Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs
Abstract The Homo sapiens Chromosomal Location Ontology (HSCLO) is designed to facilitate the integration of human genomic features into biomedical knowledge graphs from releases GRCh37 and GRCh38 at multiple resolutions. HSCLO comprises two distinct versions, HSCLO37 and HSCLO38, each tailored to i...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04358-x |
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| author | Taha Mohseni Ahooyi Benjamin Stear J. Alan Simmons Christopher M. Nemarich Jonathan C. Silverstein Deanne M. Taylor |
| author_facet | Taha Mohseni Ahooyi Benjamin Stear J. Alan Simmons Christopher M. Nemarich Jonathan C. Silverstein Deanne M. Taylor |
| author_sort | Taha Mohseni Ahooyi |
| collection | DOAJ |
| description | Abstract The Homo sapiens Chromosomal Location Ontology (HSCLO) is designed to facilitate the integration of human genomic features into biomedical knowledge graphs from releases GRCh37 and GRCh38 at multiple resolutions. HSCLO comprises two distinct versions, HSCLO37 and HSCLO38, each tailored to its respective human genome release. This ontology supports the efficient integration and analysis of human genomic data across scales ranging from entire chromosomes to individual base pairs, thereby enhancing data retrieval and interoperability within large-scale biomedical datasets. Unlike existing ontologies that primarily focus on genomic feature identification or annotation, HSCLO is specifically engineered to optimize the interoperability and scalability of genomic data within biomedical knowledge graphs. The utility and performance of HSCLO are demonstrated through a case study involving the integration of high-resolution chromatin interaction data, which reveals significant improvements in query efficiency and data linkage. HSCLO represents a valuable resource for advancing research in disease genetics, personalized medicine, and other domains that require complex genomic data integration. |
| format | Article |
| id | doaj-art-88334fbc6d06442f87dd9107434b2f3e |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-88334fbc6d06442f87dd9107434b2f3e2025-01-12T12:07:52ZengNature PortfolioScientific Data2052-44632025-01-011211910.1038/s41597-024-04358-xHomo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge GraphsTaha Mohseni Ahooyi0Benjamin Stear1J. Alan Simmons2Christopher M. Nemarich3Jonathan C. Silverstein4Deanne M. Taylor5The Department of Biomedical and Health Informatics, The Children’s Hospital of PhiladelphiaThe Department of Biomedical and Health Informatics, The Children’s Hospital of PhiladelphiaDepartment of Biomedical Informatics, School of Medicine, The University of PittsburghThe Department of Biomedical and Health Informatics, The Children’s Hospital of PhiladelphiaDepartment of Biomedical Informatics, School of Medicine, The University of PittsburghThe Department of Biomedical and Health Informatics, The Children’s Hospital of PhiladelphiaAbstract The Homo sapiens Chromosomal Location Ontology (HSCLO) is designed to facilitate the integration of human genomic features into biomedical knowledge graphs from releases GRCh37 and GRCh38 at multiple resolutions. HSCLO comprises two distinct versions, HSCLO37 and HSCLO38, each tailored to its respective human genome release. This ontology supports the efficient integration and analysis of human genomic data across scales ranging from entire chromosomes to individual base pairs, thereby enhancing data retrieval and interoperability within large-scale biomedical datasets. Unlike existing ontologies that primarily focus on genomic feature identification or annotation, HSCLO is specifically engineered to optimize the interoperability and scalability of genomic data within biomedical knowledge graphs. The utility and performance of HSCLO are demonstrated through a case study involving the integration of high-resolution chromatin interaction data, which reveals significant improvements in query efficiency and data linkage. HSCLO represents a valuable resource for advancing research in disease genetics, personalized medicine, and other domains that require complex genomic data integration.https://doi.org/10.1038/s41597-024-04358-x |
| spellingShingle | Taha Mohseni Ahooyi Benjamin Stear J. Alan Simmons Christopher M. Nemarich Jonathan C. Silverstein Deanne M. Taylor Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs Scientific Data |
| title | Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs |
| title_full | Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs |
| title_fullStr | Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs |
| title_full_unstemmed | Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs |
| title_short | Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs |
| title_sort | homo sapiens chromosomal location ontology a framework for genomic data in biomedical knowledge graphs |
| url | https://doi.org/10.1038/s41597-024-04358-x |
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