Harmonizing model organism data in the Alliance of Genome Resources

Agapite, Julie; Albou, Laurent‐Philippe; Aleksander, Suzi; Alexander, Micheal; Anagnostopoulos, Anna V.; Antonazzo, Giulia; Argasinska, Joanna; Arnaboldi, Valerio; Attrill, Helen; Becerra, Andrés; Bello, Susan M.; Blake, Judith A.; Blodgett, Olin; Bradford, Yvonne M.; Bult, Carol J.; Cain, Scott; Calvi, Brian R.; Carbon, Seth; Chan, Juancarlos; Chen, Wen J.; Cherry, J. Michael; Cho, Jae Hyoung; Christie, Karen R.; Crosby, Madeline A.; Davis, Paul A.; Beltrame, Eduardo da Veiga; Pons, Jeffrey L De; D'Eustachio, P; Diamantakis, Stavros; Dolan, M. Eileen; Santos, Gilberto dos; Douglass, Eric; Dunn, Barbara K.; Eagle, Anne; Ebert, Dustin; Engel, Stacia R.; Fashena, David; Foley, Saoirse; Frazer, Ken; Gao, Sibyl; Gibson, Adam C; Gondwe, Felix; Goodman, Josh; Gramates, L. Sian; Grove, Christian A.; Hale, Paul; Harris, Todd W.; Hayman, G. Thomas; Hill, David P.; Howe, Douglas G.; Howe, Kevin; Hu, Yanhui; Jha, Sagar; Kadin, James A.; Kaufman, Thomas C.; Kalita, Patrick; Karra, Kalpana; Kishore, Ranjana; Kwitek, Anne E.; Laulederkind, Stanley J. F.; Lee, Raymond; Longden, Ian; Luypaert, Manuel; MacPherson, Kevin; Martin, R. D.; Marygold, Steven J; Matthews, Beverley; McAndrews, Monica; Millburn, Gillian; Miyasato, Stuart R.; Motenko, Howie; Moxon, Sierra; Müller, Hans‐Michael; Mungall, Christopher J.; Muruganujan, Anushya; Mushayahama, Tremayne; Nalabolu, Harika S; Nash, Robert S.; Ng, Patrick; Nuin, Paulo; Paddock, Holly; Paulini, Michael; Perrimon, Norbert; Pich, Christian; Quinton-Tulloch, Mark; Raciti, Daniela; Ramachandran, Sridhar; Richardson, Joel E.; Gelbart, Susan Russo; Ruzicka, Leyla; Schaper, Kevin; Schindelman, Gary; Shimoyama, Mary; Simison, Matt; Shaw, David R.; Shrivatsav, Ajay; Singer, Amy; Skrzypek, Marek S.; Smith, Cyndi M.; Smith, Cynthia L.; Smith, Jennifer; Stein, Lincoln; Sternberg, Paul W.; Tabone, Christopher J.; Thomas, Paul D.; Thorat, Ketaki; Thota, Jyothi; Toro, Sabrina; Tomczuk, Monika; Trovisco, Vítor; Tutaj, Marek; Tutaj, Monika; Urbano, Jose-Maria; Auken, Kimberly Van; Slyke, Ceri E. Van; Wang, Qinghua; Wang, Shur-Jen; Weng, Shuai; Westerfield, Monte; Williams, Gary W.; Wilming, Laurens G.; Wong, Edith D.; Wright, Adam; Yook, Karen; Zarowiecki, Magdalena; Zhou, Pinglei; Zytkovicz, Mark

doi:10.1093/genetics/iyac022

Cited by 63 publications

(50 citation statements)

References 69 publications

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“…Table 2 summarizes the ROCAUC values for each organism using the four approaches (Resnik’s similarity, OPA2Vec, OWL2Vec* and DL2Vec), and show the results for the Alliance dataset ( Agapite et al, 2022 ). Similar to the first experiment, mouse phenotypes showed the highest performance across all methods we considered, and the mouse was the only organism for which all four methods to compute phenotype similarity showed a predictive performance that was better than random.…”

Section: Resultsmentioning

confidence: 99%

Contribution of model organism phenotypes to the computational identification of human disease genes

Alghamdi

Schofield

Hoehndorf

2022

Disease Models &Amp; Mechanisms

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Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation. This article has an associated First Person interview with the first author of the paper.

