PhenoDigm: analyzing curated annotations to associate animal models with human diseases.
PhenoDigm: analyzing curated annotations to associate animal models with human diseases.
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DOI：
10.1093/database/bat025
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Mungall C
中科院分区：
文献类型：
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作者：
Smedley D;Oellrich A;Köhler S;Ruef B;Sanger Mouse Genetics Project;Westerfield M;Robinson P;Lewis S;Mungall C
研究模式生物的最终目标是将所学到的知识转化为关于正常人类生物学和疾病的有用知识，以促进疾病的治疗和早期筛查。基因组技术的最新进展允许快速生成具有一系列靶向基因型的模型以及通过高通量表型分析来表征它们。随着表型数据的丰富，只有系统的分析才能从这些数据中得出有效的结论，并将其转移到人类疾病中。由于数据量大，自动化方法更可取，可以对数据进行可靠的分析，并提供有关可能的基因-疾病关联的证据。在这里，我们提出了疾病基因和模型的表型比较（PhenoDigm），作为一种自动化的方法，通过分析表型信息提供有关基因-疾病关联的证据。PhenoDigm整合了来自各种模式生物的数据，同时使用几种中间评分方法来识别只有强有力的数据支持的人类遗传疾病候选基因。我们展示了自动化评估的结果以及支持PhenoDigm有效性的手动评估示例。此外，我们提供指导如何浏览数据与PhenoDigm的Web界面，并说明其在支持研究的有用性。数据库URL：http://www.sanger.ac.uk/resources/databases/phenodigm
The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene–disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene–disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly data-supported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm’s web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm