A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics.

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem.

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO's representation of the diseaseome and enhances the findability, accessibility, interoperability and reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO's semantic disease knowledge backbone, with resources that expose and connect the DO's semantic knowledge with disease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO's global impact and improvements to data quality and content, with emphasis on changes in the last two years.

Aligning Standards Communities for Omics Biodiversity Data: Sustainable Darwin Core-MIxS Interoperability.

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.

Metagenomic Methods for Addressing NASA's Planetary Protection Policy Requirements on Future Missions: A Workshop Report.

Molecular biology methods and technologies have advanced substantially over the past decade. These new molecular methods should be incorporated among the standard tools of planetary protection (PP) and could be validated for incorporation by 2026. To address the feasibility of applying modern molecular techniques to such an application, NASA conducted a technology workshop with private industry partners, academics, and government agency stakeholders, along with NASA staff and contractors. The technical discussions and presentations of the Multi-Mission Metagenomics Technology Development Workshop focused on modernizing and supplementing the current PP assays. The goals of the workshop were to assess the state of metagenomics and other advanced molecular techniques in the context of providing a validated framework to supplement the bacterial endospore-based NASA Standard Assay and to identify knowledge and technology gaps. In particular, workshop participants were tasked with discussing metagenomics as a stand-alone technology to provide rapid and comprehensive analysis of total nucleic acids and viable microorganisms on spacecraft surfaces, thereby allowing for the development of tailored and cost-effective microbial reduction plans for each hardware item on a spacecraft. Workshop participants recommended metagenomics approaches as the only data source that can adequately feed into quantitative microbial risk assessment models for evaluating the risk of forward (exploring extraterrestrial planet) and back (Earth harmful biological) contamination. Participants were unanimous that a metagenomics workflow, in tandem with rapid targeted quantitative (digital) PCR, represents a revolutionary advance over existing methods for the assessment of microbial bioburden on spacecraft surfaces. The workshop highlighted low biomass sampling, reagent contamination, and inconsistent bioinformatics data analysis as key areas for technology development. Finally, it was concluded that implementing metagenomics as an additional workflow for addressing concerns of NASA's robotic mission will represent a dramatic improvement in technology advancement for PP and will benefit future missions where mission success is affected by backward and forward contamination.

Assessing resource use: a case study with the Human Disease Ontology.

As a genomic resource provider, grappling with getting a handle on how your resource is utilized can be extremely challenging. At the same time, being able to thus document the plethora of use cases is vital to demonstrate sustainability. Herein, we describe a flexible workflow, built on readily available software, that the Human Disease Ontology (DO) project has utilized to transition to semi-automated methods to identify uses of the ontology in the published literature. The novel R package DO.utils (https://github.com/DiseaseOntology/DO.utils) has been devised with a small set of key functions to support our usage workflow in combination with Google Sheets. Use of this workflow has resulted in a 3-fold increase in the number of identified publications that use the DO and has provided novel usage insights that offer new research directions and reveal a clearer picture of the DO's use and scientific impact. The DO's resource use assessment workflow and the supporting software are designed to be useful to other resources, including databases, software tools, method providers and other web resources, to achieve similar results. Database URL: https://github.com/DiseaseOntology/DO.utils.

Modeling the enigma of complex disease etiology.

BACKGROUND: Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors. METHODS: Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model. RESULTS: We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves. CONCLUSIONS: The Human Disease Ontology's Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.

BACKGROUND: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. RESULTS: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CONCLUSION: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

A history of the MetaSUB consortium: Tracking urban microbes around the globe.

The MetaSUB Consortium, founded in 2015, is a global consortium with an interdisciplinary team of clinicians, scientists, bioinformaticians, engineers, and designers, with members from more than 100 countries across the globe. This network has continually collected samples from urban and rural sites including subways and transit systems, sewage systems, hospitals, and other environmental sampling. These collections have been ongoing since 2015 and have continued when possible, even throughout the COVID-19 pandemic. The consortium has optimized their workflow for the collection, isolation, and sequencing of DNA and RNA collected from these various sites and processing them for metagenomics analysis, including the identification of SARS-CoV-2 and its variants. Here, the Consortium describes its foundations, and its ongoing work to expand on this network and to focus its scope on the mapping, annotation, and prediction of emerging pathogens, mapping microbial evolution and antibiotic resistance, and the discovery of novel organisms and biosynthetic gene clusters.

A decade of GigaScience: 10 years of the evolving genomic and biomedical standards landscape.

Standardization of omics data drives FAIR data practices through community-built genomic data standards and biomedical ontologies. Use of standards has progressed from a foreign concept to a sought-after solution, moving from efforts to coordinate data within individual research projects to research communities coming together to identify solutions to common challenges. Today we are seeing the benefits of this multidecade groundswell to coordinate, exchange, and reuse data; to compare data across studies; and to integrate data across previously siloed resources.

