Recovering full-length viral genomes from metagenomes

A survey of k-mer methods and applications in bioinformatics

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

A tale of caution: How endogenous viral elements affect virus discovery in transcriptomic data

Large-scale metagenomic and -transcriptomic studies have revolutionized our understanding of viral diversity and abundance. In contrast, endogenous viral elements (EVEs), remnants of viral sequences integrated into host genomes, have received limited attention in the context of virus discovery, especially in RNA-Seq data. EVEs resemble their original viruses, a challenge that makes distinguishing between active infections and integrated remnants difficult, affecting virus classification and biases downstream analyses. Here, we systematically assess the effects of EVEs on a prototypical virus discovery pipeline, evaluate their impact on data integrity and classification accuracy, and provide some recommendations for better practices. We examined EVEs and exogenous viral sequences linked to Orthomyxoviridae, a diverse family of negative-sense segmented RNA viruses, in 13 genomic and 538 transcriptomic datasets of Culicinae mosquitoes. Our analysis revealed a substantial number of viral sequences in transcriptomic datasets. However, a significant portion appeared not to be exogenous viruses but transcripts derived from EVEs. Distinguishing between transcribed EVEs and exogenous virus sequences was especially difficult in samples with low viral abundance. For example, three transcribed EVEs showed full-length segments, devoid of frameshift and nonsense mutations, exhibiting sufficient mean read depths that qualify them as exogenous virus hits. Mapping reads on a host genome containing EVEs before assembly somewhat alleviated the EVE burden, but it led to a drastic reduction of viral hits and reduced quality of assemblies, especially in regions of the viral genome relatively similar to EVEs. Our study highlights that our knowledge of the genetic diversity of viruses can be altered by the underestimated presence of EVEs in transcriptomic datasets, leading to false positives and altered or missing sequence information. Thus, recognizing and addressing the influence of EVEs in virus discovery pipelines will be key in enhancing our ability to capture the full spectrum of viral diversity.

Comparison of Source Attribution Methodologies for Human Campylobacteriosis

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.

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.

Taxonomic, functional and expression analysis of viral communities associated with marine sponges

Viruses play an essential role in shaping the structure and function of ecological communities. Marine sponges have the capacity to filter large volumes of ‘virus-laden’ seawater through their bodies and host dense communities of microbial symbionts, which are likely accessible to viral infection. However, despite the potential of sponges and their symbionts to act as viral reservoirs, little is known about the sponge-associated virome. Here we address this knowledge gap by analysing metagenomic and (meta-) transcriptomic datasets from several sponge species to determine what viruses are present and elucidate their predicted and expressed functionality. Sponges were found to carry diverse, abundant and active bacteriophages as well as eukaryotic viruses belonging to the Megavirales and Phycodnaviridae. These viruses contain and express auxiliary metabolic genes (AMGs) for photosynthesis and vitamin synthesis as well as for the production of antimicrobials and the defence against toxins. These viral AMGs can therefore contribute to the metabolic capacities of their hosts and also potentially enhance the survival of infected cells. This suggest that viruses may play a key role in regulating the abundance and activities of members of the sponge holobiont.

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.

Setting a baseline for global urban virome surveillance in sewage

The rapid development of megacities, and their growing connectedness across the world is becoming a distinct driver for emerging disease outbreaks. Early detection of unusual disease emergence and spread should therefore include such cities as part of risk-based surveillance. A catch-all metagenomic sequencing approach of urban sewage could potentially provide an unbiased insight into the dynamics of viral pathogens circulating in a community irrespective of access to care, a potential which already has been proven for the surveillance of poliovirus. Here, we present a detailed characterization of sewage viromes from a snapshot of 81 high density urban areas across the globe, including in-depth assessment of potential biases, as a proof of concept for catch-all viral pathogen surveillance. We show the ability to detect a wide range of viruses and geographical and seasonal differences for specific viral groups. Our findings offer a cross-sectional baseline for further research in viral surveillance from urban sewage samples and place previous studies in a global perspective.

Metaviromics Reveals Unknown Viral Diversity in the Biting Midge Culicoides impunctatus

Biting midges (Culicoides species) are vectors of arboviruses and were responsible for the emergence and spread of Schmallenberg virus (SBV) in Europe in 2011 and are likely to be involved in the emergence of other arboviruses in Europe. Improved surveillance and better understanding of risks require a better understanding of the circulating viral diversity in these biting insects. In this study, we expand the sequence space of RNA viruses by identifying a number of novel RNA viruses from Culicoides impunctatus (biting midge) using a meta-transcriptomic approach. A novel metaviromic pipeline called MetaViC was developed specifically to identify novel virus sequence signatures from high throughput sequencing (HTS) datasets in the absence of a known host genome. MetaViC is a protein centric pipeline that looks for specific protein signatures in the reads and contigs generated as part of the pipeline. Several novel viruses, including an alphanodavirus with both segments, a novel relative of the Hubei sobemo-like virus 49, two rhabdo-like viruses and a chuvirus, were identified in the Scottish midge samples. The newly identified viruses were found to be phylogenetically distinct to those previous known. These findings expand our current knowledge of viral diversity in arthropods and especially in these understudied disease vectors.

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.

Bacterial and Archaeal Viruses of Himalayan Hot Springs at Manikaran Modulate Host Genomes

Hot spring-associated viruses, particularly the archaeal viruses, remain under-examined compared to bacteriophages. Previous metagenomic studies of the Manikaran hot springs in India suggested an abundance of viral DNA, which prompted us to examine the virus–host (bacterial and archaeal) interactions in sediment and microbial mat samples collected from the thermal discharges. Here, we characterize the viruses (both bacterial and archaeal) from this Himalayan hot spring using both metagenomics assembly and electron microscopy. We utilized four shotgun samples from sediment (78–98°C) and two from microbial mats (50°C) to reconstruct 65 bacteriophage genomes (24–200 kb). We also identified 59 archaeal viruses that were notably abundant across the sediment samples. Whole-genome analyses of the reconstructed bacteriophage genomes revealed greater genomic conservation in sediments (65%) compared to microbial mats (49%). However, a minimal phage genome was still maintained across both sediment and microbial mats suggesting a common origin. To complement the metagenomic data, scanning-electron and helium-ion microscopy were used to reveal diverse morphotypes of Caudovirales and archaeal viruses. The genome level annotations provide further evidence for gene-level exchange between virus and host in these hot springs, and augments our knowledgebase for bacteriophages, archaeal viruses and Clustered Regularly Interspaced Short Palindromic Repeat cassettes, which provide a critical resource for studying viromes in extreme natural environments.

Pseudomonas PB1-Like Phages: Whole Genomes from Metagenomes Offer Insight into an Abundant Group of Bacteriophages

Despite the abundance, ubiquity and impact of environmental viruses, their inherent genomic plasticity and extreme diversity pose significant challenges for the examination of bacteriophages on Earth. Viral metagenomic studies have offered insight into broader aspects of phage ecology and repeatedly uncover genes to which we are currently unable to assign function. A combined effort of phage isolation and metagenomic survey of Chicago’s nearshore waters of Lake Michigan revealed the presence of Pbunaviruses, relatives of the Pseudomonas phage PB1. This prompted our expansive investigation of PB1-like phages. Genomic signatures of PB1-like phages and Pbunaviruses were identified, permitting the unambiguous distinction between the presence/absence of these phages in soils, freshwater and wastewater samples, as well as publicly available viral metagenomic datasets. This bioinformatic analysis led to the de novo assembly of nine novel PB1-like phage genomes from a metagenomic survey of samples collected from Lake Michigan. While this study finds that Pbunaviruses are abundant in various environments of Northern Illinois, genomic variation also exists to a considerable extent within individual communities.

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.

Metagenomic Sequencing for Surveillance of Food- and Waterborne Viral Diseases

A plethora of viruses can be transmitted by the food- and waterborne route. However, their recognition is challenging because of the variety of viruses, heterogeneity of symptoms, the lack of awareness of clinicians, and limited surveillance efforts. Classical food- and waterborne viral disease outbreaks are mainly caused by caliciviruses, but the source of the virus is often not known and the foodborne mode of transmission is difficult to discriminate from human-to-human transmission. Atypical food- and waterborne viral disease can be caused by viruses such as hepatitis A and hepatitis E. In addition, a source of novel emerging viruses with a potential to spread via the food- and waterborne route is the repeated interaction of humans with wildlife. Wildlife-to-human adaptation may give rise to self- limiting outbreaks in some cases, but when fully adjusted to the human host can be devastating. Metagenomic sequencing has been investigated as a promising solution for surveillance purposes as it detects all viruses in a single protocol, delivers additional genomic information for outbreak tracing, and detects novel unknown viruses. Nevertheless, several issues must be addressed to apply metagenomic sequencing in surveillance. First, sample preparation is difficult since the genomic material of viruses is generally overshadowed by host- and bacterial genomes. Second, several data analysis issues hamper the efficient, robust, and automated processing of metagenomic data. Third, interpretation of metagenomic data is hard, because of the lack of general knowledge of the virome in the food chain and the environment. Further developments in virus-specific nucleic acid extraction methods, bioinformatic data processing applications, and unifying data visualization tools are needed to gain insightful surveillance knowledge from suspect food samples.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

HoloVir: A Workflow for Investigating the Diversity and Function of Viruses in Invertebrate Holobionts

Abundant bioinformatics resources are available for the study of complex microbial metagenomes, however their utility in viral metagenomics is limited. HoloVir is a robust and flexible data analysis pipeline that provides an optimized and validated workflow for taxonomic and functional characterization of viral metagenomes derived from invertebrate holobionts. Simulated viral metagenomes comprising varying levels of viral diversity and abundance were used to determine the optimal assembly and gene prediction strategy, and multiple sequence assembly methods and gene prediction tools were tested in order to optimize our analysis workflow. HoloVir performs pairwise comparisons of single read and predicted gene datasets against the viral RefSeq database to assign taxonomy and additional comparison to phage-specific and cellular markers is undertaken to support the taxonomic assignments and identify potential cellular contamination. Broad functional classification of the predicted genes is provided by assignment of COG microbial functional category classifications using EggNOG and higher resolution functional analysis is achieved by searching for enrichment of specific Swiss-Prot keywords within the viral metagenome. Application of HoloVir to viral metagenomes from the coral Pocillopora damicornis and the sponge Rhopaloeides odorabile demonstrated that HoloVir provides a valuable tool to characterize holobiont viral communities across species, environments, or experiments.

