viruses that can infect humans. A protocol for the identification of novel coronaviruses by viral metagenomic sequencing in diagnostic laboratories may contribute to pandemic preparedness.

Aim

The aim of this study is to validate a metagenomic virus discovery protocol as a tool for coronavirus pandemic preparedness.

Methods

The performance of a viral metagenomic protocol in a clinical setting for the identification of novel coronaviruses was tested using clinical samples containing SARS-CoV-2, SARS-CoV, and MERS-CoV, in combination with databases generated to contain only viruses of before the discovery dates of these coronaviruses, to mimic virus discovery.

Results

Classification of NGS reads using Centrifuge and Genome Detective resulted in assignment of the reads to the closest relatives of the emerging coronaviruses. Low nucleotide and amino acid identity (81% and 84%, respectively, for SARS-CoV-2) in combination with up to 98% genome coverage were indicative for a related, novel coronavirus. Capture probes targeting vertebrate viruses, designed in 2015, enhanced both sequencing depth and coverage of the SARS-CoV-2 genome, the latter increasing from 71% to 98%.

Conclusion

The model used for simulation of virus discovery enabled validation of the metagenomic sequencing protocol. The metagenomic protocol with virus probes designed before the pandemic, can assist the detection and identification of novel coronaviruses directly in clinical samples.

De novo sequence assembly requires bioinformatic checking of chimeric sequences

De novo assembly of sequence reads from next generation sequencing platforms is a common strategy for detecting presence and sequencing of viruses in biospecimens. Amplification artifacts and presence of several related viruses in the same specimen can lead to assembly of erroneous, chimeric sequences. We now report that such chimeras can also occur between viral and non-viral biological sequences incorrectly joined together which may cause erroneous detection of viruses, highlighting the importance of performing a chimera checking step in bioinformatics pipelines. Using Illumina NextSeq and metagenomic sequencing, we analyzed 80 consecutive non-melanoma skin cancers (NMSCs) from 11 immunosuppressed patients together with 11 NMSCs from patients who had only developed 1 NMSC. We aligned high-quality reads against a Human Papillomavirus (HPV) database and found HPV sequences in 9/91 specimens. A previous bioinformatic analysis of the same crude sequencing data from some of these samples had found an additional 3 specimens to be HPV-positive after performing de novo assembly. The reason for the discrepancy was investigated and found to be mostly caused by chimeric sequences containing both viral and non-viral sequences. Non-viral sequences were present in these 3 samples. To avoid erroneous detection of HPV when performing sequencing, we thus developed a novel script to identify HPV chimeric sequences.

A hybrid pipeline for reconstruction and analysis of viral genomes at multi-organ level

BACKGROUND

Advances in sequencing technologies have enabled the characterization of multiple microbial and host genomes, opening new frontiers of knowledge while kindling novel applications and research perspectives. Among these is the investigation of the viral communities residing in the human body and their impact on health and disease. To this end, the study of samples from multiple tissues is critical, yet, the complexity of such analysis calls for a dedicated pipeline. We provide an automatic and efficient pipeline for identification, assembly, and analysis of viral genomes that combines the DNA sequence data from multiple organs. TRACESPipe relies on cooperation among 3 modalities: compression-based prediction, sequence alignment, and de novo assembly. The pipeline is ultra-fast and provides, additionally, secure transmission and storage of sensitive data.

FINDINGS

TRACESPipe performed outstandingly when tested on synthetic and ex vivo datasets, identifying and reconstructing all the viral genomes, including those with high levels of single-nucleotide polymorphisms. It also detected minimal levels of genomic variation between different organs.

CONCLUSIONS

TRACESPipe's unique ability to simultaneously process and analyze samples from different sources enables the evaluation of within-host variability. This opens up the possibility to investigate viral tissue tropism, evolution, fitness, and disease associations. Moreover, additional features such as DNA damage estimation and mitochondrial DNA reconstruction and analysis, as well as exogenous-source controls, expand the utility of this pipeline to other fields such as forensics and ancient DNA studies. TRACESPipe is released under GPLv3 and is available for free download at https://github.com/viromelab/tracespipe.

Computational approaches in viral ecology

Dynamic virus-host interactions play a critical role in regulating microbial community structure and function. Yet for decades prior to the genomics era, viruses were largely overlooked in microbial ecology research, as only low-throughput culture-based methods of discovering viruses were available. With the advent of metagenomics, culture-independent techniques have provided exciting opportunities to discover and study new viruses. Here, we review recently developed computational methods for identifying viral sequences, exploring viral diversity in environmental samples, and predicting hosts from metagenomic sequence data. Methods to analyze viruses in silico utilize unconventional approaches to tackle challenges unique to viruses, such as vast diversity, mosaic viral genomes, and the lack of universal marker genes. As the field of viral ecology expands exponentially, computational advances have become increasingly important to gain insight into the role viruses in diverse habitats.