Darwinian genomics and diversity in the tree of life.

Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Fewer than 18,000 of ∼2,000,000 eukaryotic species (<1%) have a representative genome sequence in GenBank, and only a fraction of these have ancillary information on genome structure, genetic variation, gene expression, epigenetic modifications, and population diversity. This imbalance reflects a perception that human studies are paramount in disease research. Yet understanding how genomes work, and how genetic variation shapes phenotypes, requires a broad view that embraces the vast diversity of life. We have the technology to collect massive and exquisitely detailed datasets about the world, but expertise is siloed into distinct fields. A new approach, integrating comparative genomics with cell and evolutionary biology, ecology, archaeology, anthropology, and conservation biology, is essential for understanding and protecting ourselves and our world. Here, we describe potential for scientific discovery when comparative genomics works in close collaboration with a broad range of fields as well as the technical, scientific, and social constraints that must be addressed.

MIxS-SA: a MIxS extension defining the minimum information standard for sequence data from symbiont-associated micro-organisms.

The symbiont-associated (SA) environmental package is a new extension to the minimum information about any (x) sequence (MIxS) standards, established by the Parasite Microbiome Project (PMP) consortium, in collaboration with the Genomics Standard Consortium. The SA was built upon the host-associated MIxS standard, but reflects the nestedness of symbiont-associated microbiota within and across host-symbiont-microbe interactions. This package is designed to facilitate the collection and reporting of a broad range of metadata information that apply to symbionts such as life history traits, association with one or multiple host organisms, or the nature of host-symbiont interactions along the mutualism-parasitism continuum. To better reflect the inherent nestedness of all biological systems, we present a novel feature that allows users to co-localize samples, to nest a package within another package, and to identify replicates. Adoption of the MIxS-SA and of the new terms will facilitate reports of complex sampling design from a myriad of environments.

The founding charter of the Omic Biodiversity Observation Network (Omic BON).

Omic BON is a thematic Biodiversity Observation Network under the Group on Earth Observations Biodiversity Observation Network (GEO BON), focused on coordinating the observation of biomolecules in organisms and the environment. Our founding partners include representatives from national, regional, and global observing systems; standards organizations; and data and sample management infrastructures. By coordinating observing strategies, methods, and data flows, Omic BON will facilitate the co-creation of a global omics meta-observatory to generate actionable knowledge. Here, we present key elements of Omic BON's founding charter and first activities.

Diseasomics: Actionable machine interpretable disease knowledge at the point-of-care.

Physicians establish diagnosis by assessing a patient's signs, symptoms, age, sex, laboratory test findings and the disease history. All this must be done in limited time and against the backdrop of an increasing overall workload. In the era of evidence-based medicine it is utmost important for a clinician to be abreast of the latest guidelines and treatment protocols which are changing rapidly. In resource limited settings, the updated knowledge often does not reach the point-of-care. This paper presents an artificial intelligence (AI)-based approach for integrating comprehensive disease knowledge, to support physicians and healthcare workers in arriving at accurate diagnoses at the point-of-care. We integrated different disease-related knowledge bodies to construct a comprehensive, machine interpretable diseasomics knowledge-graph that includes the Disease Ontology, disease symptoms, SNOMED CT, DisGeNET, and PharmGKB data. The resulting disease-symptom network comprises knowledge from the Symptom Ontology, electronic health records (EHR), human symptom disease network, Disease Ontology, Wikipedia, PubMed, textbooks, and symptomology knowledge sources with 84.56% accuracy. We also integrated spatial and temporal comorbidity knowledge obtained from EHR for two population data sets from Spain and Sweden respectively. The knowledge graph is stored in a graph database as a digital twin of the disease knowledge. We use node2vec (node embedding) as digital triplet for link prediction in disease-symptom networks to identify missing associations. This diseasomics knowledge graph is expected to democratize the medical knowledge and empower non-specialist health workers to make evidence based informed decisions and help achieve the goal of universal health coverage (UHC). The machine interpretable knowledge graphs presented in this paper are associations between various entities and do not imply causation. Our differential diagnostic tool focusses on signs and symptoms and does not include a complete assessment of patient's lifestyle and health history which would typically be necessary to rule out conditions and to arrive at a final diagnosis. The predicted diseases are ordered according to the specific disease burden in South Asia. The knowledge graphs and the tools presented here can be used as a guide.

Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association.

In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The co-occurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.

The Human Disease Ontology 2022 update.

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO's 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO's complex disease classification. The DO's updated website provides multifaceted etiology searching, enhanced documentation and educational resources.

OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies.

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology's compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/.

COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model.

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.