Computational tools for comparative phenomics: the role and promise of ontologies

Gkoutos, Georgios V.; Schofield, Paul N.; Hoehndorf, Robert

doi:10.1007/s00335-012-9404-4

Cited by 19 publications

(15 citation statements)

References 78 publications

(99 reference statements)

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“…Due to the importance of integrating species-specific phenotype ontologies for biomedical research [50], several methods have been developed that specifically focus on the integration of phenotype ontologies. For example, PhenoHM [51] uses the Unified Medical Language System (UMLS) MetaMap service [52] to map classes from MP to UMLS concepts describing disorders and phenotypes.…”

Section: Discussionmentioning

confidence: 99%

Integrating phenotype ontologies with PhenomeNET

et al. 2017

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BackgroundIntegration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.ResultsHere, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.ConclusionsPhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0167-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Integrating phenotype ontologies with PhenomeNET

et al. 2017

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“…This strategy should elucidate the relationship between the clinical phenome (symptoms and signs) and integrate information from multi-scale omics data such as metabolomics, proteomics, transcriptomics, and genomics, which would yield more information on a biological process than the analysis of a single type of data [76] . However, the gathering of clinical phenotype data likely presents a greater challenge than high-throughput sequencing projects, due to the range of phenotype measurements and the complexity of the data [77] . The use of ontologies was proposed as an approach to semantic standardization [78] for semantically categorizing phenodeviance [79] such as Semantic Web technologies [80] , Systems Biology Markup Language [81] , and Web Ontology Language (OWL) [82,83] .…”

Section: Discussionmentioning

confidence: 99%

Diverse array-designed modes of combination therapies in Fangjiomics

Wang

2015

Acta Pharmacol Sin

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In line with the complexity of disease networks, diverse combination therapies have been demonstrated potential in the treatment of different patients with complex diseases in a personal combination profile. However, the identification of rational, compatible and effective drug combinations remains an ongoing challenge. Based on a holistic theory integrated with reductionism, Fangjiomics systematically develops multiple modes of array-designed combination therapies. We define diverse "magic shotgun" vertical, horizontal, focusing, siege and dynamic arrays according to different spatiotemporal distributions of hits on targets, pathways and networks. Through these multiple adaptive modes for treating complex diseases, Fangjiomics may help to identify rational drug combinations with synergistic or additive efficacy but reduced adverse side effects that reverse complex diseases by reconstructing or rewiring multiple targets, pathways and networks. Such a novel paradigm for combination therapies may allow us to achieve more precise treatments by developing phenotype-driven quantitative multi-scale modeling for rational drug combinations.

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“…However, such comparisons have historically relied only on the expertise of researchers, and tended to make use of organism-specific language to describe phenotypes. The development of ontologies, formal hierarchies of descriptive annotations [28, 29 and 30], now allows researchers to find new human disease models by directly searching for homologous phenotypes using phenotype ontologies, an approach easily scalable to large phenotypic datasets. Multiple ontologies [31, 32 and 33] have been developed, enabling systematic analyses of phenotypes in a way that is descriptive, robust, programmatically accessible, and extensible across species.…”

Section: Finding Models Through Phenotype Comparisonmentioning

confidence: 99%

Applications of comparative evolution to human disease genetics

McWhite

Liebeskind

Marcotte

2015

Current Opinion in Genetics & Development

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Direct comparison of human diseases with model phenotypes allows exploration of key areas of human biology which are often inaccessible for practical or ethical reasons. We review recent developments in comparative evolutionary approaches for finding models for genetic disease, including high-throughput generation of gene/phenotype relationship data, the linking of orthologous genes and phenotypes across species, and statistical methods for linking human diseases to model phenotypes.

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Computational tools for comparative phenomics: the role and promise of ontologies

Cited by 19 publications

References 78 publications

Integrating phenotype ontologies with PhenomeNET

Integrating phenotype ontologies with PhenomeNET

Diverse array-designed modes of combination therapies in Fangjiomics

Applications of comparative evolution to human disease genetics

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