Linking human diseases to animal models using ontology-based phenotype annotation.
Linking human diseases to animal models using ontology-based phenotype annotation.
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DOI：
10.1371/journal.pbio.1000247
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发表时间：
2009-11
期刊：
影响因子：
9.8
通讯作者：
Lewis SE
中科院分区：
文献类型：
--
作者：
Washington NL;Haendel MA;Mungall CJ;Ashburner M;Westerfield M;Lewis SE
通过使用本体来量化表型之间的相似性的新方法可用于仅基于表型来搜索候选基因、途径成员和人类疾病模型。研究遗传变异和疾病的科学家和临床医生传统上用自然语言描述表型。这些自由文本描述中的相当大的变化对识别人类疾病的候选基因和模型的重要任务构成了障碍，并表明需要一种计算上易于处理的方法来挖掘突变表型的数据资源。在这项研究中，我们测试的假设，疾病表型的本体注释将有助于发现新的基因型和表型的关系内和跨物种。为了使用本体来描述表型，我们使用了一种可靠性-质量（EQ）方法，其中使用来自各种本体的术语来记录受影响的实体（E）及其如何受影响（Q）。使用这种EQ方法，我们注释了在线人类孟德尔遗传（OMIM）中描述的11种基因连锁人类疾病的表型。这些人类注释与来自各种模式生物数据库的突变体的其他基于本体的表型描述一起沿着加载到我们的基于本体的数据库（OBD）中。用这种EQ方法记录的表型可以基于本体中术语的层次结构和注释的频率进行计算比较。我们利用四个相似性指标来比较表型，并开发了一个本体的同源和类似的解剖结构，比较物种之间的表型。使用这些工具，我们证明，我们可以通过记录的表型的相似性，识别相同基因的其他等位基因，信号通路的其他成员，以及跨物种的正交基因和通路成员。我们的结论是，基于EQ的表型注释，结合跨物种本体论，和各种相似性度量可以识别生物学上有意义的基因之间的相似性，通过比较表型单独。这种注释和搜索方法提供了一种新的和有效的手段来识别候选基因和人类疾病的动物模型，这可能缩短识别和理解人类疾病的遗传基础的漫长路径。模式生物如果蝇、小鼠和斑马鱼对于研究基因功能很有用，因为它们很容易在实验室中生长、解剖和遗传操作。通过检查这些生物体中的突变，人们可以识别导致人类疾病的候选基因，并开发模型以更好地了解人类疾病和基因功能。然而，分析的一个基本障碍是缺乏一种计算方法来描述和比较突变动物和人类疾病的表型时，遗传基础是未知的。我们在这里描述了一种新的方法，使用本体来记录和量化表型之间的相似性。我们测试了我们的方法，通过使用注释的突变体表型的一个成员的刺猬信号通路在斑马鱼，以确定其他途径成员具有类似的记录表型。我们还比较了人类疾病的表型，在模式生物突变产生的，并表明，orthopathy和生物学相关的基因，可以通过这种方法来确定。鉴于人类疾病的遗传基础通常是未知的，该方法提供了一种通过计算识别物种内和物种间的相似表型来识别候选基因、途径成员和疾病模型的方法。
A novel method for quantifying the similarity between phenotypes by the use of ontologies can be used to search for candidate genes, pathway members, and human disease models on the basis of phenotypes alone. Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease. Model organisms such as fruit flies, mice, and zebrafish are useful for investigating gene function because they are easy to grow, dissect, and genetically manipulate in the laboratory. By examining mutations in these organisms, one can identify candidate genes that cause disease in humans, and develop models to better understand human disease and gene function. A fundamental roadblock for analysis is, however, the lack of a computational method for describing and comparing phenotypes of mutant animals and of human diseases when the genetic basis is unknown. We describe here a novel method using ontologies to record and quantify the similarity between phenotypes. We tested our method by using the annotated mutant phenotype of one member of the Hedgehog signaling pathway in zebrafish to identify other pathway members with similar recorded phenotypes. We also compared human disease phenotypes to those produced by mutation in model organisms, and show that orthologous and biologically relevant genes can be identified by this method. Given that the genetic basis of human disease is often unknown, this method provides a means for identifying candidate genes, pathway members, and disease models by computationally identifying similar phenotypes within and across species.
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本体论的实际应用在注释和分析大规模的原始小鼠表型数据。
影响因子：
3
作者：
Beck T;Morgan H;Blake A;Wells S;Hancock JM;Mallon AM
通讯作者：
Mallon AM
影响因子：
14.9
作者：
Groth P;Pavlova N;Kalev I;Tonov S;Georgiev G;Pohlenz HD;Weiss B
通讯作者：
Weiss B
影响因子：
10.5
作者：
Caneparo, Luca;Huang, Ya-Lin;Houart, Corinne
通讯作者：
Houart, Corinne
DOI：
10.1002/neu.20161
发表时间：
2005-09-15
期刊：
JOURNAL OF NEUROBIOLOGY
影响因子：
--
作者：
Bovolenta, P
通讯作者：
Bovolenta, P
影响因子：
4.4
作者：
Bergeron, Sadie A.;Milla, Luis A.;Palma, Veronica
通讯作者：
Palma, Veronica