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Section: Resultsmentioning

confidence: 99%

Contribution of model organism phenotypes to the computational identification of human disease genes

Alghamdi

Schofield

Hoehndorf

2022

Disease Models &Amp; Mechanisms

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“…The information of tissue localization of RIF signature genes was obtained through the BioGPS (building your own mash-up of gene annotations and expression profiles) website ( ) ( 39 ) and the Human Protein Atlas ( ) ( 40 ). Moreover, to further perceive the functions of these genes, we searched the databases of BioGPS, GeneCards ( ) ( 41 ), Alliance of Genome Resources ( ) ( 42 ), and UniProt ( ) ( 43 ).…”

Section: Methodsmentioning

confidence: 99%

“…proteinatlas.org/) (40). Moreover, to further perceive the functions of these genes, we searched the databases of BioGPS, GeneCards (https://www.genecards.org/) (41), Alliance of Genome Resources (https://www.alliancegenome.org/) (42), and UniProt (https://www.uniprot.org/) (43).…”

Section: The Tissue Localization and Function Of Rif Signature Genesmentioning

confidence: 99%

Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Zhao

Zhao²,

Jiang³

et al. 2022

Front. Immunol.

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Recurrent implantation failure (RIF) is an extremely thorny issue in in-vitro fertilization (IVF)-embryo transfer (ET). However, its intricate etiology and pathological mechanisms are still unclear. Nowadays, there has been extensive interest in cellular senescence in RIF, and its involvement in endometrial immune characteristics during the window of implantation (WOI) has captured scholars’ growing concerns. Therefore, this study aims to probe into the pathological mechanism of RIF from cellular senescence and investigate the correlation between cellular senescence and endometrial immune characteristics during WOI based on bioinformatics combined with machine learning strategy, so as to elucidate the underlying pathological mechanisms of RIF and to explore novel treatment strategies for RIF. Firstly, the gene sets of GSE26787 and GSE111974 from the Gene Expression Omnibus (GEO) database were included for the weighted gene correlation network analysis (WGCNA), from which we concluded that the genes of the core module were closely related to cell fate decision and immune regulation. Subsequently, we identified 25 cellular senescence-associated differentially expressed genes (DEGs) in RIF by intersecting DEGs with cellular senescence-associated genes from the Cell Senescence (CellAge) database. Moreover, functional enrichment analysis was conducted to further reveal the specific molecular mechanisms by which these molecules regulate cellular senescence and immune pathways. Then, eight signature genes were determined by the machine learning method of support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and artificial neural network (ANN), comprising LATS1, EHF, DUSP16, ADCK5, PATZ1, DEK, MAP2K1, and ETS2, which were also validated in the testing gene set (GSE106602). Furthermore, distinct immune microenvironment abnormalities in the RIF endometrium during WOI were comprehensively explored and validated in GSE106602, including infiltrating immunocytes, immune function, and the expression profiling of human leukocyte antigen (HLA) genes and immune checkpoint genes. Moreover, the correlation between the eight signature genes with the endometrial immune landscape of RIF was also evaluated. After that, two distinct subtypes with significantly distinct immune infiltration characteristics were identified by consensus clustering analysis based on the eight signature genes. Finally, a “KEGG pathway–RIF signature genes–immune landscape” association network was constructed to intuitively uncover their connection. In conclusion, this study demonstrated that cellular senescence might play a pushing role in the pathological mechanism of RIF, which might be closely related to its impact on the immune microenvironment during the WOI phase. The exploration of the molecular mechanism of cellular senescence in RIF is expected to bring new breakthroughs for disease diagnosis and treatment strategies.

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“…A growing number of other projects also consult and reuse components of the Biolink Model in designing their models. For example, the Alliance of Genome Resources 24 imports some Biolink Model components, even though they do not use the entirety of Biolink Model. Other initiatives that rely on Biolink Model for data and knowledge harmonization include KG‐COVID‐19 10 and KG‐Microbe.…”

Section: Applications Of Biolink Modelmentioning

confidence: 99%

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Unni

Moxon

Bada

et al. 2022

Clinical Translational Sci

Self Cite

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Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

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Harmonizing model organism data in the Alliance of Genome Resources

Cited by 63 publications

References 69 publications

Contribution of model organism phenotypes to the computational identification of human disease genes

Contribution of model organism phenotypes to the computational identification of human disease genes

Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

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