Assembly of viral genomes from metagenomes

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.

Viral and Bacterial Community Dynamics in Food Waste and Digestate from Full-Scale Biogas Plants

Anaerobic digestion (AD) is commonly used in food waste treatment. Prokaryotic microbial communities in AD of food waste have been comprehensively studied. The role of viruses, known to affect microbial dynamics and metabolism, remains largely unexplored. This study employed metagenomic analysis and recovered 967 high-quality viral bins within food waste and digestate derived from 8 full-scale biogas plants. The diversity of viral communities was higher in digestate. In silico predictions linked 20.8% of viruses to microbial host populations, highlighting possible virus predators of key functional microbes. Lineage-specific virus-host ratio varied, indicating that viral infection dynamics might differentially affect microbial responses to the varying process parameters. Evidence for virus-mediated gene transfer was identified, emphasizing the potential role of viruses in controlling the microbiome. AD altered the specific process parameters, potentially promoting a shift in viral lifestyle from lysogenic to lytic. Viruses encoding auxiliary metabolic genes (AMGs) were involved in microbial carbon and nutrient cycling, and most AMGs were transcriptionally expressed in digestate, meaning that viruses with active functional states were likely actively involved in AD. These findings provided a comprehensive profile of viral and bacterial communities and expanded knowledge of the interactions between viruses and hosts in food waste and digestate.

Viral genome sequencing methods: benefits and pitfalls of current approaches

Whole genome sequencing of viruses provides high-resolution molecular insights, enhancing our understanding of viral genome function and phylogeny. Beyond fundamental research, viral sequencing is increasingly vital for pathogen surveillance, epidemiology, and clinical applications. As sequencing methods rapidly evolve, the diversity of viral genomics applications and catalogued genomes continues to expand. Advances in long-read, single molecule, real-time sequencing methodologies present opportunities to sequence contiguous, haplotype resolved viral genomes in a range of research and applied settings. Here we present an overview of nucleic acid sequencing methods and their applications in studying viral genomes. We emphasise the advantages of different viral sequencing approaches, with a particular focus on the benefits of third-generation sequencing technologies in elucidating viral evolution, transmission networks, and pathogenesis.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data

The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data. Compared to other Hi-C-based methods, ViralCC leverages the virus-host proximity structure as a complementary information source for the Hi-C interactions. Using mock and real metagenomic Hi-C datasets from several different microbial ecosystems, including the human gut, cow fecal, and wastewater, we demonstrate that ViralCC outperforms existing Hi-C-based binning methods as well as state-of-the-art tools specifically dedicated to metagenomic viral binning. ViralCC can also reveal the taxonomic structure of viruses and virus-host pairs in microbial communities. When applied to a real wastewater metagenomic Hi-C dataset, ViralCC constructs a phage-host network, which is further validated using CRISPR spacer analyses. ViralCC is an open-source pipeline available at https://github.com/dyxstat/ViralCC .

Benchmarking and Assessment of Eight De Novo Genome Assemblers on Viral Next-Generation Sequencing Data, Including the SARS-CoV-2

Viral genomics has become crucial in clinical diagnostics and ecology, not to mention to stem the COVID-19 pandemic. Whole-genome sequencing (WGS) is pivotal in gaining an improved understanding of viral evolution, genomic epidemiology, infectious outbreaks, pathobiology, clinical management, and vaccine development. Genome assembly is one of the crucial steps in WGS data analyses. A series of different assemblers has been developed with the advent of high-throughput next-generation sequencing (NGS). Various studies have reported the evaluation of these assembly tools on distinct datasets; however, these lack data from viral origin. In this study, we performed a comparative evaluation and benchmarking of eight de novo assemblers: SOAPdenovo, Velvet, assembly by short sequences (ABySS), iterative De Bruijn graph assembler (IDBA), SPAdes, Edena, iterative virus assembler, and VICUNA on the viral NGS data from distinct Illumina (GAIIx, Hiseq, Miseq, and Nextseq) platforms. WGS data of diverse viruses, that is, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), dengue virus 3, human immunodeficiency virus 1, hepatitis B virus, human herpesvirus 8, human papillomavirus 16, rhinovirus A, and West Nile virus, were utilized to assess these assemblers. Performance metrics such as genome fraction recovery, assembly lengths, NG50, N50, contig length, contig numbers, mismatches, and misassemblies were analyzed. Overall, three assemblers, that is, SPAdes, IDBA, and ABySS, performed consistently well, including for genome assembly of SARS-CoV-2. These assembly methods should be considered and recommended for future studies of viruses. The study also suggests that implementing two or more assembly approaches should be considered in viral NGS studies, especially in clinical settings. Taken together, the benchmarking of eight de novo genome assemblers reported in this study can inform future public health and ecology research concerning the viruses, the COVID-19 pandemic, and viral outbreaks.

