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Traditional martial arts are treasures of humanity's knowledge and critical carriers of sociocultural memories throughout history. However, such treasured practices have encountered various challenges in knowledge transmission and now feature many entries on the UNESCO list of intangible cultural heritage. In tackling the urgency of knowledge preservation through digital means, this project employs an ontology-based approach to model the conceptual realm of traditional martial arts. Accordingly, it creates the Martial Art Ontology (MAon) , a comprehensive domain ontology with an annotated data resource incorporating entities and relations from embodied, epistemic, and sociocultural facets. MAon underlines the significance of embodied qualities and addresses relevant dimensions, such as kinesthetics, techniques, mnemonics, and tactics, along with stylistic, interpretative, and ideological components. It features scholarly terminology developed through literature analysis, interviews with masters, and expert validations. The instantiation of MAon is realized through annotating three archetypal Southern Chinese styles, offering exhaustive descriptions concerning techniques, forms, principles, and form sets, amongst others. In summary, the reported approach encodes the manifold of martial arts into a structured vocabulary and an interlinked data resource, accessible to both human-reading and machine-operating applications. By applying it to manifest a range of knowledge concepts, we demonstrate the potential of ontology-based datafication to create coherent representations for intangible cultural entities and to enable an interoperable data infrastructure across multimodal cultural archives. [ABSTRACT FROM AUTHOR]

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO , http://pcaontology.net/ and http://pcaontology.net/mobile/. [ABSTRACT FROM AUTHOR]

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans , Drosophila , zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML). [ABSTRACT FROM AUTHOR]

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD. [ABSTRACT FROM AUTHOR]

This paper argues that the philosophy of ambiguity, associated with Maurice Merleau-Ponty and Simone de Beauvoir, provides a helpful framework for understanding the complex and paradoxical nature of old age outside the dominant categories of the "third" and "fourth" ages. Building on philosophical literature in cultural gerontology including that focused on the "art of living" and other approaches that draw on existentialist thought, it suggests "ambiguity" as a theme that has been overlooked in such literature. The advantage of this approach is that it can accommodate contradictory elements and thus underpin an integrated approach to old age. At the same time, in distinguishing between ontological and social dimensions of ambiguity, the conceptual framework offers a new perspective on ageism that can help clarify the distinction between ageism as oppression and the negative aspects of deep old age itself. The paper is structured as follows. After making the case for the value and importance of "ambiguity" as a framework for viewing old age, I set out the key aspects of this philosophy as found in the work of Merleau-Ponty and particularly Beauvoir. I then apply this framework to a discussion of 2 literary memoirs of deep old age, by Diana Athill and Florida Scott-Maxwell, respectively. I argue that the picture of old age that emerges contrasts with the hegemonic discourses enframed by successful aging and frailed old age and offers the possibility not only of a true appreciation of old age but also of a more meaningful life course itself. [ABSTRACT FROM AUTHOR]

Temporal information plays a crucial role in historical research, as it enables scholars to gain insights into the events and processes that have shaped the past. However, the complexity and diversity of temporal descriptions found in Chinese historical texts pose significant challenges for analyzing and interpreting this information. This article addresses these challenges by introducing the traditional Chinese time ontology (TCT Ontology), which integrates relevant concepts and different timing methods into an ontology. The TCT Ontology comprises four classes, including the TCT Record class, Chinese Calendar class, Historical Interval class, and Person class, to represent time descriptions in Chinese texts. By separating time records and the traditional Chinese calendar, the ontology provides a reference model for understanding time information in Chinese historical archives and serves as a basis for converting those time records to the Gregorian calendar. This accurate conversion is critical for humanistic research in Chinese history, as it enables scholars to engage in meaningful reading, studying, and research of the historical record. [ABSTRACT FROM AUTHOR]

Community social work (CSW) is often regarded as a practice with a variety of intervention models. The objectives of the present article are to examine CSW's philosophical and theoretical roots and to bridge the gap in the literature regarding the theoretical and philosophical origins of CSW and to conceptualize these theoretical bases as a paradigm with ontological, epistemological, axiological, and methodological aspects. The ontology of the proposed CSW paradigm relies on ecological theory, critical theory, and community psychology. The paradigm's epistemology relies on the basic assumption that community members are autonomous subjective human beings with important and valid knowledge who make decisions concerning their lives. Axiologically, the most prominent value of the paradigm is participation, from which other central values derive. The paradigm's methodology derives from its three abovementioned components. The conceptualization of a CSW paradigm provides a theoretical foundation for community interventions and refining the goals of these interventions. The paradigm can be used as a pedagogical and identity-building tool with students and social workers who focus on CSW. [ABSTRACT FROM AUTHOR]

Ontology plays a vital role in linking and publishing heritage data. However, the main difficulties of building ontology faced by heritage institutions are how the cultural heritage information is integrated into a fine-grained ontology. Therefore, following the ISO principles of Terminology (ISO 1087 and 704), this article proposed a term-concept-characteristic methodology that is user-friendly for cultural heritage experts to build a fine-grained ontology. This article aims to provide a method to integrate the fine-grained knowledge of cultural heritage into an ontology, especially when it is necessary to build a fine-grained ontology for particular purposes, such as knowledge-based terminological resources. [ABSTRACT FROM AUTHOR]

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes. [ABSTRACT FROM AUTHOR]

Rare diseases individually affect relatively few people, but as a group they impact considerable numbers of people. The Rat Genome Database (https://rgd.mcw.edu) is a knowledgebase that offers resources for rare disease research. This includes disease definitions, genes, quantitative trail loci (QTLs), genetic variants, annotations to published literature, links to external resources, and more. One important resource is identifying relevant cell lines and rat strains that serve as models for disease research. Diseases, genes, and strains have report pages with consolidated data, and links to analysis tools. Utilizing these globally accessible resources for rare disease research, potentiating discovery of mechanisms and new treatments, can point researchers toward solutions to alleviate the suffering of those afflicted with these diseases. [ABSTRACT FROM AUTHOR]

Many ΙΤ applications rely on the operationalisation of terminologies, such as multilingual semantic search engines. By operationalisation of terminology, we mean a computational representation of the conceptual system as an ontology of knowledge engineering. This conceptual approach of terminology is close to the ISO principles on terminology as defined by the core standards ISO 1087:2019 and ISO 704:2022. The question then is to study what contribution these standards can make to the construction of ontologies for terminological purposes. In other words, are the terminology principles of ISO soluble in the ontology of knowledge engineering? In this article, we will attempt to provide some answers on the feasibility of implementing ISO principles in description logic, the dominant model in knowledge representation. We will illustrate our proposition with Protégé, the most popular environment for building ontologies. Facing the issues raised by this approach, the article will end with the ontoterminology building environment Tedi directly linked to the ISO principles. [ABSTRACT FROM AUTHOR]

Danio rerio is a model organism used to investigate vertebrate development. Manipulation of the zebrafish genome and resultant gene products by mutation or targeted knockdown has made the zebrafish a good system for investigating gene function, providing a resource to investigate genetic contributors to phenotype and human disease. Phenotypic outcomes can be the result of gene mutation, targeted knockdown of gene products, manipulation of experimental conditions, or any combination thereof. Zebrafish have been used in various genetic and chemical screens to identify genetic and environmental contributors to phenotype and disease outcomes. The Zebrafish Information Network (ZFIN, zfin.org) is the central repository for genetic, genomic, and phenotypic data that result from research using D. rerio. Here we describe how ZFIN annotates phenotype, expression, and disease model data across various experimental designs, how we computationally determine wild-type gene expression, the phenotypic gene, and how these results allow us to propagate gene expression, phenotype, and disease model data to the correct gene, or gene related entity. [ABSTRACT FROM AUTHOR]

Xenbase (https://www.xenbase.org/), the Xenopus model organism knowledgebase, is a web-accessible resource that integrates the diverse genomic and biological data from research on the laboratory frogs Xenopus laevis and Xenopus tropicalis. The goal of Xenbase is to accelerate discovery and empower Xenopus research, to enhance the impact of Xenopus research data, and to facilitate the dissemination of these data. Xenbase also enhances the value of Xenopus data through high-quality curation, data integration, providing bioinformatics tools optimized for Xenopus experiments, and linking Xenopus data to human data, and other model organisms. Xenbase also plays an indispensable role in making Xenopus data interoperable and accessible to the broader biomedical community in accordance with FAIR principles. Xenbase provides annotated data updates to organizations such as NCBI, UniProtKB, Ensembl, the Gene Ontology consortium, and most recently, the Alliance of Genomic Resources, a common clearing house for data from humans and model organisms. This article provides a brief overview of key and recently added features of Xenbase. New features include processing of Xenopus high-throughput sequencing data from the NCBI Gene Expression Omnibus; curation of anatomical, physiological, and expression phenotypes with the newly created Xenopus Phenotype Ontology; Xenopus Gene Ontology annotations; new anatomical drawings of the Normal Table of Xenopus development; and integration of the latest Xenopus laevis v10.1 genome annotations. Finally, we highlight areas for future development at Xenbase as we continue to support the Xenopus research community. [ABSTRACT FROM AUTHOR]

