Our understanding of human disease mechanisms and therapies is crucially illuminated by information discovered in model systems. Although numerous resources exist that collect information about model organisms, their very multiplicity makes it difficult for researchers and clinicians to effectively utilize such resources, since each one focuses on a different species, disease, or data type. Furthermore, models do not faithfully recapitulate all aspects of disease pathology, and we need better methods for identifying and designing disease models that focus on elucidating aspects of phenotype and pathophysiology that are most relevant to human disease. The goal of the proposed work is to facilitate disease research by integrating a large diversity of disconnected resources so that we can make better use of existing model organisms and in vitro models. The Monarch Initiative is a global, translational consortium that provides sophisticated algorithms for phenotype comparison within and across species that leverage a large corpus of deeply integrated human and model data. For instance, our Exomiser tool supports the clinical community by prioritizing exomic variants according to variant pathogenicity and phenotypic relevance to support disease-gene discovery. Here we propose to extend the range and precision of Monarch disease modeling by including a greater diversity of species and sources that focus on a broader range of diseases, as well as new categories of clinical data such as disease progression and quantitative measurements. We will utilize these data in our enhanced algorithms that will include temporal ordering of phenotypes, absence of phenotypes, and more model organisms, to provide greater specificity of model-disease matching. Finally, we will make this large, semantically integrated landscape of human and model data navigable by end-users within our website and by community distribution of visualization tools. One example is PhenoGrid, which reveals phenotypic commonalities shared across model, disease, or patients against all other models and diseases in a succinct visual matrix. Finally, we will further assist external resources by prospectively assisting others to provide data compliant to Global Alliance for Genomics and Health and organism identification community standards.

Public Health Relevance

The Monarch Initiative combines structured information about genetics and descriptions of resulting malformations, clinical signs, and symptoms from multiple organisms to increase the utility of animal models and improve our understanding of human diseases. Our computational tools for comparing disease characteristics across species have achieved success in rare disease diagnosis, as well as provisioning for bioinformatics analyses and interactive visualizations. Monarch tools provide clinicians and researchers with previously unavailable insight from numerous information sources to shorten the path of information exchange between the bench and clinic.

National Institute of Health (NIH)
Office of The Director, National Institutes of Health (OD)
Resource-Related Research Projects (R24)
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Special Emphasis Panel (ZRG1)
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Watson, Harold L
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Oregon State University
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Rozman, Jan; Rathkolb, Birgit; Oestereicher, Manuela A et al. (2018) Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Nat Commun 9:288
Dolman, Lena; Page, Angela; Babb, Lawrence et al. (2018) ClinGen advancing genomic data-sharing standards as a GA4GH driver project. Hum Mutat 39:1686-1689
Vasilevsky, Nicole A; Foster, Erin D; Engelstad, Mark E et al. (2018) Plain-language medical vocabulary for precision diagnosis. Nat Genet 50:474-476
Wimalaratne, Sarala M; Juty, Nick; Kunze, John et al. (2018) Uniform resolution of compact identifiers for biomedical data. Sci Data 5:180029
Arachchi, Harindra; Wojcik, Monica H; Weisburd, Benjamin et al. (2018) matchbox: An open-source tool for patient matching via the Matchmaker Exchange. Hum Mutat 39:1827-1834
Meehan, Terrence F; Conte, Nathalie; West, David B et al. (2017) Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium. Nat Genet 49:1231-1238
Köhler, Sebastian; Vasilevsky, Nicole A; Engelstad, Mark et al. (2017) The Human Phenotype Ontology in 2017. Nucleic Acids Res 45:D865-D876
Schubach, Max; Re, Matteo; Robinson, Peter N et al. (2017) Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants. Sci Rep 7:2959
Sobreira, Nara L M; Arachchi, Harindra; Buske, Orion J et al. (2017) Matchmaker Exchange. Curr Protoc Hum Genet 95:9.31.1-9.31.15
Forslund, Kristoffer; Pereira, Cecile; Capella-Gutierrez, Salvador et al. (2017) Gearing up to handle the mosaic nature of life in the quest for orthologs. Bioinformatics :

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