The mouse-human anatomy ontology mapping project

Hayamizu, Terry F.; Coronado, Sherri de; Fragoso, Gilberto; Sioutos, Nicholas; Kadin, James A.; Ringwald, Martin

doi:10.1093/database/bar066

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…Monarch uses four species-neutral ontologies that unify their species-specific counterparts (as shown in Figure 3): GENO for genotypes (19), UPheno for phenotypes (25), UBERON for anatomy (23), and MonDO for diseases (26). Prior efforts to map or integrate species-specific anatomical ontologies (24,48), for example, have been utilized in the construction of these species-neutral ontologies. The end result is a translational platform that allows a unified view of human, model and non-model organism biology.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

et al. 2016

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“…Methods are similar to the ones investigated in previous experiments performed with the CellFinder corpus (46). To enable data integration into the CellFinder database, all extracted mentions must be normalized to any of the ontologies or terminologies currently supported by our database: Cell Ontology (CL) (47), Cell Line Ontology (CLO) (48), EHDAA2 (49), Experimental Factor Ontology (EFO) (50), Foundational Model of Anatomy (FMA) (51), GO (52), Adult Mouse Anatomy (MA) (53) and Uberon (54). …”

Section: Methodsmentioning

confidence: 99%

Preliminary evaluation of the CellFinder literature curation pipeline for gene expression in kidney cells and anatomical parts

et al. 2013

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Biomedical literature curation is the process of automatically and/or manually deriving knowledge from scientific publications and recording it into specialized databases for structured delivery to users. It is a slow, error-prone, complex, costly and, yet, highly important task. Previous experiences have proven that text mining can assist in its many phases, especially, in triage of relevant documents and extraction of named entities and biological events. Here, we present the curation pipeline of the CellFinder database, a repository of cell research, which includes data derived from literature curation and microarrays to identify cell types, cell lines, organs and so forth, and especially patterns in gene expression. The curation pipeline is based on freely available tools in all text mining steps, as well as the manual validation of extracted data. Preliminary results are presented for a data set of 2376 full texts from which >4500 gene expression events in cell or anatomical part have been extracted. Validation of half of this data resulted in a precision of ∼50% of the extracted data, which indicates that we are on the right track with our pipeline for the proposed task. However, evaluation of the methods shows that there is still room for improvement in the named-entity recognition and that a larger and more robust corpus is needed to achieve a better performance for event extraction.Database URL: http://www.cellfinder.org/

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“…Ontologies for adult mouse anatomy [Hayamizu et al, 2005]; developmental mouse anatomy [Armit et al, 2012; Baldock et al, 1999], and mammalian phenotype [Smith and Eppig, 2012] parallel the human ontologies. A number of efforts have developed cross-species ontological links for both anatomic [Travillian et al, 2011b; Dahdul et al, 2012; Mungall et al, 2012; Hayamizu et al, 2012; Niknejad et al, 2012] and phenotypic [Kohler et al, 2013] descriptors.…”

Section: Why a Dedicated Ontology For The Craniofacial Community?mentioning

confidence: 99%

The ontology of craniofacial development and malformation for translational craniofacial research

Brinkley

Borromeo

Clarkson³

et al. 2013

American J of Med Genetics Pt C

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We introduce the Ontology of Craniofacial Development and Malformation (OCDM) as a mechanism for representing knowledge about craniofacial development and malformation, and for using that knowledge to facilitate integrating craniofacial data obtained via multiple techniques from multiple labs and at multiple levels of granularity. The OCDM is a project of the NIDCR-sponsored FaceBase Consortium, whose goal is to promote and enable research into the genetic and epigenetic causes of specific craniofacial abnormalities through the provision of publicly accessible, integrated craniofacial data. However, the OCDM should be usable for integrating any web-accessible craniofacial data, not just those data available through FaceBase. The OCDM is based on the Foundational Model of Anatomy (FMA), our comprehensive ontology of canonical human adult anatomy, and includes modules to represent adult and developmental craniofacial anatomy in both human and mouse, mappings between homologous structures in human and mouse, and associated malformations. We describe these modules, as well as prototype uses of the OCDM for integrating craniofacial data. By using the terms from the OCDM to annotate data, and by combining queries over the ontology with those over annotated data, it becomes possible to create “intelligent” queries that can, for example, find gene expression data obtained from mouse structures that are precursors to homologous human structures involved in malformations such as cleft lip. We suggest that the OCDM can be useful not only for integrating craniofacial data, but also for expressing new knowledge gained from analyzing the integrated data

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The mouse-human anatomy ontology mapping project

Cited by 14 publications

References 11 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

Preliminary evaluation of the CellFinder literature curation pipeline for gene expression in kidney cells and anatomical parts

The ontology of craniofacial development and malformation for translational craniofacial research

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