The Xenopus Phenotype Ontology: bridging model organism phenotype data to human health and development.

Background: Ontologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease. Results: Here we present the Xenopus Phenotype Ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated. Conclusions: The XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype-phenotype data that can be directly related to other uPheno compliant resources.

The Planarian Anatomy Ontology: A resource to connect data within and across experimental platforms

As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system-level across all life cycle stages, in intact animals, and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource.

Integration of anatomy ontology data with protein–protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

Background Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. Results According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. Conclusion Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.

The Pensoft Annotator: A new tool for text annotation with ontology terms

Introduction Digitisation of biodiversity knowledge from collections, scholarly literature and various research documents is an ongoing mission of the Biodiversity Information Standards (TDWG) community. Organisations such as the Biodiversity Heritage Library make historical biodiversity literature openly available and develop tools to allow biodiversity data reuse and interoperability. For instance, Plazi transforms free text into machine-readable formats and extracts collection data and feeds it into the Global Biodiversity Information Facility (GBIF) and other aggregators. All of these digitisation workflows require a lot of effort to develop and implement in practice. In essence, what these digitisation activities entail are the mapping of free text to concepts from recognised vocabularies or ontologies in order to make the content understandable to computers. Aim We aim to address the problem of mapping free text to ontological terms ("strings to things") with our tool for text-to-ontology mapping: the Pensoft Annotator. Methods & Implementation The Annotator is a web application that performs direct text matching to terms from any ontology or vocabulary list given as input to the Annotator. The term 'ontology' is used loosely here and means a collection of terms and their synonyms, where terms are uniquely identified via a Uniform Resource Identifier (URI). The Annotator accepts any of the following ontology formats (e.g. OBO, OWL, RDF/XML, etc.) but does not require the existence of a proper ontology structure (logical statements). We use the ROBOT command line tool to convert any of these formats to JSON. After the upload of a new ontology, the Annotator processes the ontology terms by normalising all exact synonyms and by removing all of the other synonyms (related, narrow and broad synonyms). This is done to limit the number of false positive matches and to preserve the semantic similarity between the matched ontology term and the text. After matching the words in the input text and the ontology term labels, the Pensoft Annotator returns a table of matched ontology terms including the following fields: the identifier of the ontology term, the ontology term label or the label of the synonym, the starting position of the matched term in the text, the term context (words surrounding the matched term in the text), the type of ontology term (class or property), the ontology from which the matched term originates and the number of times a given term is mentioned in the text. The Pensoft Annotator allows simultaneous annotation with multiple ontologies. To better visualise the exact ontology from which a matching term has been found, the terms are highlighted in different colour depending on the ontology. The Pensoft Annotator is also accessible programmatically via an Application Programming Interface (API), documented at https://annotator.pensoft.net/api. Discussion & Use Cases The Pensoft Annotator provides functionalities that will aid the transformation of free text to collections of semantic resources. However, it still requires expert knowledge to use as the ontologies need to be selected carefully. Some false positive matches from the annotation are possible because we do not perform semantic analysis of the texts. False negatives are also possible since there might be different word forms of ontology terms, which are not direct matches to them (e.g. 'wolf' and 'wolves'). For this reason, matched terms can be reviewed and removed from the results within the web interface of the Pensoft Annotator. After removal of terms, they will not be present in the downloaded results. The Pensoft Annotator can be used to annotate biodiversity and taxonomic literature to help with the extraction of biodiversity knowledge (e.g. species habitat preferences, species interaction data, localities, biogeographic data). The existence of some domain and taxon-specific ontologies, such as the Hymenoptera Anatomy Ontology, provides further opportunities for context-specific annotation. Semantic analysis of unstructured texts could be applied in addition to ontology annotation to improve the accuracy of ontology term matching and to filter out mismatched terms. Annotation of structured or semi-structured text (e.g. tables) can be done with better success. A recent example demonstrates the use of the Annotator to extract biotic interactions from tables (Dimitrova et al. 2020). The Annotator could also be used for ontology analysis and comparison. Annotation of text can help to discover gaps in ontologies as well as inaccurate synonyms. For instance, a certain word could be recognised as an ontology term match because it is an exact synonym in the ontology but in reality it might be more accurate to mark it as a related synonym. In addition, annotation with multiple ontologies can help to elucidate links between ontologies.

COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases

Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery 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, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA,TNF-∝, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4 and IL-2). These trends align with proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19.