Genome binning of viral entities from bulk metagenomics data

Despite the accelerating number of uncultivated virus sequences discovered in metagenomics and their apparent importance for health and disease, the human gut virome and its interactions with bacteria in the gastrointestinal tract are not well understood. This is partly due to a paucity of whole-virome datasets and limitations in current approaches for identifying viral sequences in metagenomics data. Here, combining a deep-learning based metagenomics binning algorithm with paired metagenome and metavirome datasets, we develop Phages from Metagenomics Binning (PHAMB), an approach that allows the binning of thousands of viral genomes directly from bulk metagenomics data, while simultaneously enabling clustering of viral genomes into accurate taxonomic viral populations. When applied on the Human Microbiome Project 2 (HMP2) dataset, PHAMB recovered 6,077 high-quality genomes from 1,024 viral populations, and identified viral-microbial host interactions. PHAMB can be advantageously applied to existing and future metagenomes to illuminate viral ecological dynamics with other microbiome constituents.

Illuminating the Virosphere Through Global Metagenomics

Viruses are the most abundant biological entity on Earth, infect cellular organisms from all domains of life, and are central players in the global biosphere. Over the last century, the discovery and characterization of viruses have progressed steadily alongside much of modern biology. In terms of outright numbers of novel viruses discovered, however, the last few years have been by far the most transformative for the field. Advances in methods for identifying viral sequences in genomic and metagenomic datasets, coupled to the exponential growth of environmental sequencing, have greatly expanded the catalog of known viruses and fueled the tremendous growth of viral sequence databases. Development and implementation of new standards, along with careful study of the newly discovered viruses, have transformed and will continue to transform our understanding of microbial evolution, ecology, and biogeochemical cycles, leading to new biotechnological innovations across many diverse fields, including environmental, agricultural, and biomedical sciences.

Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities

Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our 'vademecum' for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

CheckV assesses the quality and completeness of metagenome-assembled viral genomes

Millions of new viral sequences have been identified from metagenomes, but the quality and completeness of these sequences vary considerably. Here we present CheckV, an automated pipeline for identifying closed viral genomes, estimating the completeness of genome fragments and removing flanking host regions from integrated proviruses. CheckV estimates completeness by comparing sequences with a large database of complete viral genomes, including 76,262 identified from a systematic search of publicly available metagenomes, metatranscriptomes and metaviromes. After validation on mock datasets and comparison to existing methods, we applied CheckV to large and diverse collections of metagenome-assembled viral sequences, including IMG/VR and the Global Ocean Virome. This revealed 44,652 high-quality viral genomes (that is, >90% complete), although the vast majority of sequences were small fragments, which highlights the challenge of assembling viral genomes from short-read metagenomes. Additionally, we found that removal of host contamination substantially improved the accurate identification of auxiliary metabolic genes and interpretation of viral-encoded functions.

