Methods for generalized ontology terms enrichment analysis

描述(由申请人提供)： 对高通量数据的分析，如基因表达分析，通常会产生一长串“重要基因”。为了深入了解基因表达水平变化的生物学意义，一种常用的方法是确定关于特定生物过程、分子功能或细胞成分的基因本体论(GO)术语在作为统计分析输出生成的基因集的注释中是过高还是过低。这种分析方法通常被称为“浓缩分析”，可以用来总结和描述一个基因组以及其他基因组规模的数据。 虽然GO一直是浓缩分析的主要焦点，但我们可以使用生物医学领域中可用的任何本体进行相同类型的分析。我们可以使用疾病本体进行浓缩分析--例如SNOMED-CT。例如，通过用疾病术语注释已知的蛋白质突变，Mort等人。确定了一类疾病-凝血障碍-与O-连接的糖基化位点的替代显著耗尽有关。我们可以将丰富分析方法应用于其他感兴趣的数据集--例如患者队列。例如，浓缩分析可能会检测到类风湿性关节炎患者中发病率增加的特定并存--这是一个主题 最近文献中的讨论，并被认为是提供高质量护理的关键。我们也可以提出翻译问题；例如，通过确定与特定感兴趣的疾病相关的基因的其他疾病关联，我们可以深入了解看似无关的疾病的病因可能是如何相关的，例如Werner综合征、Cockayne综合征、Burkitt淋巴瘤和Rothmund-Thomson综合征都与它们共享与衰老相关的相同潜在基因这一事实有关。 尽管GO被广泛采用，但基于GO的富集度分析存在固有的缺陷。我们的目标是开发和应用通用的浓缩分析方法-可以使用任何生物医学本体-来分析不同的数据集，例如来自电子病历的患者队列和被认为在基因组分析中具有重要意义的基因集。我们建议解决当前浓缩分析方法的一些关键缺陷，显著扩展用于此类分析的本体，并将浓缩分析应用于新的数据源以提出翻译问题。贯穿我们所有目标的假设是，如果我们成功，浓缩分析-生物信息学科学家广泛使用的分析方法-将不仅仅是GO，而且该方法将扩展到提出临床问题。

DESCRIPTION (provided by applicant): The analysis of high-throughput data such as gene-expression assays usually results in a long list of "significant genes." One commonly used method to gain insight into the biological significance of alterations in gene expression levels is to determine whether the Gene Ontology (GO) terms about specific biological processes, molecular functions, or cellular components are over- or under-represented in the annotations of the gene sets generated as the output of the statistical analysis. This analysis method often referred to as "enrichment analysis," can be used to summarize and profile a gene-set, as well as other genome scale data. While the GO has been the principal focus for enrichment analysis, we can carry out the same sort of profiling using any ontology available in the biomedical domain. We can perform enrichment analysis using disease ontologies - such as SNOMED-CT. For example, by annotating known protein mutations with disease terms, Mort et al. identified a class of diseases - blood coagulation disorders - that are associated with a significant depletion in substitutions a O-linked glycosylation sites. We can apply the enrichment analysis methodology to other datasets of interest - such as patient cohorts. For example, enrichment analysis might detect specific co-morbidities that have an increased incidence in rheumatoid arthritis patients - a topic of recent discussion in the literature and considered essential to provide high quality care. We can also ask translational questions; for example, by identifying other disease associations for the genes involved in a certain disease of interest we can gain insight into how the causation of seemingly unrelated diseases might be related, e.g., Werner's syndrome, Cockayne syndrome, Burkitt's lymphoma, and Rothmund-Thomson Syndrome are all related by the fact that they share the same underlying gene related to aging. Despite widespread adoption, GO-based enrichment analysis has intrinsic drawbacks. Our goal is to develop and apply general enrichment analysis methods - that can use any biomedical ontology - to profile diverse datasets, such as patient cohorts from electronic medical records and sets of genes deemed significant in genomic analyses. We propose to address some of the key shortcomings of the current enrichment-analysis methods, to expand significantly the ontologies that are used for such analyses, and to apply enrichment analysis on novel data sources for asking translational questions. The hypothesis spanning all our aims is that if we are successful, enrichment analysis - a widely used analysis approach by bioinformatics scientists - will be possible with more than just the GO and the method will be extended to ask clinical questions.