Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon

Haendel, Melissa; Balhoff, James P.; Bastian, Frederic; Blackburn, David C.; Blake, Judith A.; Bradford, Yvonne M.; Comte, Aurélie; Dahdul, Wasila; Dececchi, T. Alexander; Druzinsky, Robert E.; Hayamizu, Terry F.; Ibrahim, Nizar; Lewis, Suzanna; Mabee, Paula M.; Niknejad, Anne; Robinson‐Rechavi, Marc; Sereno, Paul C.; Mungall, Christopher J.

doi:10.1186/2041-1480-5-21

Cited by 131 publications

(142 citation statements)

References 39 publications

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“…We aggregate these into a unified ontology (https://github.com/monarch-initiative/monarch-ontology/) and make use of bridging ontologies and our curated integrative ontologies to connect these together. In particular: The Uber-anatomy ontology (Uberon) bridges species-specific and clinical anatomical and tissue ontologies (23)The unified phenotype ontology bridges model organism and human phenotype ontologies and terminologies, using techniques described in (24,25)The Monarch Merged Disease Ontology (MonDO) uses a Bayes ontology merging algorithm (26) to integrate multiple human disease resources into a single ontology, and additionally includes animal diseases from OMIA.The Genotype Ontology (GENO) (19) defines genotypic elements and bridges the Sequence Ontology (SO) (27) and FALDO (28). GENO allows the propagation of phenotypes that are annotated to genotypic elements.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

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Section: Introductionmentioning

confidence: 99%

“…The Uber-anatomy ontology (Uberon) bridges species-specific and clinical anatomical and tissue ontologies (23)…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…4,5 A number of methods make use of the Uberon anatomy ontology to connect phenotype terms across species. 6,7 For example, the human phenotype "Abnormality of the upper limb" (HP 0002817) is connected to the mouse phenotype "abnormal forelimb morphology" (MP 0000550) via the Uberon class "forelimb" (UBERON 0002102).…”

Section: Cross-species Phenotype Matchingmentioning

confidence: 99%

Investigating the Importance of Anatomical Homology for Cross-Species Phenotype Comparisons Using Semantic Similarity

Manda¹,

Mungall²,

Balhoff

et al. 2015

Biocomputing 2016

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There is growing use of ontologies for the measurement of cross-species phenotype similarity. Such similarity measurements contribute to diverse applications, such as identifying genetic models for human diseases, transferring knowledge among model organisms, and studying the genetic basis of evolutionary innovations. Two organismal features, whether genes, anatomical parts, or any other inherited feature, are considered to be homologous when they are evolutionarily derived from a single feature in a common ancestor. A classic example is the homology between the paired fins of fishes and vertebrate limbs. Anatomical ontologies that model the structural relations among parts may fail to include some known anatomical homologies unless they are deliberately added as separate axioms. The consequences of neglecting known homologies for applications that rely on such ontologies has not been well studied. Here, we examine how semantic similarity is affected when external homology knowledge is included. We measure phenotypic similarity between orthologous and non-orthologous gene pairs between humans and either mouse or zebrafish, and compare the inclusion of real with faux homology axioms. Semantic similarity was preferentially increased for orthologs when using real homology axioms, but only in the more divergent of the two species comparisons (human to zebrafish, not human to mouse), and the relative increase was less than 1% to non-orthologs. By contrast, inclusion of both real and faux random homology axioms preferentially increased similarities between genes that were initially more dissimilar in the other comparisons. Biologically meaningful increases in semantic similarity were seen for a select subset of gene pairs. Overall, the effect of including homology axioms on cross-species semantic similarity was modest at the levels of divergence examined here, but our results hint that it may be greater for more distant species comparisons.

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“…Examples are the Human Phenotype Ontology (Köhler et al 2014) and the Mouse Anatomy Ontology (Hayamizu et al 2015). The Monarch Initiative has developed four speciesagnostic ontologies designed to unify their species-specific counterparts: GENO for genotypes , Uberpheno for phenotypes , UBERON for anatomy (Haendel et al 2014), and MONDO for diseases (Mungall et al 2016). These ontologies provide a bridge between species-/domain-specific ontologies, allowing unified analysis of disparate data sources (Figure 3).…”

Section: A Common Conceptual Frameworkmentioning

confidence: 99%

Navigating the Phenotype Frontier: The Monarch Initiative

et al. 2016

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The principles of genetics apply across the entire tree of life. At the cellular level we share biological mechanisms with species from which we diverged millions, even billions of years ago. We can exploit this common ancestry to learn about health and disease, by analyzing DNA and protein sequences, but also through the observable outcomes of genetic differences, i.e. phenotypes. To solve challenging disease problems we need to unify the heterogeneous data that relates genomics to disease traits. Without a big-picture view of phenotypic data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) provides tools for genotype-phenotype analysis, genomic diagnostics, and precision medicine across broad areas of disease.

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Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon

Cited by 131 publications

References 39 publications

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

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

Investigating the Importance of Anatomical Homology for Cross-Species Phenotype Comparisons Using Semantic Similarity

Navigating the Phenotype Frontier: The Monarch Initiative

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