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO--a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations--evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)--mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project. [ABSTRACT FROM AUTHOR]

Motivation: Relation extraction (RE) is a crucial process to deal with the amount of text published daily, e.g. to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders, namely, BERT. However, state-of-the-art performance may be limited by the lack of efficient external knowledge injection approaches, with a larger impact in the biomedical area given the widespread usage and high quality of biomedical ontologies. This knowledge can propel these systems forward by aiding them in predicting more explainable biomedical associations. With this in mind, we developed K-RET, a novel, knowledgeable biomedical RE system that, for the first time, injects knowledge by handling different types of associations, multiple sources and where to apply it, and multi-token entities. Results: We tested K-RET on three independent and open-access corpora (DDI, BC5CDR, and PGR) using four biomedical ontologies handling different entities. K-RET improved state-of-the-art results by 2.68% on average, with the DDI Corpus yielding the most significant boost in performance, from 79.30% to 87.19% in F-measure, representing a P-value of 2.91 × 10 − 12 . Availability and implementation: https://github.com/lasigeBioTM/K-RET. [ABSTRACT FROM AUTHOR]

Adaptive genetic diversity in crop wild relatives (CWRs) can be exploited to develop improved crops with higher yield and resilience if phylogenetic relationships between crops and their CWRs are resolved. This further allows accurate quantification of genome-wide introgression and determination of regions of the genome under selection. Using broad sampling of CWRs and whole genome sequencing, we further demonstrate the relationships among two economically valuable and morphologically diverse Brassica crop species, their CWRs, and their putative wild progenitors. Complex genetic relationships and extensive genomic introgression between CWRs and Brassica crops were revealed. Some wild Brassica oleracea populations have admixed feral origins; some domesticated taxa in both crop species are of hybrid origin, while wild Brassica rapa is genetically indistinct from turnips. The extensive genomic introgression that we reveal could result in false identification of selection signatures during domestication using traditional comparative approaches used previously; therefore, we adopted a single-population approach to study selection during domestication. We used this to explore examples of parallel phenotypic selection in the two crop groups and highlight promising candidate genes for future investigation. Our analysis defines the complex genetic relationships between Brassica crops and their diverse CWRs, revealing extensive cross-species gene flow with implications for both crop domestication and evolutionary diversification more generally. [ABSTRACT FROM AUTHOR]

Motivation The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. Results Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. Availability and implementation PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A precise description of traits is essential in genetics and genomics studies to facilitate comparative genetics and meta-analyses. It is an ongoing challenge in research and production environments to unambiguously and consistently compare traits of interest from data collected under various conditions. Despite previous efforts to standardize trait nomenclature, it remains a challenge to fully and accurately capture trait nomenclature granularity in a way that ensures long-term data sustainability in terms of the data curation processes, data management logistics and the ability to make meaningful comparisons across studies. In the Animal Quantitative Trait Loci Database and the Animal Trait Correlation Database, we have recently introduced a new method to extend livestock trait ontologies by using trait modifiers and qualifiers to define traits that differ slightly in how they are measured, examined or combined with other traits or factors. Here, we describe the implementation of a system in which the extended trait data, with modifiers, are managed at the experiment level as 'trait variants'. This has helped us to streamline the management and curation of such trait information in our database environment. Database URLhttps://www.animalgenome.org/PGNET/ [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article presents a review of the ontologies for the word "pain" based on their use in clinical applications or likelihood of relevancy to pain research and treatment, which were selected from 52 ontologies searched in the BioPortal of the National Center for Biotechnology Information. It mentions that no data acquisition and organization reflecting or integrating the complex biopsychosocial and functional components of pain conditions exists.

