Linking common human diseases to their phenotypes; development of a resource for human phenomics
Abstract Background In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations...
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | Journal of Biomedical Semantics |
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| Online Access: | https://doi.org/10.1186/s13326-021-00249-x |
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| author | Şenay Kafkas Sara Althubaiti Georgios V. Gkoutos Robert Hoehndorf Paul N. Schofield |
| author_facet | Şenay Kafkas Sara Althubaiti Georgios V. Gkoutos Robert Hoehndorf Paul N. Schofield |
| author_sort | Şenay Kafkas |
| collection | DOAJ |
| description | Abstract Background In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. Methods We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships. Results We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease–phenotype associations, measuring their similarity to disease–phenotype associations found in public databases, and assessing how well they could be used to recover gene–disease associations using phenotype similarity. Conclusion We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at https://doi.org/10.5281/zenodo.4726713 . |
| first_indexed | 2024-12-21T15:35:46Z |
| format | Article |
| id | doaj.art-d0d6817ac7ad4aa6b7df1d70783c2e2b |
| institution | Directory Open Access Journal |
| issn | 2041-1480 |
| language | English |
| last_indexed | 2024-12-21T15:35:46Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Semantics |
| spelling | doaj.art-d0d6817ac7ad4aa6b7df1d70783c2e2b2022-12-21T18:58:39ZengBMCJournal of Biomedical Semantics2041-14802021-08-0112111510.1186/s13326-021-00249-xLinking common human diseases to their phenotypes; development of a resource for human phenomicsŞenay Kafkas0Sara Althubaiti1Georgios V. Gkoutos2Robert Hoehndorf3Paul N. Schofield4Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and TechnologyComputational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and TechnologyHealth Data Research UK, Midlands siteComputational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and TechnologyDepartment of Physiology, Development & Neuroscience, University of CambridgeAbstract Background In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. Methods We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships. Results We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease–phenotype associations, measuring their similarity to disease–phenotype associations found in public databases, and assessing how well they could be used to recover gene–disease associations using phenotype similarity. Conclusion We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at https://doi.org/10.5281/zenodo.4726713 .https://doi.org/10.1186/s13326-021-00249-xDisease–phenotype associationsOntologiesText miningUK Biobank |
| spellingShingle | Şenay Kafkas Sara Althubaiti Georgios V. Gkoutos Robert Hoehndorf Paul N. Schofield Linking common human diseases to their phenotypes; development of a resource for human phenomics Journal of Biomedical Semantics Disease–phenotype associations Ontologies Text mining UK Biobank |
| title | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
| title_full | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
| title_fullStr | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
| title_full_unstemmed | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
| title_short | Linking common human diseases to their phenotypes; development of a resource for human phenomics |
| title_sort | linking common human diseases to their phenotypes development of a resource for human phenomics |
| topic | Disease–phenotype associations Ontologies Text mining UK Biobank |
| url | https://doi.org/10.1186/s13326-021-00249-x |
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