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The eHOMD ( http://www.ehomd.org ) is a valuable resource for researchers, from basic to clinical, who study the microbiomes and the individual microbes in body sites in the human aerodigestive tract, which includes the nasal passages, sinuses, throat, esophagus, and mouth, and the lower respiratory tract, in health and disease. The eHOMD is an actively curated, web-based, open-access resource. eHOMD provides the following: (i) species-level taxonomy based on grouping 16S rRNA gene sequences at 98.5% identity, (ii) a systematic naming scheme for unnamed and/or uncultivated microbial taxa, (iii) reference genomes to facilitate metagenomic, metatranscriptomic, and proteomic studies and (iv) convenient cross-links to other databases (e.g., PubMed and Entrez). By facilitating the assignment of species names to sequences, the eHOMD is a vital resource for enhancing the clinical relevance of 16S rRNA gene-based microbiome studies, as well as metagenomic studies. The expanded Human Oral Microbiome Database (eHOMD) is a comprehensive microbiome database for sites along the human aerodigestive tract that revealed new insights into the nostril microbiome. The eHOMD provides well-curated 16S rRNA gene reference sequences linked to available genomes and enables assignment of species-level taxonomy to most next-generation sequences derived from diverse aerodigestive tract sites, including the nasal passages, sinuses, throat, esophagus, and mouth. Using minimum entropy decomposition coupled with the RDP Classifier and our eHOMD V1-V3 training set, we reanalyzed 16S rRNA V1-V3 sequences from the nostrils of 210 Human Microbiome Project participants at the species level, revealing four key insights. First, we discovered that Lawsonella clevelandensis , a recently named bacterium, and Neisseriaceae [G-1] HMT-174, a previously unrecognized bacterium, are common in adult nostrils. Second, just 19 species accounted for 90% of the total sequences from all participants. Third, 1 of these 19 species belonged to a currently uncultivated genus. Fourth, for 94% of the participants, 2 to 10 species constituted 90% of their sequences, indicating that the nostril microbiome may be represented by limited consortia. These insights highlight the strengths of the nostril microbiome as a model system for studying interspecies interactions and microbiome function. Also, in this cohort, three common nasal species ( Dolosigranulum pigrum and two Corynebacterium species) showed positive differential abundance when the pathobiont Staphylococcus aureus was absent, generating hypotheses regarding colonization resistance. By facilitating species-level taxonomic assignment to microbes from the human aerodigestive tract, the eHOMD is a vital resource enhancing clinical relevance of microbiome studies. IMPORTANCE The eHOMD ( http://www.ehomd.org ) is a valuable resource for researchers, from basic to clinical, who study the microbiomes and the individual microbes in body sites in the human aerodigestive tract, which includes the nasal passages, sinuses, throat, esophagus, and mouth, and the lower respiratory tract, in health and disease. The eHOMD is an actively curated, web-based, open-access resource. eHOMD provides the following: (i) species-level taxonomy based on grouping 16S rRNA gene sequences at 98.5% identity, (ii) a systematic naming scheme for unnamed and/or uncultivated microbial taxa, (iii) reference genomes to facilitate metagenomic, metatranscriptomic, and proteomic studies and (iv) convenient cross-links to other databases (e.g., PubMed and Entrez). By facilitating the assignment of species names to sequences, the eHOMD is a vital resource for enhancing the clinical relevance of 16S rRNA gene-based microbiome studies, as well as metagenomic studies.

