Database: The Journal of Biological Databases and Curation最新文献

The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data. TOXIN KG uses graph-structured semantic technology and integrates toxicological data through ontologies, ensuring interoperable representation. The primary data source is safety information on cosmetic ingredients from scientific opinions issued by the Scientific Committee on Consumer Safety between 2009 and 2019. The ToxRTool automates the reliability assessment of toxicity studies, while the Simplified Molecular Input Line Entry System (SMILES) notation standardizes chemical identification, enabling in silico prediction of repeated-dose toxicity via the implementation of the Organization for Economic Co-operation and Development Quantitative Structure-Activity Relationship Toolbox (OECD QSAR Toolbox). The ToXic Process Ontology, enriched with relevant biological repositories, is employed to represent toxicological concepts systematically. Search filters allow the identification of cosmetic compounds potentially linked to liver toxicity. Data visualization is achieved through Ontodia, a JavaScript library. TOXIN KG, filled with information for 88 cosmetic ingredients, allowed us to identify 53 compounds affecting at least one liver toxicity parameter in a 90-day repeated-dose animal study. For one compound, we illustrate how TOXIN KG links this observation to hepatic cholestasis as an adverse outcome. In an ab initio NGRA context, follow-up in vitro studies using human-based NAMs would be necessary to understand the compound's biological activity and the molecular mechanism leading to the adverse effect. In summary, TOXIN KG emerges as a valuable tool for advancing the reusability of cosmetics safety data, providing knowledge in support of NAM-based hazard and risk assessments. Database URL: https://toxin-search.netlify.app/.

The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data. TOXIN KG uses graph-structured semantic technology and integrates toxicological data through ontologies, ensuring interoperable representation. The primary data source is safety information on cosmetic ingredients from scientific opinions issued by the Scientific Committee on Consumer Safety between 2009 and 2019. The ToxRTool automates the reliability assessment of toxicity studies, while the Simplified Molecular Input Line Entry System (SMILES) notation standardizes chemical identification, enabling in silico prediction of repeated-dose toxicity via the implementation of the Organization for Economic Co-operation and Development Quantitative Structure-Activity Relationship Toolbox (OECD QSAR Toolbox). The ToXic Process Ontology, enriched with relevant biological repositories, is employed to represent toxicological concepts systematically. Search filters allow the identification of cosmetic compounds potentially linked to liver toxicity. Data visualization is achieved through Ontodia, a JavaScript library. TOXIN KG, filled with information for 88 cosmetic ingredients, allowed us to identify 53 compounds affecting at least one liver toxicity parameter in a 90-day repeated-dose animal study. For one compound, we illustrate how TOXIN KG links this observation to hepatic cholestasis as an adverse outcome. In an ab initio NGRA context, follow-up in vitro studies using human-based NAMs would be necessary to understand the compound's biological activity and the molecular mechanism leading to the adverse effect. In summary, TOXIN KG emerges as a valuable tool for advancing the reusability of cosmetics safety data, providing knowledge in support of NAM-based hazard and risk assessments. Database URL: https://toxin-search.netlify.app/.

Amidst the global challenge of extreme poverty, the livestock sector can significantly contribute to global sustainable development goals by enhancing resilience, smallholder productivity, and market participation. The Indian livestock sector is one of the largest in the world with a total livestock population of 535.82 million, ∼10.7% of the world's livestock population. Buffalo (Bubalus bubalis) holds significant importance in India and other Asian countries, notably contributing to their economies by surpassing cattle in milk production and providing various valuable products. The limited availability of genomic and transcriptomic resources for buffaloes hinders the efforts to enhance their traits for increased milk and meat production. To address this gap, this study adopted the state-of-the-art bioinformatics tools to analyse 2429 transcriptomes representing 438 BioSamples from 23 BioProjects obtained from a public domain database, representing 76 different types of tissues and cell types from all major organ systems in buffalo species (river and swamp). The outcome of this exhaustive genomic data led to the development of a relational buffalo expression database based on a three-tier architecture named as BuffExDb (http://46.202.167.198/buffex/). The user-friendliness and flexibilities in retrieval of tissue-specific genes (TSGs) and their functional annotation are the major characteristics of BuffExDb. This is the first of its kind that offers an effortlessly navigable and filterable database, enabling users to examine and visualize the expression levels of each tissue across multiple samples, simultaneously. It also provides the Tau score parameter for the identification of TSGs along with their essential roles in tissue development, maintenance, and function as observed through the enrichment test for gene ontologies. The exhaustive outcome of this work would pave the way for the biological, functional, and evolutionary studies for easy access. This prior information based on tissue-specific mechanisms can be used for future genomic research, especially in association studies in endeavour of enhanced buffalo breeding and conservation programmes. Database URL: http://46.202.167.198/buffex/.