Enteric Virome and Carcinogenesis in the Gut

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in both the USA and the world. Recent research has demonstrated the involvement of the gut microbiota in CRC development and progression. Microbial biomarkers of disease have focused primarily on the bacterial component of the microbiome; however, the viral portion of the microbiome, consisting of both bacteriophages and eukaryotic viruses, together known as the virome, has been lesser studied. Here we review the recent advancements in high-throughput sequencing (HTS) technologies and bioinformatics, which have enabled scientists to better understand how viruses might influence the development of colorectal cancer. We discuss the contemporary findings revealing modulations in the virome and their correlation with CRC development and progression. While a variety of challenges still face viral HTS detection in clinical specimens, we consider herein numerous next steps for future basic and clinical research. Clinicians need to move away from a single infectious agent model for disease etiology by grasping new, more encompassing etiological paradigms, in which communities of various microbial components interact with each other and the host. The reporting and indexing of patient health information, socioeconomic data, and other relevant metadata will enable identification of predictive variables and covariates of viral presence and CRC development. Altogether, the virome has a more profound role in carcinogenesis and cancer progression than once thought, and viruses, specific for either human cells or bacteria, are clinically relevant in understanding CRC pathology, patient prognosis, and treatment development.

Microbial and Viral Communities and Their Antibiotic Resistance Genes Throughout a Hospital Wastewater Treatment System

Antibiotic resistance poses a serious threat to global public health, and antibiotic resistance determinants can enter natural aquatic systems through discharge of wastewater effluents. Hospital wastewater in particular is expected to contain high abundances of antibiotic resistance genes (ARGs) compared to municipal wastewater because it contains human enteric bacteria that may include antibiotic-resistant organisms originating from hospital patients, and can also have high concentrations of antibiotics and antimicrobials relative to municipal wastewater. Viruses also play an important role in wastewater treatment systems since they can influence the bacterial community composition through killing bacteria, facilitating transduction of genetic material between organisms, and modifying the chromosomal content of bacteria as prophages. However, little is known about the fate and connections between ARGs, viruses, and their associated bacteria in hospital wastewater systems. To address this knowledge gap, we characterized the composition and persistence of ARGs, dsDNA viruses, and bacteria from influent to effluent in a pilot-scale hospital wastewater treatment system in Israel using shotgun metagenomics. Results showed that ARGs, including genes conferring resistance to antibiotics of high clinical relevance, were detected in all sampling locations throughout the pilot-scale system, with only 16% overall depletion of ARGs per genome equivalent between influent and effluent. The most common classes of ARGs detected throughout the system conferred resistance to aminoglycoside, cephalosporin, macrolide, penam, and tetracycline antibiotics. A greater proportion of total ARGs were associated with plasmid-associated genes in effluent compared to in influent. No strong associations between viral sequences and ARGs were identified in viral metagenomes from the system, suggesting that phage may not be a significant vector for ARG transfer in this system. The majority of viruses in the pilot-scale system belonged to the families Myoviridae, Podoviridae, and Siphoviridae. Gammaproteobacteria was the dominant class of bacteria harboring ARGs and the most common putative viral host in all samples, followed by Bacilli and Betaproteobacteria. In the total bacterial community, the dominant class was Betaproteobacteria for each sample. Overall, we found that a variety of different types of ARGs and viruses were persistent throughout this hospital wastewater treatment system, which can be released to the environment through effluent discharge.

Association of CMV genomic mutations with symptomatic infection and hearing loss in congenital CMV infection

Background

Congenital cytomegalovirus (cCMV) infection is the most common congenital infection and a leading cause of long-term neurological and sensory sequelae, the most common being sensorineural hearing loss (SNHL). Despite extensive research, clinical or laboratory markers to identify CMV infected children with increased risk for disease have not been identified. This study utilizes viral whole-genome next generation-sequencing (NGS) of specimens from congenitally infected infants to explore viral diversity and specific viral variants that may be associated with symptomatic infection and SNHL.

Methods

CMV DNA from urine specimens of 30 infants (17 asymptomatic, 13 symptomatic) was target enriched and next generation sequenced resulting in 93% coverage of the CMV genome allowing analysis of viral diversity.

Results

Variant frequency distribution was compared between children with symptomatic and asymptomatic cCMV and those with (n = 13) and without (n = 17) hearing loss. The CMV genes UL48A, UL88, US19 and US22 were found to have an increase in nucleotide diversity in symptomatic children; while UL57, UL20, UL104, US14, UL115, and UL35 had an increase in diversity in children with hearing loss. An analysis of single variant differences between symptomatic and asymptomatic children found UL55 to have the highest number, while the most variants associated with SNHL were in the RL11 gene family. In asymptomatic infants with SNHL, mutations were observed more frequently in UL33 and UL20.