ABSTRACTTyping of methicillin-resistant Staphylococcus aureus (MRSA) is important in infection control and surveillance. The current nomenclature of MRSA includes the genetic background of the S. aureus strain determined by multilocus sequence typing (MLST) or equivalent methods like spa typing and typing of the mobile genetic element staphylococcal cassette chromosome mec (SCCmec), which carries the mecA or mecC gene. Whereas MLST and spa typing are relatively simple, typing of SCCmec is less trivial because of its heterogeneity. Whole-genome sequencing (WGS) provides the essential data for typing of the genetic background and SCCmec, but so far, no bioinformatic tools for SCCmec typing have been available. Here, we report the development and evaluation of SCCmecFinder for characterization of the SCCmec element from S. aureus WGS data. SCCmecFinder is able to identify all SCCmec element types, designated I to XIII, with subtyping of SCCmec types IV (2B) and V (5C2). SCCmec elements are characterized by two different gene prediction approaches to achieve correct annotation, a Basic Local Alignment Search Tool (BLAST)-based approach and a k-mer-based approach. Evaluation of SCCmecFinder by using a diverse collection of clinical isolates (n = 93) showed a high typeability level of 96.7%, which increased to 98.9% upon modification of the default settings. In conclusion, SCCmecFinder can be an alternative to more laborious SCCmec typing methods and is freely available at https://cge.cbs.dtu.dk/services/SCCmecFinder.IMPORTANCE SCCmec in MRSA is acknowledged to be of importance not only because it contains the mecA or mecC gene but also for staphylococcal adaptation to different environments, e.g., in hospitals, the community, and livestock. Typing of SCCmec by PCR techniques has, because of its heterogeneity, been challenging, and whole-genome sequencing has only partially solved this since no good bioinformatic tools have been available. In this article, we describe the development of a new bioinformatic tool, SCCmecFinder, that includes most of the needs for infection control professionals and researchers regarding the interpretation of SCCmec elements. The software detects all of the SCCmec elements accepted by the International Working Group on the Classification of Staphylococcal Cassette Chromosome Elements, and users will be prompted if diverging and potential new elements are uploaded. Furthermore, SCCmecFinder will be curated and updated as new elements are found and it is easy to use and freely accessible.

The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community. Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/ . The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community.

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse. Research of geoscience focuses on processing georeferenced quantitative data (e.g., rock parameters, geochemical tests, geophysical surveys and satellite imagery) for discovering new knowledge. Geological knowledge is the cognitive result of human knowledge of the spatial distribution, evolution and interaction patterns of geological objects or processes. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. In this paper, we propose a novel framework that can extract the geological knowledge graph (GKG) from public reports relating to a modelling study. Based on the analysis of basic questions answered by geology, we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of “geological concepts-geological entities-geological relations” to describe semantic units of geological knowledge and their logic relations. Finally, based on the characteristics of mineral resource reports, the geological knowledge representation model oriented to “object relationships” and the hierarchical geological knowledge representation model oriented to “process relationships” are proposed with reference to the commonly used geological knowledge graph representation. The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

The ability to manipulate the genome of paramyxoviruses and evaluate the effects of these changes at the phenotypic level is a powerful tool for the investigation of specific aspects of the viral life cycle and viral pathogenesis. However, reverse genetics systems for paramyxoviruses are notoriously inefficient, when successful. The ability to efficiently and robustly rescue paramyxovirus reverse genetics systems can be used to answer basic questions about the biology of paramyxoviruses, as well as to facilitate the considerable translational efforts being devoted to developing live attenuated paramyxovirus vaccine vectors. The notoriously low efficiency of Paramyxoviridae reverse genetics systems has posed a limiting barrier to the study of viruses in this family. Previous approaches to reverse genetics have utilized a wide variety of techniques to overcome the technical hurdles. Although robustness (i.e., the number of attempts that result in successful rescue) has been improved in some systems with the use of stable cell lines, the efficiency of rescue (i.e., the proportion of transfected cells that yield at least one successful rescue event) has remained low. We have substantially increased rescue efficiency for representative viruses from all five major Paramyxoviridae genera (from ~1 in 10 6 -10 7 to ~1 in 10 2 -10 3 transfected cells) by the addition of a self-cleaving hammerhead ribozyme (Hh-Rbz) sequence immediately preceding the start of the recombinant viral antigenome and the use of a codon-optimized T7 polymerase (T7opt) gene to drive paramyxovirus rescue. Here, we report a strategy for robust, reliable, and high-efficiency rescue of paramyxovirus reverse genetics systems, featuring several major improvements: (i) a vaccinia virus-free method, (ii) freedom to use any transfectable cell type for viral rescue, (iii) a single-step transfection protocol, and (iv) use of the optimal T7 promoter sequence for high transcription levels from the antigenomic plasmid without incorporation of nontemplated G residues. The robustness of our T7opt-HhRbz system also allows for greater latitude in the ratios of transfected accessory plasmids used that result in successful rescue. Thus, our system may facilitate the rescue and interrogation of the increasing number of emerging paramyxoviruses. IMPORTANCE The ability to manipulate the genome of paramyxoviruses and evaluate the effects of these changes at the phenotypic level is a powerful tool for the investigation of specific aspects of the viral life cycle and viral pathogenesis. However, reverse genetics systems for paramyxoviruses are notoriously inefficient, when successful. The ability to efficiently and robustly rescue paramyxovirus reverse genetics systems can be used to answer basic questions about the biology of paramyxoviruses, as well as to facilitate the considerable translational efforts being devoted to developing live attenuated paramyxovirus vaccine vectors.