The Planarian Anatomy Ontology: A resource to connect data within and across experimental platforms

As the planarian Schmidtea mediterranea (Smed) gains popularity as a research organism, the need for standard anatomical nomenclature is increasingly apparent. A controlled vocabulary streamlines data annotation, improves data organization, and enhances cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable framework of defined Smed anatomical terms organized using relationships. The most current version contains over 800 terms that describe Smed anatomy from subcellular to system-level across all life cycle stages, in intact animals, and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote ontology interoperability and comparative anatomy studies. To demonstrate the utility of PLANA for data curation, we created web-based resources for planarian embryogenesis, including a staging series and molecular fate mapping atlas, as well as a searchable Planarian Anatomy Gene Expression database, which integrates a variety of published gene expression data and allows retrieval of information of all published sequences associated with specific planarian anatomical regions. Finally, we report methods for continued curation of PLANA, providing a path for expansion and evolution of this community resource.Summary StatementWe report construction of an anatomy ontology for an emerging research organism and show its use to curate and mine data across multiple experimental platforms.

Modeling quantitative traits for COVID-19 case reports

Medical practitioners record the condition status of a patient through qualitative and quantitative observations. The measurement of vital signs and molecular parameters in the clinics gives a complementary description of abnormal phenotypes associated with the progression of a disease. The Clinical Measurement Ontology (CMO) is used to standardize annotations of these measurable traits. However, researchers have no way to describe how these quantitative traits relate to phenotype concepts in a machine-readable manner. Using the WHO clinical case report form standard for the COVID-19 pandemic, we modeled quantitative traits and developed OWL axioms to formally relate clinical measurement terms with anatomical, biomolecular entities and phenotypes annotated with the Uber-anatomy ontology (Uberon), Chemical Entities of Biological Interest (ChEBI) and the Phenotype and Trait Ontology (PATO) biomedical ontologies. The formal description of these relations allows interoperability between clinical and biological descriptions, and facilitates automated reasoning for analysis of patterns over quantitative and qualitative biomedical observations.

A comparative description of the mesosomal musculature in Sphecidae and Ampulicidae (Hymenoptera, Apoidea) using 3D techniques

Conflicting hypotheses about the relationships among the major lineages of aculeate Hymenoptera clearly show the necessity of detailed comparative morphological studies. Using micro-computed tomography and 3D reconstructions, the skeletal musculature of the meso- and metathorax and the first and second abdominal segment in Apoidea are described. Females of Sceliphron destillatorium, Sphex (Fernaldina) lucae (both Sphecidae), and Ampulex compressa (Ampulicidae) were examined. The morphological terminology provided by the Hymenoptera Anatomy Ontology is used. Up to 42 muscles were found. The three species differ in certain numerical and structural aspects. Ampulicidae differs significantly from Sphecidae in the metathorax and the anterior abdomen. The metapleural apodeme and paracoxal ridge are weakly developed in Ampulicidae, which affect some muscular structures. Furthermore, the muscles that insert on the coxae and trochanters are broader and longer in Ampulicidae. A conspicuous characteristic of Sphecidae is the absence of the metaphragma. Overall, we identified four hitherto unrecognized muscles. Our work suggests additional investigations on structures discussed in this paper.

Revisited morphology applied for Systematics of flat wasps (Hymenoptera, Bethylidae)

The world fauna of the flat wasps (Bethylidae) is represented by about 3,000 valid species. The skeletal morphology of bethylids is still not adequately understood and the terminology is generally not standardized between its internal taxa and with other Hymenoptera families. The same scenario exists in most of the families in this order. To address this problem, we describe the external skeletal morphology of Bethylidae. We review the terms used to describe skeletal features in the Hymenoptera in general and a consensus terminology is proposed for Bethylidae, which is linked to the online Hymenoptera Anatomy Ontology. The morphology of the studied specimens is illustrated with photos and line drawings. We also discuss the morphological variation at both subfamilial and generic ranks. Our analyses challenge hundreds of inappropriate, confused or imprecise terms traditionally used for Hymenoptera morphology. As a result, we have applied hundreds of updates of the terminology available online at the Hymenoptera Anatomy Ontology.

Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

BackgroundIdentification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet-lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. This is because PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes for anatomical entities. We developed an integrative framework to predict candidate genes for anatomical entities by combining existing experimental knowledge about gene-anatomy relationships with PPI networks using anatomy ontology annotations. We expected this integration to improve the quality of the PPI networks and be better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomy entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These ‘anatomy-based gene networks’ are semantic networks, as they are constructed based on the Uberon anatomy ontology annotations that are obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database, and we compared the performance of their network-based candidate gene predictions.ResultsAccording to candidate gene prediction performance evaluations tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks showed better receiver operating characteristic (ROC) and precision-recall curve performances than PPI networks for both zebrafish and mouse.ConclusionIntegration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improves the network quality, which makes them better optimized for predicting candidate genes for anatomical entities.