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

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

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

Genomic and taxonomic evaluation of 38 Treponema prophage sequences

BACKGROUND

Despite Spirochetales being a ubiquitous and medically important order of bacteria infecting both humans and animals, there is extremely limited information regarding their bacteriophages. Of the genus Treponema, there is just a single reported characterised prophage.

RESULTS

We applied a bioinformatic approach on 24 previously published Treponema genomes to identify and characterise putative treponemal prophages. Thirteen of the genomes did not contain any detectable prophage regions. The remaining eleven contained 38 prophage sequences, with between one and eight putative prophages in each bacterial genome. The prophage regions ranged from 12.4 to 75.1 kb, with between 27 and 171 protein coding sequences. Phylogenetic analysis revealed that 24 of the prophages formed three distinct sequence clusters, identifying putative myoviral and siphoviral morphology. ViPTree analysis demonstrated that the identified sequences were novel when compared to known double stranded DNA bacteriophage genomes.

CONCLUSIONS

In this study, we have started to address the knowledge gap on treponeme bacteriophages by characterising 38 prophage sequences in 24 treponeme genomes. Using bioinformatic approaches, we have been able to identify and compare the prophage-like elements with respect to other bacteriophages, their gene content, and their potential to be a functional and inducible bacteriophage, which in turn can help focus our attention on specific prophages to investigate further.

Tools and methodology to in silico phage discovery in freshwater environments

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

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

BACKGROUND

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance.

RESULTS

We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered.

CONCLUSIONS

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

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.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

VirGrapher: a graph-based viral identifier for long sequences from metagenomes

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.

The insights into the phage communities of fermented foods in the age of viral metagenomics

Phages play a critical role in the assembly and regulation of fermented food microbiome through lysis and lysogenic lifestyle, which in turn affects the yield and quality of fermented foods. Therefore, it is important to investigate and characterize the diversity and function of phages under complex microbial communities and nutrient substrate conditions to provide novel insights into the regulation of traditional spontaneous fermentation. Viral metagenomics has gradually garnered increasing attention in fermented food research to elucidate phage functions and characterize the interactions between phages and the microbial community. Advances in this technology have uncovered a wide range of phages associated with the production of traditional fermented foods and beverages. This paper reviews the common methods of viral metagenomics applied in fermented food research, and summarizes the ecological functions of phages in traditional fermented foods. In the future, combining viral metagenomics with culturable methods and metagenomics will broaden the scope of research on fermented food systems, revealing the complex role of phages and intricate phage-bacterium interactions.

VESPA: an optimized protocol for accurate metabarcoding-based characterization of vertebrate eukaryotic endosymbiont and parasite assemblages

Protocols for characterizing taxonomic assemblages by deep sequencing of short DNA barcode regions (metabarcoding) have revolutionized our understanding of microbial communities and are standardized for bacteria, archaea, and fungi. Unfortunately, comparable methods for host-associated eukaryotes have lagged due to technical challenges. Despite 54 published studies, issues remain with primer complementarity, off-target amplification, and lack of external validation. Here, we present VESPA (Vertebrate Eukaryotic endoSymbiont and Parasite Analysis) primers and optimized metabarcoding protocol for host-associated eukaryotic community analysis. Using in silico prediction, panel PCR, engineered mock community standards, and clinical samples, we demonstrate VESPA to be more effective at resolving host-associated eukaryotic assemblages than previously published methods and to minimize off-target amplification. When applied to human and non-human primate samples, VESPA enables reconstruction of host-associated eukaryotic endosymbiont communities more accurately and at finer taxonomic resolution than microscopy. VESPA has the potential to advance basic and translational science on vertebrate eukaryotic endosymbiont communities, similar to achievements made for bacterial, archaeal, and fungal microbiomes.

Metagenomic assembly is the main bottleneck in the identification of mobile genetic elements

Antimicrobial resistance genes (ARG) are commonly found on acquired mobile genetic elements (MGEs) such as plasmids or transposons. Understanding the spread of resistance genes associated with mobile elements (mARGs) across different hosts and environments requires linking ARGs to the existing mobile reservoir within bacterial communities. However, reconstructing mARGs in metagenomic data from diverse ecosystems poses computational challenges, including genome fragment reconstruction (assembly), high-throughput annotation of MGEs, and identification of their association with ARGs. Recently, several bioinformatics tools have been developed to identify assembled fragments of plasmids, phages, and insertion sequence (IS) elements in metagenomic data. These methods can help in understanding the dissemination of mARGs. To streamline the process of identifying mARGs in multiple samples, we combined these tools in an automated high-throughput open-source pipeline, MetaMobilePicker, that identifies ARGs associated with plasmids, IS elements and phages, starting from short metagenomic sequencing reads. This pipeline was used to identify these three elements on a simplified simulated metagenome dataset, comprising whole genome sequences from seven clinically relevant bacterial species containing 55 ARGs, nine plasmids and five phages. The results demonstrated moderate precision for the identification of plasmids (0.57) and phages (0.71), and moderate sensitivity of identification of IS elements (0.58) and ARGs (0.70). In this study, we aim to assess the main causes of this moderate performance of the MGE prediction tools in a comprehensive manner. We conducted a systematic benchmark, considering metagenomic read coverage, contig length cutoffs and investigating the performance of the classification algorithms. Our analysis revealed that the metagenomic assembly process is the primary bottleneck when linking ARGs to identified MGEs in short-read metagenomics sequencing experiments rather than ARGs and MGEs identification by the different tools.

Hecatomb: an integrated software platform for viral metagenomics

BACKGROUND

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

FINDINGS

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

CONCLUSION

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

Bacteriophage communities are a reservoir of unexplored microbial diversity in neonatal health and disease

Acquisition and development of the gut microbiome are vital for immune education in neonates, especially those born preterm. As such, microbial communities have been extensively studied in the context of postnatal health and disease. Bacterial communities have been the focus of research in this area due to the relative ease of targeted bacterial sequencing and the availability of databases to align and validate sequencing data. Recent increases in high-throughput metagenomic sequencing accessibility have facilitated research to investigate bacteriophages within the context of neonatal gut microbial communities. Focusing on unexplored viral diversity, has identified novel bacteriophage species and previously uncharacterised viral diversity. In doing so, studies have highlighted links between bacteriophages and bacterial community structure in the context of health and disease. However, much remains unknown about the complex relationships between bacteriophages, the bacteria they infect and their human host. With a particular focus on preterm infants, this review highlights opportunities to explore the influence of bacteriophages on developing microbial communities and the tripartite relationships between bacteriophages, bacteria and the neonatal human host. We suggest a focus on expanding collections of isolated bacteriophages that will further our understanding of the growing numbers of bacteriophages identified in metagenomes.

VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.