Conclusion

CMV genomes from infected newborns can be mapped to 93% of the genome at a depth allowing accurate and reproducible analysis of polymorphisms for variant and gene discovery that may be linked to symptomatic and hearing loss outcomes.

Coding-Complete Genome Sequence of a Pollen-Associated Virus Belonging to the Secoviridae Family Recovered from a Japanese Apricot (Prunus mume) Metagenome Data Set

We report the coding-complete genome sequence of Japanese apricot pollen-associated secovirus 1 (JAPSV1), a virus belonging to the Secoviridae family, recovered from Japanese apricot (Prunus mume) pollen that is closely related to Peach leaf pitting-associated virus (PLPAV). This discovery adds to the number of known pollen-associated viruses.

We report the coding-complete genome sequence of Japanese apricot pollen-associated secovirus 1 (JAPSV1), a virus belonging to the Secoviridae family, recovered from Japanese apricot (Prunus mume) pollen that is closely related to Peach leaf pitting-associated virus (PLPAV). This discovery adds to the number of known pollen-associated viruses.

virMine: automated detection of viral sequences from complex metagenomic samples

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

Choice of assembly software has a critical impact on virome characterisation

BACKGROUND

The viral component of microbial communities plays a vital role in driving bacterial diversity, facilitating nutrient turnover and shaping community composition. Despite their importance, the vast majority of viral sequences are poorly annotated and share little or no homology to reference databases. As a result, investigation of the viral metagenome (virome) relies heavily on de novo assembly of short sequencing reads to recover compositional and functional information. Metagenomic assembly is particularly challenging for virome data, often resulting in fragmented assemblies and poor recovery of viral community members. Despite the essential role of assembly in virome analysis and difficulties posed by these data, current assembly comparisons have been limited to subsections of virome studies or bacterial datasets.

DESIGN

This study presents the most comprehensive virome assembly comparison to date, featuring 16 metagenomic assembly approaches which have featured in human virome studies. Assemblers were assessed using four independent virome datasets, namely, simulated reads, two mock communities, viromes spiked with a known phage and human gut viromes.

RESULTS

Assembly performance varied significantly across all test datasets, with SPAdes (meta) performing consistently well. Performance of MIRA and VICUNA varied, highlighting the importance of using a range of datasets when comparing assembly programs. It was also found that while some assemblers addressed the challenges of virome data better than others, all assemblers had limitations. Low read coverage and genomic repeats resulted in assemblies with poor genome recovery, high degrees of fragmentation and low-accuracy contigs across all assemblers. These limitations must be considered when setting thresholds for downstream analysis and when drawing conclusions from virome data.

Family A DNA Polymerase Phylogeny Uncovers Diversity and Replication Gene Organization in the Virioplankton

Shotgun metagenomics, which allows for broad sampling of viral diversity, has uncovered genes that are widely distributed among virioplankton populations and show linkages to important biological features of unknown viruses. Over 25% of known dsDNA phage carry the DNA polymerase I (polA) gene, making it one of the most widely distributed phage genes. Because of its pivotal role in DNA replication, this enzyme is linked to phage lifecycle characteristics. Previous research has suggested that a single amino acid substitution might be predictive of viral lifestyle. In this study Chesapeake Bay virioplankton were sampled by shotgun metagenomic sequencing (using long and short read technologies). More polA sequences were predicted from this single viral metagenome (virome) than from 86 globally distributed virome libraries (ca. 2,100, and 1,200, respectively). The PolA peptides predicted from the Chesapeake Bay virome clustered with 69% of PolA peptides from global viromes; thus, remarkably the Chesapeake Bay virome captured the majority of known PolA peptide diversity in viruses. This deeply sequenced virome also expanded the diversity of PolA sequences, increasing the number of PolA clusters by 44%. Contigs containing polA sequences were also used to examine relationships between phylogenetic clades of PolA and other genes within unknown viral populations. Phylogenic analysis revealed five distinct groups of phages distinguished by the amino acids at their 762 (Escherichia coli IAI39 numbering) positions and replication genes. DNA polymerase I sequences from Tyr762 and Phe762 groups were most often neighbored by ring-shaped superfamily IV helicases and ribonucleotide reductases (RNRs). The Leu762 groups had non-ring shaped helicases from superfamily II and were further distinguished by an additional helicase gene from superfamily I and the lack of any identifiable RNR genes. Moreover, we found that the inclusion of ribonucleotide reductase associated with PolA helped to further differentiate phage diversity, chiefly within lytic podovirus populations. Altogether, these data show that DNA Polymerase I is a useful marker for observing the diversity and composition of the virioplankton and may be a driving factor in the divergence of phage replication components.

WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs

Summary

WIsH predicts prokaryotic hosts of phages from their genomic sequences. It achieves 63% mean accuracy when predicting the host genus among 20 genera for 3 kbp-long phage contigs. Over the best current tool, WisH shows much improved accuracy on phage sequences of a few kbp length and runs hundreds of times faster, making it suited for metagenomics studies.

Availability and implementation

OpenMP-parallelized GPL-licensed C ++ code available at https://github.com/soedinglab/wish.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evaluation of bias induced by viral enrichment and random amplification protocols in metagenomic surveys of saliva DNA viruses

BACKGROUND

Viruses are key players regulating microbial ecosystems. Exploration of viral assemblages is now possible thanks to the development of metagenomics, the most powerful tool available for studying viral ecology and discovering new viruses. Unfortunately, several sources of bias lead to the misrepresentation of certain viruses within metagenomics workflows, hindering the shift from merely descriptive studies towards quantitative comparisons of communities. Therefore, benchmark studies on virus enrichment and random amplification protocols are required to better understand the sources of bias.

RESULTS

We assessed the bias introduced by viral enrichment on mock assemblages composed of seven DNA viruses, and the bias from random amplification methods on human saliva DNA viromes, using qPCR and deep sequencing, respectively. While iodixanol cushions and 0.45 μm filtration preserved the original composition of nuclease-protected viral genomes, low-force centrifugation and 0.22 μm filtration removed large viruses. Comparison of unamplified and randomly amplified saliva viromes revealed that multiple displacement amplification (MDA) induced stochastic bias from picograms of DNA template. However, the type of bias shifted to systematic using 1 ng, with only a marginal influence by amplification time. Systematic bias consisted of over-amplification of small circular genomes, and under-amplification of those with extreme GC content, a negative bias that was shared with the PCR-based sequence-independent, single-primer amplification (SISPA) method. MDA based on random priming provided by a DNA primase activity slightly outperformed those based on random hexamers and SISPA, which may reflect differences in ability to handle sequences with extreme GC content. SISPA viromes showed uneven coverage profiles, with high coverage peaks in regions with low linguistic sequence complexity. Despite misrepresentation of certain viruses after random amplification, ordination plots based on dissimilarities among contig profiles showed perfect overlapping of related amplified and unamplified saliva viromes and strong separation from unrelated saliva viromes. This result suggests that random amplification bias has a minor impact on beta diversity studies.

CONCLUSIONS

Benchmark analyses of mock and natural communities of viruses improve understanding and mitigate bias in metagenomics surveys. Bias induced by random amplification methods has only a minor impact on beta diversity studies of human saliva viromes.

Overview of Virus Metagenomic Classification Methods and Their Biological Applications

Metagenomics poses opportunities for clinical and public health virology applications by offering a way to assess complete taxonomic composition of a clinical sample in an unbiased way. However, the techniques required are complicated and analysis standards have yet to develop. This, together with the wealth of different tools and workflows that have been proposed, poses a barrier for new users. We evaluated 49 published computational classification workflows for virus metagenomics in a literature review. To this end, we described the methods of existing workflows by breaking them up into five general steps and assessed their ease-of-use and validation experiments. Performance scores of previous benchmarks were summarized and correlations between methods and performance were investigated. We indicate the potential suitability of the different workflows for (1) time-constrained diagnostics, (2) surveillance and outbreak source tracing, (3) detection of remote homologies (discovery), and (4) biodiversity studies. We provide two decision trees for virologists to help select a workflow for medical or biodiversity studies, as well as directions for future developments in clinical viral metagenomics.

