The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

Kent A Shefchek¹, Nomi L Harris², Michael Gargano³, Nicolas Matentzoglu⁴, Deepak Unni², Matthew Brush⁵, Daniel Keith¹, Tom Conlin¹, Nicole Vasilevsky⁵, Xingmin Aaron Zhang³, James P Balhoff⁶, Larry Babb⁷, Susan M Bello⁸, Hannah Blau³, Yvonne Bradford⁹, Seth Carbon², Leigh Carmody³, Lauren E Chan¹⁰, Valentina Cipriani¹¹, Alayne Cuzick¹², Maria Della Rocca¹³, Nathan Dunn², Shahim Essaid⁵, Petra Fey¹⁴, Chris Grove¹⁵, Jean-Phillipe Gourdine⁵, Ada Hamosh¹⁶, Midori Harris¹⁷, Ingo Helbig^{18

19

20

21}, Maureen Hoatlin²², Marcin Joachimiak², Simon Jupp⁴, Kenneth B Lett¹, Suzanna E Lewis², Craig McNamara²³, Zoë M Pendlington⁴, Clare Pilgrim¹⁷, Tim Putman¹, Vida Ravanmehr³, Justin Reese², Erin Riggs²⁴, Sofia Robb²⁵, Paola Roncaglia⁴, James Seager¹², Erik Segerdell²⁶, Morgan Similuk²⁷, Andrea L Storm¹³, Courtney Thaxon²⁸, Anne Thessen¹, Julius O B Jacobsen¹¹, Julie A McMurry¹⁰, Tudor Groza²³, Sebastian Köhler²⁹, Damian Smedley¹¹, Peter N Robinson³, Christopher J Mungall², Melissa A Haendel^{1

5}, Monica C Munoz-Torres¹, David Osumi-Sutherland⁴

Affiliations

¹ Center for Genome Research and Biocomputing, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA.
² Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA.
³ The Jackson Laboratory For Genomic Medicine, Farmington, CT 06032, USA.
⁴ European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
⁵ Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA.
⁶ Renaissance Computing Institute at UNC, Chapel Hill, NC 27517, USA.
⁷ Broad Institute, Cambridge, MA 02142, USA.
⁸ The Jackson Laboratory, Bar Harbor, ME 04609, USA.
⁹ Institute of Neuroscience, University of Oregon, Eugene, OR 97401, USA.
¹⁰ College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA.
¹¹ William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
¹² Rothamsted Research, Harpenden AL5 2JQ, UK.
¹³ Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD 20892, USA.
¹⁴ dictyBase, Center for Genetic Medicine, Northwestern University, Chicago, IL 60611, USA.
¹⁵ California Institute of Technology, Pasadena, CA 91125, USA.
¹⁶ McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
¹⁷ University of Cambridge, Cambridge CB2 1TN, UK.
¹⁸ Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
¹⁹ Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
²⁰ Department of Neuropediatrics, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany.
²¹ Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
²² Department of Biochemistry and Molecular Biology, Oregon Health & Science University, Portland, OR 97239, USA.
²³ Pryzm Health, 4215 Queensland, Australia.
²⁴ Autism & Developmental Medicine Institute, Geisinger, Danville, PA 17837, USA.
²⁵ Stowers Institute for Medical Research, Kansas City, MO 64110, USA.
²⁶ Xenbase, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA.
²⁷ National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
²⁸ University of North Carolina Medical School, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
²⁹ Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

PMID: 31701156
PMCID: PMC7056945
DOI: 10.1093/nar/gkz997

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

Kent A Shefchek et al. Nucleic Acids Res. 2020.

. 2020 Jan 8;48(D1):D704-D715.

doi: 10.1093/nar/gkz997.

