Methods for generalized ontology terms enrichment analysis

广义本体术语富集分析方法

• 批准号: 8909186
• 负责人: NIGAM H SHAH
• 金额: $ 61.99万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8909186
• 关键词: Accounting; Address; Adoption; Adverse event; Aging; Bioinformatics; Biological; Biological Process; Blood Coagulation Disorders; Burkitt Lymphoma; Calculi; Cessation of life; Clinical; Clinical Research; Cockayne Syndrome; Comorbidity; Computerized Medical Record; Custom; Data; Data Set; Data Sources; Dependency; Detection; Disease; Drug usage; Electronic Health Record; Etiology; Gene Expression; Gene Expression Profiling; Genes; Genomics; Goals; Hospitals; Incidence; Label; Learning; Length of Stay; Letters; Link; Literature; Methodology; Methods; Molecular; Mutation; Ontology; Output; Patients; Pattern; Pharmaceutical Preparations; Plant Leaves; Proteins; Quality of Care; Research Personnel; Rheumatoid Arthritis; Rothmund-Thomson syndrome; Safety; Scientist; Signal Transduction; Site; Sorting - Cell Movement; Source; Statistical Methods; Systematized Nomenclature of Medicine; Text; Tissue Microarray; Werner Syndrome; Work; base; biomedical ontology; cohort; data mining; genome-wide; glycosylation; health record; high throughput analysis; improved; innovation; insight; interest; novel

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.

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”。“一种常用的方法来深入了解基因表达水平变化的生物学意义，是确定基因本体论（GO）关于特定生物学过程，分子功能或细胞成分的术语是否在作为统计分析的输出生成的基因集的注释中过度或不足。这种分析方法通常被称为“富集分析”，可用于总结和分析基因组以及其他基因组规模数据。 虽然GO一直是富集分析的主要焦点，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（如SNOMED-CT）进行富集分析。例如，通过用疾病术语注释已知的蛋白质突变，Mort等人鉴定了一类疾病-凝血障碍-其与0-连接的糖基化位点的取代中的显著消耗相关。我们可以将富集分析方法应用于其他感兴趣的数据集-例如患者队列。例如，富集分析可以检测在类风湿性关节炎患者中发病率增加的特定共病-一个主题 最近在文献中的讨论，并认为是必不可少的，以提供高质量的护理。我们还可以提出翻译问题;例如，通过确定与某种感兴趣的疾病有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因果关系可能是如何相关的，例如，沃纳综合征、科凯恩综合征、伯基特淋巴瘤和罗斯蒙-汤姆森综合征都是由于它们共享与衰老相关的相同潜在基因而相关的。 尽管被广泛采用，但基于GO的富集分析具有固有的缺点。我们的目标是开发和应用通用富集分析方法-可以使用任何生物医学本体-来分析不同的数据集，例如来自电子医疗记录的患者队列和在基因组分析中被认为重要的基因组。我们建议解决目前的富集分析方法的一些关键缺点，显着扩展用于这种分析的本体，并应用富集分析新的数据源，以询问翻译问题。涵盖我们所有目标的假设是，如果我们成功，富集分析-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询问临床问题。