PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics

Kahraman, Abdullah; Avramov, Andrey; Nashev, Lyubomir G.; Popov, Dimitar; Ternes, Rainer; Pohlenz, Hans-Dieter; Weiß, Bertram

doi:10.1093/bioinformatics/bti010

Cited by 64 publications

(37 citation statements)

References 13 publications

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“…Considerable resources have been expended on comparative genomics, but there is also a justification for 'comparative phenomics' where phenotypic databases on different model organisms are available and comparable in a single database and the phenotypes of orthologous genes from different organisms can be directly compared. This was the motivation between the development of PhenomicDB (Kahraman et al, 2005). Although standardisation of phenotype descriptions is a major hurdle to the success of such an endeavour, Kahraman et al (2005) use the example that a gene associated with cancer in mammals which also exhibits a proliferation phenotype in lower organisms such as yeasts may warrant further investigation.…”

Section: Challenges and Future Researchmentioning

confidence: 99%

“…This was the motivation between the development of PhenomicDB (Kahraman et al, 2005). Although standardisation of phenotype descriptions is a major hurdle to the success of such an endeavour, Kahraman et al (2005) use the example that a gene associated with cancer in mammals which also exhibits a proliferation phenotype in lower organisms such as yeasts may warrant further investigation. Fraught with less complications would be the development of freely available phenotypic and genotypic databases within species, although the costs incurred in acquiring such phenotypes and the commercial sensitivities associated with them may hinder such an initiative, in the near future at least.…”

Section: Challenges and Future Researchmentioning

confidence: 99%

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The integration of ‘omic’ disciplines and systems biology in cattle breeding

Berry

Meade

Mullen

et al. 2011

Animal

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Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists -and they will also be discussed.

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Section: Challenges and Future Researchmentioning

confidence: 99%

Section: Challenges and Future Researchmentioning

confidence: 99%

The integration of ‘omic’ disciplines and systems biology in cattle breeding

Berry

Meade

Mullen

et al. 2011

Animal

View full text Add to dashboard Cite

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“…Being able to analyse such ancestral data from both genomic and transcriptomic point of view, using a phylomics approach, will lead to a more biologically informative data. The field of phenomics, transcriptomics, genomics, methylomics, phylomics, interactomics and so on (Harris et al, 2003;Drysdale and Crosby, 2005;Kahraman et al, 2005) has opened avenues and can increase our ability to characterize changes in different cancers through 'multi-omics' analysis. The integration of these multiple approaches will be far more efficient and effective in identifying, refining and validating cancer genes, their participating pathways and biological impact on the cancer phenotypes (Chin et al, 2006;Yao et al, 2006).…”

Section: Future Of 'Omics'-ology and Ptenmentioning

confidence: 99%

Genetic and phenotypic heterogeneity in the PTEN hamartoma tumour syndrome

2008

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“…Some efforts have been made in the integration and standardization of this data for sharing purposes. For example, the PhenomicDB [53] database provides a single portal for heterogeneous phenotypic information from a number of different model organisms and humans. It contains over 15,000 distinct phenotypic terms and 120,000 genotypes for the mouse and human species.…”

Section: Integration Of Clinical and Genomic Databasesmentioning

confidence: 99%

Clinical Ontologies for Discovery Applications

Lussier

Bodenreider

Semantic Web

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Abstract:The recent achievements in the Human Genome Project have made possible a high-throughput "systems approach" for accelerating bioinformatics research. In addition, the NIH Whole Genome Association Studies will soon supply abundant clinical data annotated to clinical ontologies for mining. The elucidation of the molecular underpinnings of human diseases will require the use of genomic and ontology-anchored clinical databases. The objective of this chapter is to provide the background required to conduct biological discovery research with clinical ontologies. We first provide a description of the complexity of clinical information and the main characteristics of various clinical ontologies. The second section illustrates several methods used to integrate clinical ontologies and therefore databases annotated with heterogeneous standards. Finally the third section reviews a few genome-wide studies that leverage clinical ontologies. We conclude with the future opportunities and challenges offered by the Semantic Web and clinical ontologies for clinical data integration and mining. Discovery research faces the challenge of generating novel tools to help collect, access, integrate, organize and manage clinical information and enable genome wide analyses to associate phenotypic information with genomic data at different scales of biology. Collaborations between bioinformaticians and clinical informaticians are poised to leverage the Semantic Web.

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PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics

Abstract: database model, semantic mapping table.

Cited by 64 publications

References 13 publications

The integration of ‘omic’ disciplines and systems biology in cattle breeding

The integration of ‘omic’ disciplines and systems biology in cattle breeding

Genetic and phenotypic heterogeneity in the PTEN hamartoma tumour syndrome

Clinical Ontologies for Discovery Applications

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