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N -glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N -glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan–protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N -glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/). [ABSTRACT FROM AUTHOR]

Motivation We have previously designed and implemented a tree-based ontology to represent glycan structures with the aim of searching these structures with a glyco-driven syntax. This resulted in creating the GlySTreeM knowledge-base as a linchpin of the structural matching procedure and we now introduce a query language, called GlycoQL, for the actual implementation of a glycan structure search. Results The methodology is described and illustrated with a use-case focused on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike protein glycosylation. We show how to enhance site annotation with federated queries involving UniProt and GlyConnect, our glycoprotein database. Availability and implementation https://glyconnect.expasy.org/glycoql/. [ABSTRACT FROM AUTHOR]

Hypermedia Dante Network (HDN) is a 3-year Italian National Research Project, started in 2020, which aims to enrich the functionalities of the DanteSources Digital Library to efficiently represent knowledge about the primary sources of Dante's Comedy. DanteSources allows users to retrieve and visualize the list and the distribution of Dante's primary sources that have been identified by recent commentaries of five of Dante's minor works (i.e. Vita nova , De vulgari eloquentia , Convivio , De Monarchia , and Rime). The digital library is based on a formal ontology expressed in Resource Description Framework Schema (RDFS) language. Based on the DanteSources experience, the HDN project aims to formally represent the primary sources of the Divine Comedy whose identification is based on several commentaries included in the Dartmouth Dante Project corpus. To reach this goal, we restructured and extended the DanteSources ontology to provide a wider and more complete representation of the knowledge concerning the primary sources of the Comedy. In this article, we present the result of this effort, i.e. the HDN ontology. The ontology is expressed in OWL and has as reference ontologies the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation LRMoo. We also briefly describe the semi-automatic tool that will be used by the scholars to populate the ontology. [ABSTRACT FROM AUTHOR]

Measuring the semantic similarity between Gene Ontology (GO) terms is a fundamental step in numerous functional bioinformatics applications. To fully exploit the metadata of GO terms, word embedding-based methods have been proposed recently to map GO terms to low-dimensional feature vectors. However, these representation methods commonly overlook the key information hidden in the whole GO structure and the relationship between GO terms. In this paper, we propose a novel representation model for GO terms, named GT2Vec, which jointly considers the GO graph structure obtained by graph contrastive learning and the semantic description of GO terms based on BERT encoders. Our method is evaluated on a protein similarity task on a collection of benchmark datasets. The experimental results demonstrate the effectiveness of using a joint encoding graph structure and textual node descriptors to learn vector representations for GO terms. [ABSTRACT FROM AUTHOR]

A representation method is an algorithm that calculates numerical feature vectors for samples in a dataset. Such vectors, also known as embeddings, define a relatively low-dimensional space able to efficiently encode high-dimensional data. Very recently, many types of learned data representations based on machine learning have appeared and are being applied to several tasks in bioinformatics. In particular, protein representation learning methods integrate different types of protein information (sequence, domains, etc.), in supervised or unsupervised learning approaches, and provide embeddings of protein sequences that can be used for downstream tasks. One task that is of special interest is the automatic function prediction of the huge number of novel proteins that are being discovered nowadays and are still totally uncharacterized. However, despite its importance, up to date there is not a fair benchmark study of the predictive performance of existing proposals on the same large set of proteins and for very concrete and common bioinformatics tasks. Therefore, this lack of benchmark studies prevent the community from using adequate predictive methods for accelerating the functional characterization of proteins. In this study, we performed a detailed comparison of protein sequence representation learning methods, explaining each approach and comparing them with an experimental benchmark on several bioinformatics tasks: (i) determining protein sequence similarity in the embedding space; (ii) inferring protein domains and (iii) predicting ontology-based protein functions. We examine the advantages and disadvantages of each representation approach over the benchmark results. We hope the results and the discussion of this study can help the community to select the most adequate machine learning-based technique for protein representation according to the bioinformatics task at hand. [ABSTRACT FROM AUTHOR]

Motivation Protein–protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms. Results We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions. Availability and implementation https://github.com/Ieremie/TransformerGO. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Mangroves are of great ecological and economical importance, providing shelters for a wide range of species and nursery habitats for commercially important marine species. Ceriops zippeliana (yellow mangrove) belongs to Rhizophoraceae family and is commonly distributed in the tropical and subtropical coastal communities. In this study, we present a high-quality assembly of the C. zippeliana genome. We constructed an initial draft assembly of 240,139,412 bases with an N50 contig length of 564,761 bases using the 10x Genomics linked-read technology. This assembly was further scaffolded with RagTag using a chromosome-scale assembly of a closely related Ceriops species as a reference. The final assembly contained 243,228,612 bases with an N50 scaffold length of 10,559,178Mb. The size of the final assembly was close to those estimated using DNA flow cytometry (248Mb) and the k-mer distribution analysis (246Mb). We predicted a total of 23,474 gene models and 21,724 protein-coding genes in the C. zippeliana genome, of which 16,002 were assigned gene ontology terms. We recovered 97.1% of the highly conserved orthologs based on the Benchmarking Universal Single-Copy Orthologs analysis. The phylogenetic analysis based on single-copy orthologous genes illustrated that C. zippeliana and Ceriops tagal diverged approximately 10.2 million years ago (MYA), and their last common ancestor and Kandelia obovata diverged approximately 29.9 MYA. The high-quality assembly of C. zippeliana presented in this work provides a useful genomic resource for studying mangroves' unique adaptations to stressful intertidal habitats and for developing sustainable mangrove forest restoration and conservation programs. [ABSTRACT FROM AUTHOR]