Authors

Affiliations

¹ Center for Genome Research and Biocomputing, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA.
² Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA.
³ The Jackson Laboratory For Genomic Medicine, Farmington, CT 06032, USA.
⁴ European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
⁵ Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA.
⁶ Renaissance Computing Institute at UNC, Chapel Hill, NC 27517, USA.
⁷ Broad Institute, Cambridge, MA 02142, USA.
⁸ The Jackson Laboratory, Bar Harbor, ME 04609, USA.
⁹ Institute of Neuroscience, University of Oregon, Eugene, OR 97401, USA.
¹⁰ College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA.
¹¹ William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
¹² Rothamsted Research, Harpenden AL5 2JQ, UK.
¹³ Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD 20892, USA.
¹⁴ dictyBase, Center for Genetic Medicine, Northwestern University, Chicago, IL 60611, USA.
¹⁵ California Institute of Technology, Pasadena, CA 91125, USA.
¹⁶ McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
¹⁷ University of Cambridge, Cambridge CB2 1TN, UK.
¹⁸ Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
¹⁹ Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
²⁰ Department of Neuropediatrics, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany.
²¹ Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
²² Department of Biochemistry and Molecular Biology, Oregon Health & Science University, Portland, OR 97239, USA.
²³ Pryzm Health, 4215 Queensland, Australia.
²⁴ Autism & Developmental Medicine Institute, Geisinger, Danville, PA 17837, USA.
²⁵ Stowers Institute for Medical Research, Kansas City, MO 64110, USA.
²⁶ Xenbase, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA.
²⁷ National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
²⁸ University of North Carolina Medical School, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
²⁹ Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

PMID: 31701156
PMCID: PMC7056945
DOI: 10.1093/nar/gkz997

Abstract

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

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Figures

**Figure 1.**
uPheno template-driven ontology development and harmonization. uPheno templates are used to define phenotypes according to agreed upon design patterns. (A). Computable definitions specified using uPheno templates are used to automate classification of uPheno and parts of the Zebrafish Phenotype Ontology (ZP (13); dashed lines). (B). Computable definitions also drive automated classification of HPO and ZP classes under uPheno classes. For example, enlarged heart in ZP (defined using the zebrafish anatomy heart term) and enlarged heart in HPO are both classified under uPheno enlarged heart (defined using Uberon heart). Algorithms can use this classification under uPheno to predict that human orthologs of zebrafish genes annotated to enlarged heart may cause enlarged heart in humans.

**Figure 2.**
Decomposition of a Zebrafish Genotype. The left panel shows classes in the core genotype partonomy. The center panel shows an example instance of each class from the zebrafish genotype (see also https://zfin.org/ZDB-GENO-161227-1). The right panel shows a graphical depiction of the portion of the genome specified at each level (where the top panel shows a complete genome composed of two sets of homologous chromosomes).

**Figure 3.**
A workflow diagram of the Monarch architecture. Since our last report, we have developed the Monarch API (highlighted) for accessing associations between entities, performing computations on phenotype profiles, executing graph traversal queries, and performing text annotation (https://api.monarchinitiative.org/api).

**Figure 4.**
Monarch's data sources. The leftmost set of columns shows the types of data that the integrated data sources serve to Monarch. Note that these sources offer many additional data types that have not yet been integrated into Monarch. Each data source is annotated to specific ontologies and standards, which are, in turn, harmonized using the ontologies indicated in the rightmost panel. Those are used to create an integrated knowledge graph which drives the views and analytics on the Monarch website.

**Figure 5.**
The New Monarch User Interface. A beta version of the new website is available at https://beta.monarchinitiative.org. Entering information on the ‘Search' bar, users can navigate directly to terms suggested via autocomplete, or explore more results through the results tables. In this example, a user enters only part of the name of a disease, ‘Pierpont syndrome’ (A). Selecting the term from the auto-complete menu, the user arrives at an overview page, which offers a summary of all available information in the integrated knowledge graph of the Monarch database (B). Users can explore all available data using a menu of options shown on a panel on the left (B-1), while the information is updated on the main panel on the right (B-2). In this example, the user learns that Pierpont syndrome, a rare subcutaneous tissue disorder, is characterized by phenotypes that include ‘prominent subcalcaneal fat pad' (a term in HPO, with identifier HP:0032276), ‘deep plantar creases' (HP:0001869) and ‘muscular hypotonia' (HP:0001252), among many others (C). Information integrated from the OMIM and Orphanet databases, as well as a number of publications, also support the association of a mutation in one gene, TBL1XR1, as the cause of Pierpont syndrome (D).

**Figure 6.**
Text annotation widget on the new Monarch website. Users can supply free text and retrieve the resulting marked up text with links to terms in various ontologies. In this example, a user has entered text from a publication entitled ‘A specific mutation in TBL1XR1 causes Pierpont syndrome’ (51). The Text Annotator tool (in **beta version**) has highlighted terms identified in various ontologies, and hovering over each highlighted term offers details about the marked up annotations, in this case, ‘abnormal fat distribution.’

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References

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The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

Affiliations

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources