Comparison of k-mer-based de novo comparative metagenomic tools and approaches.

Aim: Comparative metagenomic analysis requires measuring a pairwise similarity between metagenomes in the dataset. Reference-based methods that compute a beta-diversity distance between two metagenomes are highly dependent on the quality and completeness of the reference database, and their application on less studied microbiota can be challenging. On the other hand, de-novo comparative metagenomic methods only rely on the sequence composition of metagenomes to compare datasets. While each one of these approaches has its strengths and limitations, their comparison is currently limited. Methods: We developed sets of simulated short-reads metagenomes to (1) compare k-mer-based and taxonomy-based distances and evaluate the impact of technical and biological variables on these metrics and (2) evaluate the effect of k-mer sketching and filtering. We used a real-world metagenomic dataset to provide an overview of the currently available tools for de novo metagenomic comparative analysis. Results: Using simulated metagenomes of known composition and controlled error rate, we showed that k-mer-based distance metrics were well correlated to the taxonomic distance metric for quantitative Beta-diversity metrics, but the correlation was low for presence/absence distances. The community complexity in terms of taxa richness and the sequencing depth significantly affected the quality of the k-mer-based distances, while the impact of low amounts of sequence contamination and sequencing error was limited. Finally, we benchmarked currently available de-novo comparative metagenomic tools and compared their output on two datasets of fecal metagenomes and showed that most k-mer-based tools were able to recapitulate the data structure observed using taxonomic approaches. Conclusion: This study expands our understanding of the strength and limitations of k-mer-based de novo comparative metagenomic approaches and aims to provide concrete guidelines for researchers interested in applying these approaches to their metagenomic datasets.

Evaluation of computational phage detection tools for metagenomic datasets.

Introduction: As new computational tools for detecting phage in metagenomes are being rapidly developed, a critical need has emerged to develop systematic benchmarks. Methods: In this study, we surveyed 19 metagenomic phage detection tools, 9 of which could be installed and run at scale. Those 9 tools were assessed on several benchmark challenges. Fragmented reference genomes are used to assess the effects of fragment length, low viral content, phage taxonomy, robustness to eukaryotic contamination, and computational resource usage. Simulated metagenomes are used to assess the effects of sequencing and assembly quality on the tool performances. Finally, real human gut metagenomes and viromes are used to assess the differences and similarities in the phage communities predicted by the tools. Results: We find that the various tools yield strikingly different results. Generally, tools that use a homology approach (VirSorter, MARVEL, viralVerify, VIBRANT, and VirSorter2) demonstrate low false positive rates and robustness to eukaryotic contamination. Conversely, tools that use a sequence composition approach (VirFinder, DeepVirFinder, Seeker), and MetaPhinder, have higher sensitivity, including to phages with less representation in reference databases. These differences led to widely differing predicted phage communities in human gut metagenomes, with nearly 80% of contigs being marked as phage by at least one tool and a maximum overlap of 38.8% between any two tools. While the results were more consistent among the tools on viromes, the differences in results were still significant, with a maximum overlap of 60.65%. Discussion: Importantly, the benchmark datasets developed in this study are publicly available and reusable to enable the future comparability of new tools developed.

Network analysis reveals dysregulated functional patterns in type II diabetic skin.

Skin disorders are one of the most common complications of type II diabetes (T2DM). Long-term effects of high blood glucose leave individuals with T2DM more susceptible to cutaneous diseases, but its underlying molecular mechanisms are unclear. Network-based methods consider the complex interactions between genes which can complement the analysis of single genes in previous research. Here, we use network analysis and topological properties to systematically investigate dysregulated gene co-expression patterns in type II diabetic skin with skin samples from the Genotype-Tissue Expression database. Our final network consisted of 8812 genes from 73 subjects with T2DM and 147 non-T2DM subjects matched for age, sex, and race. Two gene modules significantly related to T2DM were functionally enriched in the pathway lipid metabolism, activated by PPARA and SREBF (SREBP). Transcription factors KLF10, KLF4, SP1, and microRNA-21 were predicted to be important regulators of gene expression in these modules. Intramodular analysis and betweenness centrality identified NCOA6 as the hub gene while KHSRP and SIN3B are key coordinators that influence molecular activities differently between T2DM and non-T2DM populations. We built a TF-miRNA-mRNA regulatory network to reveal the novel mechanism (miR-21-PPARA-NCOA6) of dysregulated keratinocyte proliferation, differentiation, and migration in diabetic skin, which may provide new insights into the susceptibility of skin disorders in T2DM patients. Hub genes and key coordinators may serve as therapeutic targets to improve diabetic skincare.

Ontology-driven analysis of marine metagenomics: what more can we learn from our data?

BACKGROUND: The proliferation of metagenomic sequencing technologies has enabled novel insights into the functional genomic potentials and taxonomic structure of microbial communities. However, cyberinfrastructure efforts to manage and enable the reproducible analysis of sequence data have not kept pace. Thus, there is increasing recognition of the need to make metagenomic data discoverable within machine-searchable frameworks compliant with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship. Although a variety of metagenomic web services exist, none currently leverage the hierarchically structured terminology encoded within common life science ontologies to programmatically discover data. RESULTS: Here, we integrate large-scale marine metagenomic datasets with community-driven life science ontologies into a novel FAIR web service. This approach enables the retrieval of data discovered by intersecting the knowledge represented within ontologies against the functional genomic potential and taxonomic structure computed from marine sequencing data. Our findings highlight various microbial functional and taxonomic patterns relevant to the ecology of prokaryotes in various aquatic environments. CONCLUSIONS: In this work, we present and evaluate a novel Semantic Web architecture that can be used to ask novel biological questions of existing marine metagenomic datasets. Finally, the FAIR ontology searchable data products provided by our API can be leveraged by future research efforts.

Role of the gut microbiome in cardiovascular drug response: The potential for clinical application.

Response to cardiovascular drugs can vary greatly between individuals, and the role of the microbiome in this variability is being increasingly appreciated. Recent evidence indicates that bacteria and other microbes are responsible for direct and indirect effects on drug efficacy and toxicity. Pharmacomicrobiomics aims to uncover variability in drug response due to microbes in the human body, which may alter drug disposition through microbial metabolism, interference by microbial metabolites, or modification of host enzymes. In this review, we present recent advances in our understanding of the interplay between microbes, host metabolism, and cardiovascular drugs. We report numerous cardiovascular drugs with evidence of, or potential for, gut-microbe interactions. However, the effects of gut microbiota on many cardiovascular drugs are yet uninvestigated. Finally, we consider potential clinical applications for the described findings.

Ontology-Enriched Specifications Enabling Findable, Accessible, Interoperable, and Reusable Marine Metagenomic Datasets in Cyberinfrastructure Systems.

Marine microbial ecology requires the systematic comparison of biogeochemical and sequence data to analyze environmental influences on the distribution and variability of microbial communities. With ever-increasing quantities of metagenomic data, there is a growing need to make datasets Findable, Accessible, Interoperable, and Reusable (FAIR) across diverse ecosystems. FAIR data is essential to developing analytical frameworks that integrate microbiological, genomic, ecological, oceanographic, and computational methods. Although community standards defining the minimal metadata required to accompany sequence data exist, they haven't been consistently used across projects, precluding interoperability. Moreover, these data are not machine-actionable or discoverable by cyberinfrastructure systems. By making 'omic and physicochemical datasets FAIR to machine systems, we can enable sequence data discovery and reuse based on machine-readable descriptions of environments or physicochemical gradients. In this work, we developed a novel technical specification for dataset encapsulation for the FAIR reuse of marine metagenomic and physicochemical datasets within cyberinfrastructure systems. This includes using Frictionless Data Packages enriched with terminology from environmental and life-science ontologies to annotate measured variables, their units, and the measurement devices used. This approach was implemented in Planet Microbe, a cyberinfrastructure platform and marine metagenomic web-portal. Here, we discuss the data properties built into the specification to make global ocean datasets FAIR within the Planet Microbe portal. We additionally discuss the selection of, and contributions to marine-science ontologies used within the specification. Finally, we use the system to discover data by which to answer various biological questions about environments, physicochemical gradients, and microbial communities in meta-analyses. This work represents a future direction in marine metagenomic research by proposing a specification for FAIR dataset encapsulation that, if adopted within cyberinfrastructure systems, would automate the discovery, exchange, and re-use of data needed to answer broader reaching questions than originally intended.

An adaptable and non-invasive method for tracking Bifidobacterium animalis subspecies lactis 420 in the mouse gut.

Probiotic strains from the Bifidobacterium or Lactobacillus genera improve health outcomes in models of metabolic and cardiovascular disease. Yet, underlying mechanisms governing these improved health outcomes are rooted in the interaction of gut microbiota, intestinal interface, and probiotic strain. Central to defining the underlying mechanisms governing these improved health outcomes is the development of adaptable and non-invasive tools to study probiotic localization and colonization within the host gut microbiome. The objective of this study was to test labeling and tracking efficacy of Bifidobacterium animalis subspecies lactis 420 (B420) using a common clinical imaging agent, indocyanine green (ICG). ICG was an effective in situ labeling agent visualized in either intact mouse or excised gastrointestinal (GI) tract at different time intervals. Quantitative PCR was used to validate ICG visualization of B420, which also demonstrated that B420 transit time matched normal murine GI motility (~8 hours). Contrary to previous thoughts, B420 did not colonize any region of the GI tract whether following a single bolus or daily administration for up to 10 days. We conclude that ICG may provide a useful tool to visualize and track probiotic species such as B420 without implementing complex molecular and genetic tools. Proof-of-concept studies indicate that B420 did not colonize and establish residency align the murine GI tract.

Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative's Workshop and Follow-On Activities.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

Cyanolichen microbiome contains novel viruses that encode genes to promote microbial metabolism.

Lichen thalli are formed through the symbiotic association of a filamentous fungus and photosynthetic green alga and/or cyanobacterium. Recent studies have revealed lichens also host highly diverse communities of secondary fungal and bacterial symbionts, yet few studies have examined the viral component within these complex symbioses. Here, we describe viral biodiversity and functions in cyanolichens collected from across North America and Europe. As current machine-learning viral-detection tools are not trained on complex eukaryotic metagenomes, we first developed efficient methods to remove eukaryotic reads prior to viral detection and a custom pipeline to validate viral contigs predicted with three machine-learning methods. Our resulting high-quality viral data illustrate that every cyanolichen thallus contains diverse viruses that are distinct from viruses in other terrestrial ecosystems. In addition to cyanobacteria, predicted viral hosts include other lichen-associated bacterial lineages and algae, although a large fraction of viral contigs had no host prediction. Functional annotation of cyanolichen viral sequences predicts numerous viral-encoded auxiliary metabolic genes (AMGs) involved in amino acid, nucleotide, and carbohydrate metabolism, including AMGs for secondary metabolism (antibiotics and antimicrobials) and fatty acid biosynthesis. Overall, the diversity of cyanolichen AMGs suggests that viruses may alter microbial interactions within these complex symbiotic assemblages.

Planet Microbe: a platform for marine microbiology to discover and analyze interconnected 'omics and environmental data.

In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each 'omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.

PuMA: A papillomavirus genome annotation tool.