Novel viruses in birds: Flying through the roof or is a cage needed?

Emerging viral diseases continue to have a major global impact on human beings and animals. To be able to take adequate measures in case of an outbreak of an emerging disease, rapid detection of the causative agent is a crucial first step. In this review, various aspects of virus discovery are discussed, with a special focus on recently discovered viruses in birds. Novel viruses with a potential major impact have been discovered in domestic and wild bird species in recent years using various virus discovery methods. Only a few studies report the detection of novel viruses in endangered bird species, although increased knowledge about viruses circulating in these species is important. Additional studies focusing on the exact role of a novel virus in disease and on the impact of a novel virus on bird populations are often lacking. Intensive collaboration between different disciplines is needed to obtain useful information about the role of these novel viruses.

Assessing the Impact of Assemblers on Virus Detection in a De Novo Metagenomic Analysis Pipeline

Applying high-throughput sequencing to pathogen discovery is a relatively new field, the objective of which is to find disease-causing agents when little or no background information on disease is available. Key steps in the process are the generation of millions of sequence reads from an infected tissue sample, followed by assembly of these reads into longer, contiguous stretches of nucleotide sequences, and then identification of the contigs by matching them to known databases, such as those stored at GenBank or Ensembl. This technique, that is, de novo metagenomics, is particularly useful when the pathogen is viral and strong discriminatory power can be achieved. However, recently, we found that striking differences in results can be achieved when different assemblers were used. In this study, we test formally the impact of five popular assemblers (MIRA, VELVET, METAVELVET, SPADES, and OMEGA) on the detection of a novel virus and assembly of its whole genome in a data set for which we have confirmed the presence of the virus by empirical laboratory techniques, and compare the overall performance between assemblers. Our results show that if results from only one assembler are considered, biologically important reads can easily be overlooked. The impacts of these results on the field of pathogen discovery are considered.

A viral metagenomic approach on a non-metagenomic experiment: Mining next generation sequencing datasets from pig DNA identified several porcine parvoviruses for a retrospective evaluation of viral infections

Shot-gun next generation sequencing (NGS) on whole DNA extracted from specimens collected from mammals often produces reads that are not mapped (i.e. unmapped reads) on the host reference genome and that are usually discarded as by-products of the experiments. In this study, we mined Ion Torrent reads obtained by sequencing DNA isolated from archived blood samples collected from 100 performance tested Italian Large White pigs. Two reduced representation libraries were prepared from two DNA pools constructed each from 50 equimolar DNA samples. Bioinformatic analyses were carried out to mine unmapped reads on the reference pig genome that were obtained from the two NGS datasets. In silico analyses included read mapping and sequence assembly approaches for a viral metagenomic analysis using the NCBI Viral Genome Resource. Our approach identified sequences matching several viruses of the Parvoviridae family: porcine parvovirus 2 (PPV2), PPV4, PPV5 and PPV6 and porcine bocavirus 1-H18 isolate (PBoV1-H18). The presence of these viruses was confirmed by PCR and Sanger sequencing of individual DNA samples. PPV2, PPV4, PPV5, PPV6 and PBoV1-H18 were all identified in samples collected in 1998-2007, 1998-2000, 1997-2000, 1998-2004 and 2003, respectively. For most of these viruses (PPV4, PPV5, PPV6 and PBoV1-H18) previous studies reported their first occurrence much later (from 5 to more than 10 years) than our identification period and in different geographic areas. Our study provided a retrospective evaluation of apparently asymptomatic parvovirus infected pigs providing information that could be important to define occurrence and prevalence of different parvoviruses in South Europe. This study demonstrated the potential of mining NGS datasets non-originally derived by metagenomics experiments for viral metagenomics analyses in a livestock species.

