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

Lanping black-boned (LPBB) sheep are a unique and rare ruminant species, characterized by black pigmentation in the skin and internal organs. Thus far, LPBB are the only known animal with heritable melanin characteristics besides the black-boned chicken, and the only mammal known to contain a large amount of melanin in the body. LPBB have therefore attracted substantial research attention, due to their potential contribution to medicine. However, long periods of grazing freely and crossbreeding with Lanping normal sheep (LPN) have diluted LPBB breeding resources, posing a challenge to the protection of species. To ensure the effective conservation and management of LPBB genetic resources, the construction of a large-scale database of genotypic information is therefore very important. To achieve this, we established the first LPBB-specific SNP database, named Black-boned Sheep Genome SNP Database (BbGSD, http://202.203.179.115:3838/oarsnpdb) using sheep genotype data (100 LPBB and 50 LPN) across 46 894 242 SNP sites. In this database, we implemented four main function modules: (i) the "LD heatmap" module, which uses a heatmap to enable the interactive visualization of pairwise linkage disequilibrium (LD) measurements between SNPs; (ii) the "SNP distribution" module, which allows users to interactively visualize tabular genotype data as heat maps; (iii) the "Phylogenetics" module which enables phylogenetic analysis to explore the evolutionary history or genetic relationships of the LPBB sheep; and the "Diversity" module, which can be used to calculate and display the nucleotide diversity among sheep populations in user-specified genomic regions. BbGSD is essential for accelerating studies on the functional genomics and screening of molecular markers of molecular-assisted breeding in black-boned sheep. Database URL: http://202.203.179.115:3838/oarsnpdb.

Lanping black-boned (LPBB) sheep are a unique and rare ruminant species, characterized by black pigmentation in the skin and internal organs. Thus far, LPBB are the only known animal with heritable melanin characteristics besides the black-boned chicken, and the only mammal known to contain a large amount of melanin in the body. LPBB have therefore attracted substantial research attention, due to their potential contribution to medicine. However, long periods of grazing freely and crossbreeding with Lanping normal sheep (LPN) have diluted LPBB breeding resources, posing a challenge to the protection of species. To ensure the effective conservation and management of LPBB genetic resources, the construction of a large-scale database of genotypic information is therefore very important. To achieve this, we established the first LPBB-specific SNP database, named Black-boned Sheep Genome SNP Database (BbGSD, http://202.203.179.115:3838/oarsnpdb) using sheep genotype data (100 LPBB and 50 LPN) across 46 894 242 SNP sites. In this database, we implemented four main function modules: (i) the "LD heatmap" module, which uses a heatmap to enable the interactive visualization of pairwise linkage disequilibrium (LD) measurements between SNPs; (ii) the "SNP distribution" module, which allows users to interactively visualize tabular genotype data as heat maps; (iii) the "Phylogenetics" module which enables phylogenetic analysis to explore the evolutionary history or genetic relationships of the LPBB sheep; and the "Diversity" module, which can be used to calculate and display the nucleotide diversity among sheep populations in user-specified genomic regions. BbGSD is essential for accelerating studies on the functional genomics and screening of molecular markers of molecular-assisted breeding in black-boned sheep. Database URL: http://202.203.179.115:3838/oarsnpdb.

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.