In this article we present the first achievement of the Index Medii Aevi Geographiae Operum (IMAGO)—Italian National Research Project (2020–23), that is, the ontology we have created in order to formally represent the knowledge about the geographical works written in Middle Ages and Renaissance (6th–15th centuries). The IMAGO ontology is derived from a strict collaboration between the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council (CNR) and the scholars who are involved in the project, who have supported ISTI-CNR in defining a conceptualization of the domain of knowledge. Following the re-use logic, we have selected as reference ontologies the International Committee on Documentation CRM vocabulary and its extension FRBRoo, including its in-progress reformulation, LRMoo. This research is included in a wider project context whose final aim is the creation of a knowledge base (KB) of Latin geographic literature of the Middle Ages and Renaissance Humanism in which the data are formally represented following the Linked Open Data paradigm and using the Semantic Web languages. At the end of the project, this KB will be accessed through a Web application that allows retrieving and consulting the collected data in a user-friendly way for scholars and general users, e.g. tables, maps, CSV files. [ABSTRACT FROM AUTHOR]

The Zebrafish Information Network (zfin.org) is the central repository for Danio rerio genetic and genomic data. The Zebrafish Information Network has served the zebrafish research community since 1994, expertly curating, integrating, and displaying zebrafish data. Key data types available at the Zebrafish Information Network include, but are not limited to, genes, alleles, human disease models, gene expression, phenotype, and gene function. The Zebrafish Information Network makes zebrafish research data Findable, Accessible, Interoperable, and Reusable through nomenclature, curatorial and annotation activities, web interfaces, and data downloads. Recently, the Zebrafish Information Network and 6 other model organism knowledgebases have collaborated to form the Alliance of Genome Resources, aiming to develop sustainable genome information resources that enable the use of model organisms to understand the genetic and genomic basis of human biology and disease. Here, we provide an overview of the data available at the Zebrafish Information Network including recent updates to the gene page to provide access to single-cell RNA sequencing data, links to Alliance web pages, ribbon diagrams to summarize the biological systems and Gene Ontology terms that have annotations, and data integration with the Alliance of Genome Resources. [ABSTRACT FROM AUTHOR]

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html. [ABSTRACT FROM AUTHOR]

Nowadays, mobile applications have been widely used in various health and social domains. While there are large number of smart devices that connect and transmit their context data in free manner, these mobile applications have limitations regarding the increasing number of heterogeneous context data sent by devices, conducting the problem of identifying user-centric situations and providing services in real-time. Many smart mobile health systems were proposed to ensure context-aware personal health monitoring and diseases detection, while most of them are failed to ensure a right level of dynamicity and enough flexibility for assisting users everywhere, anytime, through widespread sensors and mobile devices. In fact, it becomes necessary to rethink a new way to minimize the response time combining competitive agents with semantic-based situation reasoning strategy. In this paper, we introduce a novel agent-based platform with three-layered ontology for the semantic description and parallel management of services selection approach dedicated to context-aware health mobile applications. In addition, we propose an innovative parallel services discovery and optimal selection process, which involved a set of filtered and classified semantic health multipath according to user's context and preferences with consistent mobility of users and limited resources (low battery, processing capabilities, memory and others). Experimental results show the effectiveness of the proposed approach as it includes the semantic service information on cooperative agents. In addition, the proposed approach ensures fast response time and prolongs the continuity of the mobile application. [ABSTRACT FROM AUTHOR]

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL : https://github.com/INCATools/ontology-development-kit [ABSTRACT FROM AUTHOR]