Genome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

Fermented foods have microbial communities that influence food safety, flavor, and human health. Microbial Food DataBase (MiFoDB), an alignment-based sequencing workflow and database, addresses the limitations of existing tools by enabling strain-level resolution, identifying novel genomes, and providing functional insights into microbial communities. Applying MiFoDB to fermented food samples, we demonstrate its ability to uncover novel species, track microbial strains across substrates, and integrate functional annotations. Additionally, the outlined workflow is highly customizable and can be used to generate alignment-based databases for other microbial ecosystems. This work highlights the importance of fermentation-specific workflows for studying microbial food ecosystems, advancing food safety, sustainability, and innovation in fermented food research.

The three Rs (reduction, refinement, and replacement, which govern ethical and humane use of animals in scientific research) compel investigators to consider ways to maximize value and impact of in vivo experimentation using a minimum number of animals. One way to achieve this is to aggregate and share publicly results from multiple studies for subsequent investigation. This resource report describes such a data set, reporting infectious virus titers detected in multiple tissues from influenza A virus-infected ferrets, day 3 post-inoculation, aggregated from studies conducted over multiple decades by one research group. We provide usage notes for best practices to inform analysis of these data by other investigators and report results of exploratory studies that illustrate conclusions that can be informed by analyses of this nature. Future public release of like data sets by other groups with similar historical archives may be informed by the practices and formatting described herein.

A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn . For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub ( https://github.com/qibebt-bioinfo/meta-storms ). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

Determining reinfection rates and correlates of protection against SARS-CoV-2 infection induced by both natural infection and vaccination is of high significance for the prevention and control of coronavirus disease 2019 (COVID-19). Furthermore, understanding reinfections or infection after vaccination and the role immune escape plays in these scenarios will inform the need for updates of the current SARS-CoV-2 vaccines and help update guidelines suitable for the postpandemic world. To understand reinfection rates and correlates of protection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we established eight different longitudinal cohorts in 2020 under the umbrella of the PARIS (Protection Associated with Rapid Immunity to SARS-CoV-2)/SPARTA (SARS SeroPrevalence And Respiratory Tract Assessment) studies. Here, we describe the PARIS/SPARTA cohorts, the harmonized assays and analysis that are performed across the cohorts, as well as case definitions for SARS-CoV-2 infection and reinfection that have been established by the team of PARIS/SPARTA investigators. IMPORTANCE Determining reinfection rates and correlates of protection against SARS-CoV-2 infection induced by both natural infection and vaccination is of high significance for the prevention and control of coronavirus disease 2019 (COVID-19). Furthermore, understanding reinfections or infection after vaccination and the role immune escape plays in these scenarios will inform the need for updates of the current SARS-CoV-2 vaccines and help update guidelines suitable for the postpandemic world.