High-throughput sequencing technologies provide unprecedented power to identify novel viruses from a wide variety of (environmental) samples. The field of 'viral metagenomics' has dramatically expanded our understanding of viral diversity. Viral metagenomic approaches imply that many novel viruses will not be described by researchers who are experts on (the genomic organization of) that virus family. We have developed the papillomavirus annotation tool (PuMA) to provide researchers with a convenient and reproducible method to annotate and report novel papillomaviruses. PuMA currently correctly annotates 99% of the papillomavirus genes when benchmarked against the 655 reference genomes in the papillomavirus episteme. Compared to another viral annotation pipeline, PuMA annotates more viral features while being more accurate. To demonstrate its general applicability, we also developed a preliminary version of PuMA that can annotate polyomaviruses. PuMA is available on GitHub (https://github.com/KVD-lab/puma) and through the iMicrobe online environment (https://www.imicrobe.us/#/apps/puma).

The dynamic wound microbiome.

BACKGROUND: Diabetic foot ulcers (DFUs) account for the majority of all limb amputations and hospitalizations due to diabetes complications. With 30 million cases of diabetes in the USA and 500,000 new diagnoses each year, DFUs are a growing health problem. Diabetes patients with limb amputations have high postoperative mortality, a high rate of secondary amputation, prolonged inpatient hospital stays, and a high incidence of re-hospitalization. DFU-associated amputations constitute a significant burden on healthcare resources that cost more than 10 billion dollars per year. Currently, there is no way to identify wounds that will heal versus those that will become severely infected and require amputation. MAIN BODY: Accurate identification of causative pathogens in diabetic foot ulcers is a critical component of effective treatment. Compared to traditional culture-based methods, advanced sequencing technologies provide more comprehensive and unbiased profiling on wound microbiome with a higher taxonomic resolution, as well as functional annotation such as virulence and antibiotic resistance. In this review, we summarize the latest developments in defining the microbiology of diabetic foot ulcers that have been unveiled by sequencing technologies and discuss both the future promises and current limitations of these approaches. In particular, we highlight the temporal patterns and system dynamics in the diabetic foot microbiome monitored and measured during wound progression and medical intervention, and explore the feasibility of molecular diagnostics in clinics. CONCLUSION: Molecular tests conducted during weekly office visits to clean and examine DFUs would allow clinicians to offer personalized treatment and antibiotic therapy. Personalized wound management could reduce healthcare costs, improve quality of life for patients, and recoup lost productivity that is important not only to the patient, but also to healthcare payers and providers. These efforts could also improve antibiotic stewardship and control the rise of "superbugs" vital to global health.

Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity.

Infections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or even replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the workflow are optimized and tested, including data analysis and computational time and resources. We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. All three classifiers identified the organisms present in their default databases from a mock bacterial community of 20 organisms, but only Centrifuge had no false positives. In addition, Centrifuge required far less computational resources and time for analysis. Centrifuge analysis of metagenomes obtained from samples of VAP, infected DFUs, and FN showed Centrifuge identified pathogenic bacteria and one virus that were corroborated by culture or a clinical PCR assay. Importantly, in both diabetic foot ulcer patients, metagenomic sequencing identified pathogens 4-6 weeks before culture. Finally, we show that Centrifuge results were minimally affected by elimination of time-consuming read quality control and host screening steps.

iMicrobe: Tools and data-dreaiven discovery platform for the microbiome sciences.

BACKGROUND: Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. FINDINGS: The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation-supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). CONCLUSIONS: iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

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.

Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons.

Background: Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content. Results: We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community. Conclusions: A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.

Minimum Information about an Uncultivated Virus Genome (MIUViG).

We present an extension of the Minimum Information about any (x) Sequence (MIxS) standard for reporting sequences of uncultivated virus genomes. Minimum Information about an Uncultivated Virus Genome (MIUViG) standards were developed within the Genomic Standards Consortium framework and include virus origin, genome quality, genome annotation, taxonomic classification, biogeographic distribution and in silico host prediction. Community-wide adoption of MIUViG standards, which complement the Minimum Information about a Single Amplified Genome (MISAG) and Metagenome-Assembled Genome (MIMAG) standards for uncultivated bacteria and archaea, will improve the reporting of uncultivated virus genomes in public databases. In turn, this should enable more robust comparative studies and a systematic exploration of the global virosphere.