Reconstruction of Bacterial and Viral Genomes from Multiple Metagenomes

Several metagenomic projects have been accomplished or are in progress. However, in most cases, it is not feasible to generate complete genomic assemblies of species from the metagenomic sequencing of a complex environment. Only a few studies have reported the reconstruction of bacterial genomes from complex metagenomes. In this work, Binning-Assembly approach has been proposed and demonstrated for the reconstruction of bacterial and viral genomes from 72 human gut metagenomic datasets. A total 1156 bacterial genomes belonging to 219 bacterial families and, 279 viral genomes belonging to 84 viral families could be identified. More than 80% complete draft genome sequences could be reconstructed for a total of 126 bacterial and 11 viral genomes. Selected draft assembled genomes could be validated with 99.8% accuracy using their ORFs. The study provides useful information on the assembly expected for a species given its number of reads and abundance. This approach along with spiking was also demonstrated to be useful in improving the draft assembly of a bacterial genome. The Binning-Assembly approach can be successfully used to reconstruct bacterial and viral genomes from multiple metagenomic datasets obtained from similar environments.

A New Zamilon-like Virophage Partial Genome Assembled from a Bioreactor Metagenome

Virophages replicate within viral factories inside the Acanthamoeba cytoplasm, and decrease the infectivity and replication of their associated giant viruses. Culture isolation and metagenome analyses have suggested that they are common in our environment. By screening metagenomic databases in search of amoebal viruses, we detected virophage-related sequences among sequences generated from the same non-aerated bioreactor metagenome as recently screened by another team for virophage capsid-encoding genes. We describe here the assembled partial genome of a virophage closely related to Zamilon, which infects Acanthamoeba with mimiviruses of lineages B and C but not A. Searches for sequences related to amoebal giant viruses, other Megavirales representatives and virophages were conducted using BLAST against this bioreactor metagenome (PRJNA73603). Comparative genomic and phylogenetic analyses were performed using sequences from previously identified virophages. A total of 72 metagenome contigs generated from the bioreactor were identified as best matching with sequences from Megavirales representatives, mostly Pithovirus sibericum, pandoraviruses and amoebal mimiviruses from three lineages A–C, as well as from virophages. In addition, a partial genome from a Zamilon-like virophage, we named Zamilon 2, was assembled. This genome has a size of 6716 base pairs, corresponding to 39% of the Zamilon genome, and comprises partial or full-length homologs for 15 Zamilon predicted open reading frames (ORFs). Mean nucleotide and amino acid identities for these 15 Zamilon 2 ORFs with their Zamilon counterparts were 89% (range, 81–96%) and 91% (range, 78–99%), respectively. Notably, these ORFs included two encoding a capsid protein and a packaging ATPase. Comparative genomics and phylogenetic analyses indicated that the partial genome was that of a new Zamilon-like virophage. Further studies are needed to gain better knowledge of the tropism and prevalence of virophages in our biosphere and in humans.

Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data

Background

Identification of biological specimens is a requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.

Results

We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on genome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100 % identification accuracy at supra-species level and 78 % accuracy at the species level.

Conclusion

CNIDARIA allows for fast, resource-efficient comparison and identification of both raw and assembled genome and transcriptome data. This can help answer both fundamental (e.g. in phylogeny, ecological diversity analysis) and practical questions (e.g. sequencing quality control, primer design).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0806-7) contains supplementary material, which is available to authorized users.

Recovering full-length viral genomes from metagenomes

Infectious disease metagenomics is driven by the question: “what is causing the disease?” in contrast to classical metagenome studies which are guided by “what is out there?” In case of a novel virus, a first step to eventually establishing etiology can be to recover a full-length viral genome from a metagenomic sample. However, retrieval of a full-length genome of a divergent virus is technically challenging and can be time-consuming and costly. Here we discuss different assembly and fragment linkage strategies such as iterative assembly, motif searches, k-mer frequency profiling, coverage profile binning, and other strategies used to recover genomes of potential viral pathogens in a timely and cost-effective manner.