Amidst the global challenge of extreme poverty, the livestock sector can significantly contribute to global sustainable development goals by enhancing resilience, smallholder productivity, and market participation. The Indian livestock sector is one of the largest in the world with a total livestock population of 535.82 million, ∼10.7% of the world's livestock population. Buffalo (Bubalus bubalis) holds significant importance in India and other Asian countries, notably contributing to their economies by surpassing cattle in milk production and providing various valuable products. The limited availability of genomic and transcriptomic resources for buffaloes hinders the efforts to enhance their traits for increased milk and meat production. To address this gap, this study adopted the state-of-the-art bioinformatics tools to analyse 2429 transcriptomes representing 438 BioSamples from 23 BioProjects obtained from a public domain database, representing 76 different types of tissues and cell types from all major organ systems in buffalo species (river and swamp). The outcome of this exhaustive genomic data led to the development of a relational buffalo expression database based on a three-tier architecture named as BuffExDb (http://46.202.167.198/buffex/). The user-friendliness and flexibilities in retrieval of tissue-specific genes (TSGs) and their functional annotation are the major characteristics of BuffExDb. This is the first of its kind that offers an effortlessly navigable and filterable database, enabling users to examine and visualize the expression levels of each tissue across multiple samples, simultaneously. It also provides the Tau score parameter for the identification of TSGs along with their essential roles in tissue development, maintenance, and function as observed through the enrichment test for gene ontologies. The exhaustive outcome of this work would pave the way for the biological, functional, and evolutionary studies for easy access. This prior information based on tissue-specific mechanisms can be used for future genomic research, especially in association studies in endeavour of enhanced buffalo breeding and conservation programmes. Database URL: http://46.202.167.198/buffex/.

The Rat Genome Database (RGD) is a multispecies knowledgebase which integrates genetic, multiomic, phenotypic, and disease data across 10 mammalian species. To support cross-species, multiomics studies and to enhance and expand on data manually extracted from the biomedical literature by the RGD team of expert curators, RGD imports and integrates data from multiple sources. These include major databases and a substantial number of domain-specific resources, as well as direct submissions by individual researchers. The incorporation of these diverse datatypes is handled by a growing list of automated import, export, data processing, and quality control pipelines. This article outlines the development over time of a standardized infrastructure for automated RGD pipelines with a summary of key design decisions and a focus on lessons learned.

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of "apply patch" and "diff" commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders-e.g. ontology edits can be specified by commands like "add synonym 'arm' to 'forelimb'" or "move 'Parkinson disease' under 'neurodegenerative disease'." We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs. Database URL: https://github.com/INCATools/kgcl.

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of "apply patch" and "diff" commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders-e.g. ontology edits can be specified by commands like "add synonym 'arm' to 'forelimb'" or "move 'Parkinson disease' under 'neurodegenerative disease'." We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs. Database URL: https://github.com/INCATools/kgcl.

The Rat Genome Database (RGD) is a multispecies knowledgebase which integrates genetic, multiomic, phenotypic, and disease data across 10 mammalian species. To support cross-species, multiomics studies and to enhance and expand on data manually extracted from the biomedical literature by the RGD team of expert curators, RGD imports and integrates data from multiple sources. These include major databases and a substantial number of domain-specific resources, as well as direct submissions by individual researchers. The incorporation of these diverse datatypes is handled by a growing list of automated import, export, data processing, and quality control pipelines. This article outlines the development over time of a standardized infrastructure for automated RGD pipelines with a summary of key design decisions and a focus on lessons learned.

Snakes are among the most difficult terrestrial vertebrates to survey, resulting in poor distributional information on most species. This database comprises of 3812 records of 58 species of snakes in 37 genera reported from within the boundaries of Eswatini. The data were compiled from multiple sources including museum specimens, iNaturalist records, literature records, and snake rescue operations. For each specimen reported in the database, we provide the scientific name, latitude and longitude coordinates, and location. Most records also have an associated date. This comprehensive database will be useful to biodiversity experts, conservationists, medical practitioners, researchers, and snake enthusiasts, especially for mapping and modelling snake distributions in the country. To allow easy viewing of the distribution of snakes in the country, we provide an online visualization tool, which should allow a greater number of non-scientists to utilize this database.

The Microbe Directory (TMD) is a centralized database of metadata for microbes from all domains that helps with the biological interpretation of metagenomic data. The database comprises phenotypical and ecological traits of microorganisms, which have been verified by independent manual annotations. This effort has been possible by the help of a community of volunteer students worldwide who were trained in manual curation of microbiology data. To summarize this information, we have built an interactive browser that makes the database accessible to everyone, including non-bioinformaticians. We used the TMD data to analyse microbiome samples from different projects such as MetaSUB, TARA Oceans, Human Microbiome Project, and Sponge Microbiome Project, showcasing the utility of TMD. Furthermore, we compare our microbial annotations with annotations collected by artificial intelligence (AI) and demonstrate that despite the high speed of AI in reviewing and collecting microbial data, annotation requires domain knowledge and therefore manual curation. Collectively, TMD provides a unique source of information that can help to interpret microbiome data and uncover biological associations. Database URL: www.themicrobedirectory.com/.