There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has led to the wide-spread use of evidence-based filtering of genome-wide sequence data as a diagnostic tool in DD. Determining whether the association of a filtered variant at a specific locus is a plausible explanation of the phenotype in the proband is crucial and commonly requires extensive manual literature review by both clinical scientists and clinicians. Access to a database of weighted clinical features extracted from rigorously curated literature would increase the efficiency of this process and facilitate the development of robust phenotypic similarity metrics. However, given the large and rapidly increasing volume of published information, conventional biocuration approaches are becoming impractical. Here, we present a scalable, automated method for the extraction of categorical phenotypic descriptors from the full-text literature. Papers identified through literature review were downloaded and parsed using the Cadmus custom retrieval package. Human Phenotype Ontology terms were extracted using MetaMap, with 76–84% precision and 65–73% recall. Mean terms per paper increased from 9 in title + abstract, to 68 using full text. We demonstrate that these literature-derived disease models plausibly reflect true disease expressivity more accurately than widely used manually curated models, through comparison with prospectively gathered data from the Deciphering Developmental Disorders study. The area under the curve for receiver operating characteristic (ROC) curves increased by 5–10% through the use of literature-derived models. This work shows that scalable automated literature curation increases performance and adds weight to the need for this strategy to be integrated into informatic variant analysis pipelines. Database URL : https://doi.org/10.1093/database/baac038 [ABSTRACT FROM AUTHOR]

The Sickle Cell Disease (SCD) Ontology (SCDO, https://scdontology.h3abionet.org/) provides a comprehensive knowledge base of SCD management, systems and standardized human and machine-readable resources that unambiguously describe terminology and concepts about SCD for researchers, patients and clinicians. The SCDO was launched in 2016 and is continuously updated in quantity, as well as in quality, to effectively support the curation of SCD research, patient databasing and clinical informatics applications. SCD knowledge from the scientific literature is used to update existing SCDO terms and create new terms where necessary. Here, we report major updates to the SCDO, from December 2019 until April 2021, for promoting interoperability and facilitating SCD data harmonization, sharing and integration across different studies and for retrospective multi-site research collaborations. SCDO developers continue to collaborate with the SCD community, clinicians and researchers to improve specific ontology areas and expand standardized descriptions to conditions influencing SCD phenotypic expressions and clinical manifestations of the sickling process, e.g. thalassemias. Database URL : https://scdontology.h3abionet.org/ [ABSTRACT FROM AUTHOR]

Experimental protein function annotation does not scale with the fast-growing sequence databases. Only a tiny fraction (<0.1%) of protein sequences has experimentally determined functional annotations. Computational methods may predict protein function very quickly, but their accuracy is not very satisfactory. Based upon recent breakthroughs in protein structure prediction and protein language models, we develop GAT-GO, a graph attention network (GAT) method that may substantially improve protein function prediction by leveraging predicted structure information and protein sequence embedding. Our experimental results show that GAT-GO greatly outperforms the latest sequence- and structure-based deep learning methods. On the PDB-mmseqs testset where the train and test proteins share <15% sequence identity, our GAT-GO yields Fmax (maximum F -score) 0.508, 0.416, 0.501, and area under the precision-recall curve (AUPRC) 0.427, 0.253, 0.411 for the MFO, BPO, CCO ontology domains, respectively, much better than the homology-based method BLAST (Fmax 0.117, 0.121, 0.207 and AUPRC 0.120, 0.120, 0.163) that does not use any structure information. On the PDB-cdhit testset where the training and test proteins are more similar, although using predicted structure information, our GAT-GO obtains Fmax 0.637, 0.501, 0.542 for the MFO, BPO, CCO ontology domains, respectively, and AUPRC 0.662, 0.384, 0.481, significantly exceeding the just-published method DeepFRI that uses experimental structures, which has Fmax 0.542, 0.425, 0.424 and AUPRC only 0.313, 0.159, 0.193. [ABSTRACT FROM AUTHOR]

Motivation Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams and network graphs. Integration and structuring of such mechanistic information greatly enhances its utility. Results MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology and depiction of knowledge gaps, ambiguities and uncertainties. The web platform enables a user to construct, store, publish, browse, query and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites and critical future experiments. Availability and implementation The MecCog framework is freely available at http://www.meccog.org. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org. [ABSTRACT FROM AUTHOR]

Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention. Short Abstract Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy. [ABSTRACT FROM AUTHOR]