Fragmentation and Coverage Variation in Viral Metagenome Assemblies, and Their Effect in Diversity Calculations

Metagenomic libraries consist of DNA fragments from diverse species, with varying genome size and abundance. High-throughput sequencing platforms produce large volumes of reads from these libraries, which may be assembled into contigs, ideally resembling the original larger genomic sequences. The uneven species distribution, along with the stochasticity in sample processing and sequencing bias, impacts the success of accurate sequence assembly. Several assemblers enable the processing of viral metagenomic data de novo, generally using overlap layout consensus or de Bruijn graph approaches for contig assembly. The success of viral genomic reconstruction in these datasets is limited by the degree of fragmentation of each genome in the sample, which is dependent on the sequencing effort and the genome length. Depending on ecological, biological, or procedural biases, some fragments have a higher prevalence, or coverage, in the assembly. However, assemblers must face challenges, such as the formation of chimerical structures and intra-species variability. Diversity calculation relies on the classification of the sequences that comprise a metagenomic dataset. Whenever the corresponding genomic and taxonomic information is available, contigs matching the same species can be classified accordingly and the coverage of its genome can be calculated for that species. This may be used to compare populations by estimating abundance and assessing species distribution from this data. Nevertheless, the coverage does not take into account the degree of fragmentation, or else genome completeness, and is not necessarily representative of actual species distribution in the samples. Furthermore, undetermined sequences are abundant in viral metagenomic datasets, resulting in several independent contigs that cannot be assigned by homology or genomic information. These may only be classified as different operational taxonomic units (OTUs), sometimes remaining inadvisably unrelated. Thus, calculations using contigs as different OTUs ultimately overestimate diversity when compared to diversity calculated from species coverage. In order to compare the effect of coverage and fragmentation, we generated three sets of simulated Illumina paired-end reads with different sequencing depths. We compared different assemblies performed with RayMeta, CLC Assembly Cell, MEGAHIT, SPAdes, Meta-IDBA, SOAPdenovo, Velvet, Metavelvet, and MIRA with the best attainable assemblies for each dataset (formed by arranging data using known genome coordinates) by calculating different assembly statistics. A new fragmentation score was included to estimate the degree of genome fragmentation of each taxon and adjust the coverage accordingly. The abundance in the metagenome was compared by bootstrapping the assembly data and hierarchically clustering them with the best possible assembly. Additionally, richness and diversity indexes were calculated for all the resulting assemblies and were assessed under two distributions: contigs as independent OTUs and sequences classified by species. Finally, we search for the strongest correlations between the diversity indexes and the different assembly statistics. Although fragmentation was dependent of genome coverage, it was not as heavily influenced by the assembler. The sequencing depth was the predominant attractor that influenced the success of the assemblies. The coverage increased notoriously in larger datasets, whereas fragmentation values remained lower and unsaturated. While still far from obtaining the ideal assemblies, the RayMeta, SPAdes, and the CLC assemblers managed to build the most accurate contigs with larger datasets while Meta-IDBA showed a good performance with the medium-sized dataset, even after the adjusted coverage was calculated. Their resulting assemblies showed the highest coverage scores and the lowest fragmentation values. Alpha diversity calculated from contigs as OTUs resulted in significantly higher values for all assemblies when compared with actual species distribution, showing an overestimation due to the increased predicted abundance. Conversely, using PHACCS resulted in lower values for all assemblers. Different association methods (random-forest, generalized linear models, and the Spearman correlation index) support the number of contigs, the coverage, and fragmentation as the assembly parameters that most affect the estimation of the alpha diversity. Coverage calculations may provide an insight into relative completeness of a genome but they overlook missing fragments or overly separated sequences in a genome. The assembly of a highly fragmented genomes with high coverage may still lead to the clustering of different OTUs that are actually different fragments of a genome. Thus, it proves useful to penalize coverage with a fragmentation score. Using contigs for calculating alpha diversity result in overestimation but it is usually the only approach available. Still, it is enough for sample comparison. The best approach may be determined by choosing the assembler that better fits the sequencing depth and adjusting the parameters for longer accurate contigs whenever possible whereas diversity may be calculated considering taxonomical and genomic information if available.