MIREOT: the Minimum Information to Reference an External Ontology Term

While the Web Ontology Language (OWL) provides a mechanism to import ontologies, this mechanism is not always suitable. First, given the current state of editing tools and the issues they have working with large ontologies, direct OWL imports have sometimes proven impractical for day-to-day development. Second, ontologies chosen for integration may be under active development and not aligned with the chosen design principles. Importing heterogeneous ontologies in their entirety may lead to inconsistencies or unintended inferences. In this paper we propose a set of guidelines for importing required terms from an external resource into a target ontology. We describe the guidelines, their implementation, present some examples of application, and outline future work and extensions

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.
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Overcoming the Ontology Enrichment Bottleneck with Quick Term Templates

The developers of the Ontology of Biomedical Investigations (OBI) primarily use Protégé for editing. However, adding many classes with similar patterns of logical definition is time consuming, error prone, and requires the editor to have some expertise in OWL. Therefore, the process is poorly suited for a large number of domain experts who have limited experience Protégé and ontology development. We have developed a procedure to ease this task and allow such domain experts to add terms to the ontology in a way that both effectively includes complex logical definitions yet requires minimal manual intervention by OBI developers. The procedure is based on editing a Quick Term Template in a spreadsheet format which is subsequently converted into an OWL file. This procedure promises to be a robust and scalable approach for ontology enrichment

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented

Glioblastoma cell fate is differentially regulated by the microenvironments of the tumour bulk and infiltrative margin

Glioblastoma recurrence originates from invasive cells at the tumour margin that escape surgical debulking, but their biology remains poorly understood. Here we generated three somatic mouse models recapitulating the main glioblastoma driver mutations to characterise margin cells. We find that, regardless of genetics, tumours converge on a common set of neural- like cellular states. However, bulk and margin display distinct neurogenic patterns and immune microenvironments. The margin is immune-cold and preferentially follows developmental-like trajectories to produce astrocyte-like cells. In contrast, injury-like programmes dominate in the bulk, are associated with immune infiltration and generate lowly-proliferative injured neural progenitor-like (iNPCs) cells. In vivo label-retention approaches further demonstrate that iNPCs account for a significant proportion of dormant glioblastoma cells and are induced by interferon signalling within T-cell niches. These findings indicate that tumour region is a major determinant of glioblastoma cell fate and therapeutic vulnerabilities identified in bulk may not extend to the margin residuum

Author Guide for Addressing Animal Methods Bias in Publishing

There is growing recognition that animal methods bias, a preference for animal‐based methods where they are not necessary or where nonanimal‐based methods may already be suitable, can impact the likelihood or timeliness of a manuscript being accepted for publication. Following April 2022 workshop about animal methods bias in scientific publishing, a coalition of scientists and advocates formed a Coalition to Illuminate and Address Animal Methods Bias (COLAAB). The COLAAB has developed this guide to be used by authors who use nonanimal methods to avoid and respond to animal methods bias from manuscript reviewers. It contains information that researchers may use during 1) study design, including how to find and select appropriate nonanimal methods and preregister a research plan, 2) manuscript preparation and submission, including tips for discussing methods and choosing journals and reviewers that may be more receptive to nonanimal methods, and 3) the peer review process, providing suggested language and literature to aid authors in responding to biased reviews. The author's guide for addressing animal methods bias in publishing is a living resource also available online at animalmethodsbias.org, which aims to help ensure fair dissemination of research that uses nonanimal methods and prevent unnecessary experiments on animals

Glioblastoma cell fate is differentially regulated by the microenvironments of the tumor bulk and infiltrative margin

Glioblastoma (GBM) recurrence originates from invasive margin cells that escape surgical debulking, but to what extent these cells resemble their bulk counterparts remains unclear. Here, we generated three immunocompetent somatic GBM mouse models, driven by subtype-associated mutations, to compare matched bulk and margin cells. We find that, regardless of mutations, tumors converge on common sets of neural-like cellular states. However, bulk and margin have distinct biology. Injury-like programs associated with immune infiltration dominate in the bulk, leading to the generation of lowly proliferative injured neural progenitor-like cells (iNPCs). iNPCs account for a significant proportion of dormant GBM cells and are induced by interferon signaling within T cell niches. In contrast, developmental-like trajectories are favored within the immune-cold margin microenvironment resulting in differentiation toward invasive astrocyte-like cells. These findings suggest that the regional tumor microenvironment dominantly controls GBM cell fate and biological vulnerabilities identified in the bulk may not extend to the margin residuum

VO: Vaccine Ontology

The collaborative, community-based Vaccine Ontology (VO) was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Currently VO covers a variety of aspects of the vaccine domain, with an emphasis on classification of vaccines and vaccine components, and on host immune response to vaccines. VO can be used for a number of applications, e.g., ontology-based vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI)

Logical Development of the Cell Ontology

<p>Abstract</p> <p>Background</p> <p>The Cell Ontology (CL) is an ontology for the representation of <it>in vivo </it>cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.</p> <p>Results</p> <p>Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.</p> <p>Conclusion</p> <p>Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.</p

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 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 FAIR. Competing Interest StatementThe authors have declared no competing interest