High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved

The systems biology of microbial communities, organismal communities inhabiting all ecological niches on earth, has in recent years been strongly facilitated by the rapid development of experimental, sequencing and data analysis methods. Novel experimental approaches and binning methods in metagenomics render the semi-automatic reconstructions of near-complete genomes of uncultivable bacteria possible, while advances in high-resolution amplicon analysis allow for efficient and less biased taxonomic community characterization. This will also facilitate predictive modeling approaches, hitherto limited by the low resolution of metagenomic data. In this review, we pinpoint the most promising current developments in metagenomics. They facilitate microbial systems biology towards a systemic understanding of mechanisms in microbial communities with scopes of application in many areas of our daily life.

GenSeed-HMM: A Tool for Progressive Assembly Using Profile HMMs as Seeds and its Application in Alpavirinae Viral Discovery from Metagenomic Data

This work reports the development of GenSeed-HMM, a program that implements seed-driven progressive assembly, an approach to reconstruct specific sequences from unassembled data, starting from short nucleotide or protein seed sequences or profile Hidden Markov Models (HMM). The program can use any one of a number of sequence assemblers. Assembly is performed in multiple steps and relatively few reads are used in each cycle, consequently the program demands low computational resources. As a proof-of-concept and to demonstrate the power of HMM-driven progressive assemblies, GenSeed-HMM was applied to metagenomic datasets in the search for diverse ssDNA bacteriophages from the recently described Alpavirinae subfamily. Profile HMMs were built using Alpavirinae-specific regions from multiple sequence alignments (MSA) using either the viral protein 1 (VP1; major capsid protein) or VP4 (genome replication initiation protein). These profile HMMs were used by GenSeed-HMM (running Newbler assembler) as seeds to reconstruct viral genomes from sequencing datasets of human fecal samples. All contigs obtained were annotated and taxonomically classified using similarity searches and phylogenetic analyses. The most specific profile HMM seed enabled the reconstruction of 45 partial or complete Alpavirinae genomic sequences. A comparison with conventional (global) assembly of the same original dataset, using Newbler in a standalone execution, revealed that GenSeed-HMM outperformed global genomic assembly in several metrics employed. This approach is capable of detecting organisms that have not been used in the construction of the profile HMM, which opens up the possibility of diagnosing novel viruses, without previous specific information, constituting a de novo diagnosis. Additional applications include, but are not limited to, the specific assembly of extrachromosomal elements such as plastid and mitochondrial genomes from metagenomic data. Profile HMM seeds can also be used to reconstruct specific protein coding genes for gene diversity studies, and to determine all possible gene variants present in a metagenomic sample. Such surveys could be useful to detect the emergence of drug-resistance variants in sensitive environments such as hospitals and animal production facilities, where antibiotics are regularly used. Finally, GenSeed-HMM can be used as an adjunct for gap closure on assembly finishing projects, by using multiple contig ends as anchored seeds.

Sharing Data for Global Infectious Disease Surveillance and Outbreak Detection

Rapid global sharing and comparison of epidemiological and genomic data on infectious diseases would enable more rapid and efficient global outbreak control and tracking of diseases. Several barriers for global sharing exist but, in our opinion, the presumed magnitude of the problems appears larger than they are, and solutions can be found.

Viral metagenomics and blood safety

The characterization of the human blood-associated viral community (also called blood virome) is essential for epidemiological surveillance and to anticipate new potential threats for blood transfusion safety. Currently, the risk of blood-borne agent transmission of well-known viruses (HBV, HCV, HIV and HTLV) can be considered as under control in high-resource countries. However, other viruses unknown or unsuspected may be transmitted to recipients by blood-derived products. This is particularly relevant considering that a significant proportion of transfused patients are immunocompromised and more frequently subjected to fatal outcomes. Several measures to prevent transfusion transmission of unknown viruses have been implemented including the exclusion of at-risk donors, leukocyte reduction of donor blood, and physicochemical treatment of the different blood components. However, up to now there is no universal method for pathogen inactivation, which would be applicable for all types of blood components and, equally effective for all viral families. In addition, among available inactivation procedures of viral genomes, some of them are recognized to be less effective on non-enveloped viruses, and inadequate to inactivate higher viral titers in plasma pools or derivatives. Given this, there is the need to implement new methodologies for the discovery of unknown viruses that may affect blood transfusion. Viral metagenomics combined with High Throughput Sequencing appears as a promising approach for the identification and global surveillance of new and/or unexpected viruses that could impair blood transfusion safety.