The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them.National Science Foundation [1640775]Open access journal.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes

Tools allowing for the identification of viral sequences in host-associated and environmental metagenomes allows for a better understanding of the genetics and ecology of viruses and their hosts. Recently, new approaches using machine learning methods to distinguish viral from bacterial signal using k-mer sequence signatures were published for identifying viral contigs in metagenomes. The promise of these content-based approaches is the ability to discover new viruses, with no or few known relatives. In this perspective paper, we examine the use of the content-based machine learning tool VirFinder for the identification of viral sequences in aquatic metagenomes and explore the possibility of using ecosystem-focused models targeted to marine metagenomes. We discuss the impact of the training set composition on the tool performance and the current limitation for the retrieval of low abundance viral sequences in metagenomes. We identify potential biases that could arise from machine learning approaches for viral hunting in real-world datasets and suggest possible avenues to overcome them

Freshwater Metaviromics and Bacteriophages: A Current Assessment of the State of the Art in Relation to Bioinformatic Challenges

Advances in bioinformatics and sequencing technologies have allowed for the analysis of complex microbial communities at an unprecedented rate. While much focus is often placed on the cellular members of these communities, viruses play a pivotal role, particularly bacteria-infecting viruses (bacteriophages); phages mediate global biogeochemical processes and drive microbial evolution through bacterial grazing and horizontal gene transfer. Despite their importance and ubiquity in nature, very little is known about the diversity and structure of viral communities. Though the need for culture-based methods for viral identification has been somewhat circumvented through metagenomic techniques, the analysis of metaviromic data is marred with many unique issues. In this review, we examine the current bioinformatic approaches for metavirome analyses and the inherent challenges facing the field as illustrated by the ongoing efforts in the exploration of freshwater phage populations

Divergent responses of viral and bacterial communities in the gut microbiome to dietary disturbances in mice

To improve our understanding of the stability of mammalian intestinal communities, we characterized the responses of both bacterial and viral communities in murine fecal samples to dietary changes between high- and low-fat (LF) diets. Targeted DNA extraction methods for bacteria, virus-like particles and induced prophages were used to generate bacterial and viral metagenomes as well as 16S ribosomal RNA amplicons. Gut microbiome communities from two cohorts of C57BL/6 mice were characterized in a 6-week diet perturbation study in response to high fiber, LF and high-refined sugar, milkfat (MF) diets. The resulting metagenomes from induced bacterial prophages and extracellular viruses showed significant overlap, supporting a largely temperate viral lifestyle within these gut microbiomes. The resistance of baseline communities to dietary disturbances was evaluated, and we observed contrasting responses of baseline LF and MF bacterial and viral communities. In contrast to baseline LF viral communities and bacterial communities in both diet treatments, baseline MF viral communities were sensitive to dietary disturbances as reflected in their non-recovery during the washout period. The contrasting responses of bacterial and viral communities suggest that these communities can respond to perturbations independently of each other and highlight the potentially unique role of viruses in gut health

ViromeScan: a new tool for metagenomic viral community profiling

BACKGROUND: Bioinformatics tools available for metagenomic sequencing analysis are principally devoted to the identification of microorganisms populating an ecological niche, but they usually do not consider viruses. Only some software have been designed to profile the viral sequences, however they are not efficient in the characterization of viruses in the context of complex communities, like the intestinal microbiota, containing bacteria, archeabacteria, eukaryotic microorganisms and viruses. In any case, a comprehensive description of the host-microbiota interactions can not ignore the profile of eukaryotic viruses within the virome, as viruses are definitely critical for the regulation of the host immunophenotype. RESULTS: ViromeScan is an innovative metagenomic analysis tool that characterizes the taxonomy of the virome directly from raw data of next-generation sequencing. The tool uses hierarchical databases for eukaryotic viruses to unambiguously assign reads to viral species more accurately and >1000 fold faster than other existing approaches. We validated ViromeScan on synthetic microbial communities and applied it on metagenomic samples of the Human Microbiome Project, providing a sensitive eukaryotic virome profiling of different human body sites. CONCLUSIONS: ViromeScan allows the user to explore and taxonomically characterize the virome from metagenomic reads, efficiently denoising samples from reads of other microorganisms. This implies that users can fully characterize the microbiome, including bacteria and viruses, by shotgun metagenomic sequencing followed by different bioinformatic pipelines. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2446-3) contains supplementary material, which is available to authorized users

Optimizing protocols for extraction of bacteriophages prior to metagenomic analyses of phage communities in the human gut

BACKGROUND: The human gut is densely populated with archaea, eukaryotes, bacteria, and their viruses, such as bacteriophages. Advances in high-throughput sequencing (HTS) as well as bioinformatics have opened new opportunities for characterizing the viral communities harbored in our gut. However, limited attention has been given to the efficiency of protocols dealing with extraction of phages from fecal communities prior to HTS and their impact on the metagenomic dataset. RESULTS: We describe two optimized methods for extraction of phages from fecal samples based on tangential-flow filtration (TFF) and polyethylene glycol precipitation (PEG) approaches using an adapted method from a published protocol as control (literature-adapted protocol (LIT)). To quantify phage recovery, samples were spiked with low numbers of c2, ϕ29, and T4 phages (representatives of the Siphoviridae, Podoviridae, and Myoviridae families, respectively) and their concentration (plaque-forming units) followed at every step during the extraction procedure. Compared with LIT, TFF and PEG had higher recovery of all spiked phages, yielding up to 16 times more phage particles (PPs) and up to 68 times more phage DNA per volume, increasing thus the chances of extracting low abundant phages. TFF- and PEG-derived metaviromes showed 10 % increase in relative abundance of Caudovirales and unclassified phages infecting gut-associated bacteria (>92 % for TFF and PEG, 82.4 % for LIT). Our methods obtained lower relative abundance of the Myoviridae family (<16 %) as compared to the reference protocol (22 %). This decline, however, was not considered a true loss of Myoviridae phages but rather a greater level of extraction of Siphoviridae phages (TFF and PEG >32.5 %, LIT 22.6 %), which was achieved with the enhanced conditions of our procedures (e.g., reduced filter clogging). A high degree of phage diversity in samples extracted using TFF and PEG was documented by transmission electron microscopy. CONCLUSIONS: Two procedures (TFF and PEG) for extraction of bacteriophages from fecal samples were optimized using a set of spiked bacteriophages as process control. These protocols are highly efficient tools for extraction and purification of PPs prior to HTS in phage-metavirome studies. Our methods can be easily modified, being thus applicable and adjustable for in principle any solid environmental material in dissolution. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-015-0131-4) contains supplementary material, which is available to authorized users

The Human Gut Virome in Hypertension

Objectives: Previous studies have reported that the gut microbiome has an important link with the development of hypertension. Though previous researches have focused on the links of gut bacteria with hypertension, little has been known about the linkage of gut viruses to hypertension and the development of hypertension, largely due to the lack of data mining tools for such investigation. In this work, we have analyzed 196 fecal metagenomic data related to hypertension aiming to profile the gut virome and link the gut virome to pre-hypertension and hypertension.Design: Here, we have applied a statistically sound method for mining of gut virome data and linking gut virome to hypertension. We characterized the viral composition and bacterial composition of 196 samples, identified the viral-type of each sample and linked gut virome to hypertension.Results: We stratified these 196 fecal samples into two viral-types and selected 32 viruses as the biomarkers for these groups. We found that viruses could have a superior resolution and discrimination power than bacteria for differentiation of healthy samples and pre-hypertension samples, as well as hypertension samples. Moreover, as to the co-occurrence networks linking viruses and bacteria, we found increasingly pervasive virus-bacteria linkages from healthy people to pre-hypertension people to hypertension patients.Conclusion: Overall, our results have shown ample indications of the link between human gut virome and hypertension, and could help provide microbial solutions toward early diagnoses of hypertension

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

BackgroundThe prediction of bacteriophage sequences in metagenomic datasets has become a topic of considerable interest, leading to the development of many novel bioinformatic tools. A comparative analysis of ten state-of-the-art phage identification tools was performed to inform their usage in microbiome research.MethodsArtificial contigs generated from complete RefSeq genomes representing phages, plasmids, and chromosomes, and a previously sequenced mock community containing four phage species, were used to evaluate the precision, recall, and F1 scores of the tools. We also generated a dataset of randomly shuffled sequences to quantify false-positive calls. In addition, a set of previously simulated viromes was used to assess diversity bias in each tool’s output.ResultsVIBRANT and VirSorter2 achieved the highest F1 scores (0.93) in the RefSeq artificial contigs dataset, with several other tools also performing well. Kraken2 had the highest F1 score (0.86) in the mock community benchmark by a large margin (0.3 higher than DeepVirFinder in second place), mainly due to its high precision (0.96). Generally, k-mer-based tools performed better than reference similarity tools and gene-based methods. Several tools, most notably PPR-Meta, called a high number of false positives in the randomly shuffled sequences. When analysing the diversity of the genomes that each tool predicted from a virome set, most tools produced a viral genome set that had similar alpha- and beta-diversity patterns to the original population, with Seeker being a notable exception.ConclusionsThis study provides key metrics used to assess performance of phage detection tools, offers a framework for further comparison of additional viral discovery tools, and discusses optimal strategies for using these tools. We highlight that the choice of tool for identification of phages in metagenomic datasets, as well as their parameters, can bias the results and provide pointers for different use case scenarios. We have also made our benchmarking dataset available for download in order to facilitate future comparisons of phage identification tools

Vipie: web pipeline for parallel characterization of viral populations from multiple NGS samples

BioMed Central open acces

Novel phages of healthy skin metaviromes from South Africa

Recent skin metagenomic studies have investigated the harbored viral diversity and its possible influence on healthy skin microbial populations, and tried to establish global patterns of skin-phage evolution. However, the detail associated with the phages that potentially play a role in skin health has not been investigated. While skin metagenome and -metavirome studies have indicated that the skin virome is highly site specific and shows marked interpersonal variation, they have not assessed the presence/absence of individual phages. Here, we took a semi-culture independent approach (metaviromic) to better understand the composition of phage communities on skin from South African study participants. Our data set adds over 130 new phage species of the skin to existing databases. We demonstrated that identical phages were present on different individuals and in different body sites, and we conducted a detailed analysis of the structural organization of these phages. We further found that a bacteriophage related to the Staphylococcus capitis phage Stb20 may be a common skin commensal virus potentially regulating its host and its activities on the ski