Comparison of third-generation sequencing approaches to identify viral pathogens under public health emergency conditions

The capability of high-throughput sequencing (HTS) for detection of known and unknown viruses timely makes it a powerful tool for public health emergency response. Third-generation sequencing (TGS) offers advantages in speed and length of detection over second-generation sequencing (SGS). Here, we presented the end-to-end workflows for both Oxford Nanopore MinION and Pacbio Sequel on a viral disease emergency event, along with Ion Torrent PGM as a reference. A specific pipeline for comparative analysis on viral genomes recovered by each platform was assembled, given the high errors of base-calling for TGS platforms. All the three platforms successfully identified and recovered at least 85% Norovirus GII genomes. Oxford Nanopore MinION spent the least sample-to-answer turnaround time with relatively low but enough accuracy for taxonomy classification. Pacbio Sequel recovered the most accurate viral genome, while spending the longest time. Overall, Nanopore metagenomics can rapidly characterize viruses, and Pacbio Sequel can accurately recover viruses. This study provides a framework for designing the appropriate experiments that are likely to lead to accurate and rapid virus emergency response.

Toward a dynamical understanding of microbial communities

The challenge of moving beyond descriptions of microbial community composition to the point where understanding underlying eco-evolutionary dynamics emerges is daunting. While it is tempting to simplify through use of model communities composed of a small number of types, there is a risk that such strategies fail to capture processes that might be specific and intrinsic to complexity of the community itself. Here, we describe approaches that embrace this complexity and show that, in combination with metagenomic strategies, dynamical insight is increasingly possible. Arising from these studies is mounting evidence of rapid eco-evolutionary change among lineages and a sense that processes, particularly those mediated by horizontal gene transfer, not only are integral to system function, but are central to long-term persistence. That such dynamic, systems-level insight is now possible, means that the study and manipulation of microbial communities can move to new levels of inquiry. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.

DEN-IM: dengue virus genotyping from amplicon and shotgun metagenomic sequencing

Dengue virus (DENV) represents a public health threat and economic burden in affected countries. The availability of genomic data is key to understanding viral evolution and dynamics, supporting improved control strategies. Currently, the use of high-throughput sequencing (HTS) technologies, which can be applied both directly to patient samples (shotgun metagenomics) and to PCR-amplified viral sequences (amplicon sequencing), is potentially the most informative approach to monitor viral dissemination and genetic diversity by providing, in a single methodological step, identification and characterization of the whole viral genome at the nucleotide level. Despite many advantages, these technologies require bioinformatics expertise and appropriate infrastructure for the analysis and interpretation of the resulting data. In addition, the many software solutions available can hamper the reproducibility and comparison of results. Here we present DEN-IM, a one-stop, user-friendly, containerized and reproducible workflow for the analysis of DENV short-read sequencing data from both amplicon and shotgun metagenomics approaches. It is able to infer the DENV coding sequence (CDS), identify the serotype and genotype, and generate a phylogenetic tree. It can easily be run on any UNIX-like system, from local machines to high-performance computing clusters, performing a comprehensive analysis without the requirement for extensive bioinformatics expertise. Using DEN-IM, we successfully analysed two types of DENV datasets. The first comprised 25 shotgun metagenomic sequencing samples from patients with variable serotypes and genotypes, including an in vitro spiked sample containing the four known serotypes. The second consisted of 106 paired-end and 76 single-end amplicon sequences of DENV 3 genotype III and DENV 1 genotype I, respectively, where DEN-IM allowed detection of the intra-genotype diversity. The DEN-IM workflow, parameters and execution configuration files, and documentation are freely available at https://github.com/B-UMMI/DEN-IM).

Identification of Histoplasma causing an unexplained disease cluster in Matthews Ridge, Guyana

Here, we report the identification of Histoplasma causing an unexplained disease cluster in Matthews Ridge, Guyana. In March 2019, 14 employees of Chongqing Bosai Mining Company, China, working in a manganese mining of Guyana, had unexplained fever, and two of them died. We obtained lung and brain tissues as well as the blood samples from the two deceased cases (patient No. 1 and 2), and bronchoscopy lavages and cerebrospinal fluid samples from one severe case (patient No. 3), respectively. All samples were tested by pathological examination, high-throughput sequencing, and real-time PCR. Pathological detection showed the presence of spore-like structures in the lung tissue of patient No. 1, indicating a fungal infection in this patient. Nanopore sequencing identified the existing of H. capsulatum in the lung tissue sample within 13 h. Next-generation sequencing identified specific fragments of H. capsulatum in all of the samples tested (lung, brain and blood serum from the deceased cases, and plasma from the severe case). Real-time PCR assays did not reveal any viral infection related to transmission from bat feces. We conclude that H. capsulatum was the causative pathogen of this disease cluster based on epidemiologic, clinical, pathological and nucleic acid evidence.

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Scientific question

This study reported the identification of Histoplasma as the cause of an unexplained disease cluster in Matthews Ridge, Guyana.