Recent years have seen great advances in the development of glycoproteomics protocols and methods resulting in a sustainable increase in the reporting proteins, their attached glycans and glycosylation sites. However, only very few of these reports find their way into databases or data repositories. One of the major reasons is the absence of digital standard to represent glycoproteins and the challenging annotations with glycans. Depending on the experimental method, such a standard must be able to represent glycans as complete structures or as compositions, store not just single glycans but also represent glycoforms on a specific glycosylation side, deal with partially missing site information if no site mapping was performed, and store abundances or ratios of glycans within a glycoform of a specific site. To support the above, we have developed the GlycoConjugate Ontology (GlycoCoO) as a standard semantic framework to describe and represent glycoproteomics data. GlycoCoO can be used to represent glycoproteomics data in triplestores and can serve as a basis for data exchange formats. The ontology, database providers and supporting documentation are available online (https://github.com/glycoinfo/GlycoCoO). [ABSTRACT FROM AUTHOR]

Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies. [ABSTRACT FROM AUTHOR]

Aspergillosis is an important opportunistic human disease caused by filamentous fungi in the genus Aspergillus. Roughly 70% of infections are caused by Aspergillus fumigatus, with the rest stemming from approximately a dozen other Aspergillus species. Several of these pathogens are closely related to A. fumigatus and belong in the same taxonomic section, section Fumigati. Pathogenic species are frequently most closely related to nonpathogenic ones, suggesting Aspergillus pathogenicity evolved multiple times independently. To understand the repeated evolution of Aspergillus pathogenicity, we performed comparative genomic analyses on 18 strains from 13 species, including 8 species in section Fumigati, which aimed to identify genes, both ones previously connected to virulence as well as ones never before implicated, whose evolution differs between pathogens and nonpathogens. We found that most genes were present in all species, including approximately half of those previously connected to virulence, but a few genes were section-or species-specific. Evolutionary rate analyses identified over 1700 genes whose evolutionary rate differed between pathogens and nonpathogens and dozens of genes whose rates differed between specific pathogens and the rest of the taxa. Functional testing of deletion mutants of 17 transcription factor-encoding genes whose evolution differed between pathogens and nonpathogens identified eight genes that affect either fungal survival in a model of phagocytic killing, host survival in an animal model of fungal disease, or both. These results suggest that the evolution of pathogenicity in Aspergillus involved both conserved and species-specific genetic elements, illustrating how an evolutionary genomic approach informs the study of fungal disease. [ABSTRACT FROM AUTHOR]

Motivation Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this article, we explore challenges and opportunities in research oriented to understanding the relations among elements of socio-economic life and mental health (MH) and the development and evaluation of mental health promotion (HP) initiatives. We review the population health intervention research (PHIR) literature and respond to recommendations regarding social determinants of health and health inequities-focused research. We discuss three inter-related issue areas: first, the continued dominance of linear and individually oriented theories within predominantly quantitative research approaches and the underdevelopment of ontological and theoretical perspectives that capture complexity; second, the inconsistent use of measures of socio-economic status and health with a lack of attention to taken for granted assumptions; and third, the continued focus on measuring MH challenges to the neglect of exploring the meaning of MH in a positive sense. We extend recommendations within the PHIR literature by sharing our application of a historical–dialectical ontological perspective within a process of social praxis with diverse Canadian young people with varying degrees of access to socio-economic resources. Young people were engaged to explore the relations among socio-economic processes, young people's MH and implications for mental HP. We argue that this ontological perspective can support the development of structurally oriented critical qualitative research approaches in PHIR. [ABSTRACT FROM AUTHOR]

Producing findable, accessible, interoperable and reusable (FAIR) data cannot be accomplished solely by data curators in all disciplines. In biology, we have shown that phenotypic data curation is not only costly, but it is burdened with inter-curator variation. We intend to propose a software platform that would enable all data producers, including authors of scientific publications, to produce ontologized data at the time of publication. Working toward this goal, we need to identify ontology construction methods that are preferred by end users. Here, we employ two usability studies to evaluate effectiveness, efficiency and user satisfaction with a set of four methods that allow an end user to add terms and their relations to an ontology. Thirty-three participants took part in a controlled experiment where they evaluated the four methods (Quick Form, Wizard, WebProtégé and Wikidata) after watching demonstration videos and completing a hands-on task. Another think-aloud study was conducted with three professional botanists. The efficiency effectiveness and user confidence in the methods are clearly revealed through statistical and content analyses of participants' comments. Quick Form, Wizard and WebProtégé offer distinct strengths that would benefit our author-driven FAIR data generation system. Features preferred by the participants will guide the design of future iterations. [ABSTRACT FROM AUTHOR]

