The Human Disease Ontology 2022 update

Schriml, Lynn M.; Munro, James B.; Schor, Mike; Olley, Dustin; McCracken, Carrie; Felix, Victor; Baron, J Allen; Jackson, Rebecca; Bello, Susan M.; Bearer, Cynthia F.; Lichenstein, Richard; Bisordi, Katharine; Dialo, Nicole Campion; Giglio, Michelle; Greene, Carol L.

doi:10.1093/nar/gkab1063

Cited by 139 publications

(97 citation statements)

References 35 publications

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“…In the Alliance, the GO ontologies are used for annotation to molecular functions, biological processes, and cellular components ( https://geneontology.org ); Chemical Entities of Biological Interest (ChEBI; Hastings et al 2016 ) is used for chemical entities; Evidence & Conclusion Ontology for evidence types (ECO; Giglio et al 2019 ); Ontology of bioscientific data analysis and data management (EDAM) for metadata ( Ison et al 2013 ); Experimental Factor Ontology for experimental variables (EFO; Malone et al 2010 ); Human Phenotype Ontology for human phenotypes (HPO; Köhler et al 2021 ); Mammalian Phenotype ontology for mouse and rat phenotypes (MP; Smith and Eppig 2009 ); WBPhenotype for worm phenotypes ( Schindelman et al 2011 ); Drosophila Phenotype Ontology for fly phenotypes (DPO; Osumi-Sutherland et al 2013 ); Ascomycete Phenotype Ontology for yeast phenotypes (APO; Engel et al 2010 ); Proteomics Standards Initiative—Molecular Interaction for molecular interactions (PSI-MI; Kerrien et al 2007 ), Proteomics Standards Initiative—Protein Modification Ontology for protein modifications (PSI-MOD; Montecchi-Palazzi et al 2008 ); Disease Ontology for human disease and disease model annotations (DO; Schriml et al 2022 ); the Cell Ontology for cell type (CL; Diehl et al 2016 ); Uberon for animal anatomy ( Haendel et al 2014 ); Mouse Developmental Anatomy Ontology for mouse anatomy (EMAPA; Hayamizu et al 2015 ); Zebrafish anatomy (ZFA) and development ontology for (ZFA; Van Slyke et al 2014 ); Drosophila gross anatomy for fly anatomy (FBbt; Costa et al 2013 ); C. elegans Gross Anatomy Ontology for worm anatomy (WBbt; Lee and Sternberg 2003 ); WormBase life stage ontology for worm developmental stages (WBls; W. Chen and D. Raciti, unpublished); Sequence Ontology (SO; Mungall et al 2011 ; Sant et al 2021 ) for sequence features; Relation Ontology (RO; Smith et al 2005 ) for relations; and Measurement Method Ontology (MMO; Smith et al 2013 ) for expression assays.…”

Section: Introduction: the Model Organism Databases The Goals And The...mentioning

confidence: 99%

Harmonizing model organism data in the Alliance of Genome Resources

Agapite¹,

Albou²,

Aleksander³

et al. 2022

Genetics

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The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein–protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

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Section: Introduction: the Model Organism Databases The Goals And The...mentioning

confidence: 99%

Harmonizing model organism data in the Alliance of Genome Resources

Agapite¹,

Albou²,

Aleksander³

et al. 2022

Genetics

View full text Add to dashboard Cite

show abstract

“…Further, the machine-readability contents of WikiPathways provides a unique high level of interoperability with all major databases for genes, proteins, chemicals and probes as well as direct links to PubMed entries, PubMedCentral, and Scholia ( 59 ), and is annotated with the Pathway Ontology ( 60 ), Human Disease Ontology ( 61 ) and Cell Type Ontology ( 62 ) for improved semantic meaning and usefulness in computational approaches. These aspects make the pathway compliant to most types of data for analysis purposes, allow for a variety of computational approaches, workflows, and integrations with other tools and databases ( 20 , 21 ), and align with the recently established FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship ( 63 ), which dictate the future of data-driven science.…”

Section: Discussionmentioning

confidence: 99%

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma

et al. 2022

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Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.

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“…Medical Subject Headings (MeSH) is the standard used for cancer and drugs, while HUGO Gene Nomenclature Committee is for genes. We also map diseases to Disease Ontology (37) terms whenever possible to make them more interoperable with databases like CIViC (8). We adopted the standardized findings of tmVar without additional processing due to the vast number of variations and the lack of a single standard library akin to the MeSH entry library.…”

Section: System Descriptionmentioning

confidence: 99%

OncoPubMiner: A platform for oncology publication mining

Xu¹,

Liu

Sun³

et al. 2022

Preprint

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Knowledge bases that are up-to-date and of expert quality are fundamental in biomedical research fields. A knowledge base established with human participation and subjected to multiple inspections is crucial for supporting clinical decision-making, especially in the exponentially growing field of precision oncology. The number of original publications in the field has skyrocketed with the advancement of technology and in-depth research evolved. It has become an increasingly pressing issue that researchers need to consider how to gather and mine these articles accurately and efficiently. In this paper, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customization, publication search with online reading, project-centered and team-based data collection to realize a one-stop "keyword in, knowledge out" oncology publication mining platform. It was built by integrating all the open-access abstracts from PubMed and full-text articles from PubMed Central, and is updated on a daily basis. The system makes it straightforward to obtain precision oncology knowledge from scientific articles. OncoPubMiner will assist researchers in developing professional structured knowledge base systems efficiently, and bringing the oncology community closer to achieving precision oncology goals.

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The Human Disease Ontology 2022 update

Cited by 139 publications

References 35 publications

Harmonizing model organism data in the Alliance of Genome Resources

Harmonizing model organism data in the Alliance of Genome Resources

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma

OncoPubMiner: A platform for oncology publication mining

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