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Evidence before this study

In March 2019, 14 Chinese employees from Chongqing Bosai Mining Company, China, were engaged in manganese mining in Guyana and presented with unexplained fever. Two of them died. After preliminary examination by the local hospital, some potential infectious pathogens were excluded, including Leptospira, HIV, influenza H1N1, Zika virus, Chikungunya virus, Dengue virus, and Influenza A and B viruses. Histoplasmosis is a fungal disease caused by members of the genus Histoplasma and is mainly prevalent in the American continents. Histoplasma is capable of survival in moist soils and can often be isolated from soils containing decaying feces of bats and birds. Human activities in the surface soil produce aerosols, which in turn are inhaled to cause infection.

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New findings

In response to the unexplained disease cluster, pathological examination, high through-put sequencing and real-time PCR were implemented. A TGS platform found Histoplasma within 13 hours. NGS was also successfully applied in response to this event. Compared with NGS, the main features of nanopore sequencing are long sequencing ability, simplicity of use, the fastest turn-around time, high portability and real-time analysis of sequencing data. Though NGS had a longer turnaround time (24 hours), it worked well with different sample types (lung tissue, brain tissue and serum from deceased cases and plasma from a severe case) and was more sensitive than nanopore sequencing. Real-time PCR assays did not reveal any infection by viruses related to bat feces transmission. Pathological detection results showed the presence of spore-like structures, indicating fungus infection in this patient. All the results were consistent with the NGS analysis, supporting the fungus infection.

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Significance of the study

We concluded that H. capsulatum is the causative pathogen for this disease cluster based on epidemiologic, clinical, pathological and nucleic acid supportive evidence.

Human Virome and Disease: High-Throughput Sequencing for Virus Discovery, Identification of Phage-Bacteria Dysbiosis and Development of Therapeutic Approaches with Emphasis on the Human Gut

The virome is comprised of endogenous retroviruses, eukaryotic viruses, and bacteriophages and is increasingly being recognized as an essential part of the human microbiome. The human virome is associated with Type-1 diabetes (T1D), Type-2 diabetes (T2D), Inflammatory Bowel Disease (IBD), Human Immunodeficiency Virus (HIV) infection, and cancer. Increasing evidence also supports trans-kingdom interactions of viruses with bacteria, small eukaryotes and host in disease progression. The present review focuses on virus ecology and biology and how this translates mostly to human gut virome research. Current challenges in the field and how the development of bioinformatic tools and controls are aiding to overcome some of these challenges are also discussed. Finally, the present review also focuses on how human gut virome research could result in translational and clinical studies that may facilitate the development of therapeutic approaches.

HPViewer: sensitive and specific genotyping of human papillomavirus in metagenomic DNA

Motivation

Shotgun DNA sequencing provides sensitive detection of all 182 HPV types in tissue and body fluid. However, existing computational methods either produce false positives misidentifying HPV types due to shared sequences among HPV, human and prokaryotes, or produce false negative since they identify HPV by assembled contigs requiring large abundant of HPV reads.

Results

We designed HPViewer with two custom HPV reference databases masking simple repeats and homology sequences respectively and one homology distance matrix to hybridize these two databases. It directly identified HPV from short DNA reads rather than assembled contigs. Using 100 100 simulated samples, we revealed that HPViewer was robust for samples containing either high or low number of HPV reads. Using 12 shotgun sequencing samples from respiratory papillomatosis, HPViewer was equal to VirusTAP, and Vipie and better than HPVDetector with the respect to specificity and was the most sensitive method in the detection of HPV types 6 and 11. We demonstrated that contigs-based approaches had disadvantages of detection of HPV. In 1573 sets of metagenomic data from 18 human body sites, HPViewer identified 104 types of HPV in a body-site associated pattern and 89 types of HPV co-occurring in one sample with other types of HPV. We demonstrated HPViewer was sensitive and specific for HPV detection in metagenomic data.

Availability and implementation

HPViewer can be accessed at https://github.com/yuhanH/HPViewer/.

Supplementary information

Supplementary data are available at Bioinformatics online.

DisCVR: Rapid viral diagnosis from high-throughput sequencing data

High-throughput sequencing (HTS) enables most pathogens in a clinical sample to be detected from a single analysis, thereby providing novel opportunities for diagnosis, surveillance, and epidemiology. However, this powerful technology is difficult to apply in diagnostic laboratories because of its computational and bioinformatic demands. We have developed DisCVR, which detects known human viruses in clinical samples by matching sample k-mers (twenty-two nucleotide sequences) to k-mers from taxonomically labeled viral genomes. DisCVR was validated using published HTS data for eighty-nine clinical samples from adults with upper respiratory tract infections. These samples had been tested for viruses metagenomically and also by real-time polymerase chain reaction assay, which is the standard diagnostic method. DisCVR detected human viruses with high sensitivity (79%) and specificity (100%), and was able to detect mixed infections. Moreover, it produced results comparable to those in a published metagenomic analysis of 177 blood samples from patients in Nigeria. DisCVR has been designed as a user-friendly tool for detecting human viruses from HTS data using computers with limited RAM and processing power, and includes a graphical user interface to help users interpret and validate the output. It is written in Java and is publicly available from http://bioinformatics.cvr.ac.uk/discvr.php.