The biological basis of exercise behavior is increasingly relevant for maintaining healthy lifestyles. Various quantitative genetic studies and selection experiments have conclusively demonstrated substantial heritability for exercise behavior in both humans and laboratory rodents. In the "High Runner" selection experiment, four replicate lines of Mus domesticus were bred for high voluntary wheel running (HR), along with four nonselected control (C) lines. After 61 generations, the genomes of 79 mice (9-10 from each line) were fully sequenced and single nucleotide polymorphisms (SNPs) were identified. We used nested ANOVA with MIVQUE estimation and other approaches to compare allele frequencies between the HR and C lines for both SNPs and haplotypes. Approximately 61 genomic regions, across all somatic chromosomes, showed evidence of differentiation; 12 of these regions were differentiated by all methods of analysis. Gene function was inferred largely using Panther gene ontology terms and KO phenotypes associated with genes of interest. Some of the differentiated genes are known to be associated with behavior/motivational systems and/or athletic ability, including Sorl1, Dach1, and Cdh10. Sorl1 is a sorting protein associated with cholinergic neuron morphology, vascular wound healing, and metabolism. Dach1 is associated with limb bud development and neural differentiation. Cdh10 is a calcium ion binding protein associated with phrenic neurons. Overall, these results indicate that selective breeding for high voluntary exercise has resulted in changes in allele frequencies for multiple genes associated with both motivation and ability for endurance exercise, providing candidate genes that may explain phenotypic changes observed in previous studies. [ABSTRACT FROM AUTHOR]

Motivation The FOBI ontology can be of great help in nutrimetabolomic studies due to its wide variety of applications, including the possibility of performing different enrichment analyses. However, the programming skills required to query and explore it may limit its use by the scientific community. Results Here, we present the fobitools framework, comprised of an R/Bioconductor package and its complementary web interface. These two tools allow researchers to interact and explore the FOBI ontology in a highly user-friendly way. The fobitools framework is focused on the novel concept of food enrichment analysis in nutrimetabolomic studies. However, other useful features, such as the network interactive visualization of FOBI and the automatic annotation of dietary free-text data are also presented. Availability and implementation Both the fobitools R/Bioconductor package and the fobitoolsGUI web-based application, together with their installation instructions and examples, are freely available at https://github.com/nutrimetabolomics/fobitools and https://github.com/nutrimetabolomics/fobitoolsGUI , respectively. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this paper, we present a new semi-automatic methodology for construction of event-based ontology from the library catalogue of the largest collection in the world of metadata records of historical Hebrew manuscripts. Based on the constructed ontology, we developed and implemented a new framework for catalogue data enrichment, correction, and its systematic quantitative analysis. Finally, we demonstrate the results of the proposed large-scale analysis of three most prominent event types in the corpus, as well as a few cross-event relations and trends. [ABSTRACT FROM AUTHOR]

Background Identifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments." Results Here, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features (amino acid sequence, subcellular location, and protein domains) to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model, and to two popular protein function prediction methods (Mashup and deepNF), we demonstrated that our model can achieve state-of-the-art performance. We show the robustness of our model by testing on multiple species. We also provide a web server supporting function query and downstream analysis on-the-fly. Conclusions Graph2GO is the first model that has utilized attributed network representation learning methods to model both interaction networks and protein features for predicting protein functions, and achieved promising performance. Our model can be easily extended to include more protein features to further improve the performance. Besides, Graph2GO is also applicable to other application scenarios involving biological networks, and the learned latent representations can be used as feature inputs for machine learning tasks in various downstream analyses. [ABSTRACT FROM AUTHOR]

In the domain of technology-enhanced competency-based learning and training, there is an increased interest in the integration of competency-related information for supporting competency-driven decision-making purposes. Indeed, since competency development draws upon several related areas, including teaching subjects, instructional design, learning resource annotation, e-Portfolios and motivated by the need for an integrated and semantic-based approach to competency management and development, a series of ontological structures have been formalized and developed for each of these areas. This paper aims to provide a framework specification for lifelong competency management and development, called LCMDF. The main advantage of this framework lies in its ability to provide a unifying semantic foundation in the form of a set of controlled vocabularies for describing competencies and their related details within the contexts of technology-enhanced competency-based learning and training. Moreover, this framework provides a novel integrated model to support a wide range of use cases. The proposed framework results from reusing widespread international standards for competency modeling which helps designing and implementing interoperability architecture of semantically-enhanced competency-based learning/human resource (HR) systems. [ABSTRACT FROM AUTHOR]