Annotation-based meta-analysis of microarray experiments

基于注释的微阵列实验荟萃分析

• 批准号: 7595177
• 负责人: Christian J. Stoeckert
• 金额: $ 18.5万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-28 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7595177
• 关键词: Address; Automated Annotation; Biological Assay; Blood; Caring; Computer software; Data; Data Analyses; Disease; Ensure; Evaluation; Gene Expression; Gene Expression Profile; Heart; Knowledge; Lung; Malignant Neoplasms; Manuals; Measures; Meta-Analysis; Methods; Microarray Analysis; Molecular; Molecular Profiling; Ontology; Organism; Procedures; Quality Control; Risk; Severities; Sleep Disorders; Source; Structure; Testing; Text; Work; base; data integration; foot; high throughput technology; improved; insight; interest; open source; repository; research study; software development

DESCRIPTION (provided by applicant): Meta-analyses of microarray experiments require the usage of meta-data annotations, however these annotations are often a barrier because they usually entail significant manual evaluation. Combining data from assays in different experiments for analysis is challenging as it requires suitably transforming these data so that they are on equal footing. In particular, extra care needs to be taken to ensure that the results are not driven by confounding factors but rather by biologically-relevant ones. Consideration of annotations can improve meta-analyses through guiding choice of experiments, assays (within each experiment), data transformations, and analysis procedures. We propose to develop software that will extract annotations for use in meta-data analyses and which should motivate better annotation of microarray experiments using established standards. Standardized experiment annotations can be generated using the MGED Ontology (MO) and can be extracted from files based on the MAGE (MicroArray Gene Expression) standard that have information covering the MIAME (Minimal Information About a Microarray Experiment) checklist. Standardized MO-based assay annotations are also available from MAGE based files, but further relevant information (such as treatment descriptions) also resides in free-text annotation fields in these files. Thus, in order to get fully standardized annotation for assays, more work is needed than just extracting the MO terms associated with them. Our first aim is to develop software that will extract annotations either directly from appropriate MAGE fields or parse them as needed from free-text descriptions. The annotations will be used to generate dissimilarity measures between experiments and assays based on shared annotation. The software will need to recognize synonymous terms when terms from different experiments or assays for the same annotation (e.g., organism part) are drawn from different sources. Our second aim is to develop software to compute with annotations based on these measures, e.g. to find experiments or assays related to a query experiment/assay, or to cluster experiments or assays based on their annotation (as opposed to clustering based on gene expression profiles). These clusters can be used as the basis for organizing experiments/assays and performing meta-analyses of gene expression profiles. Additionally, annotation-based dissimilarity measures can be used to evaluate existing (gene expression profile based) clusters of experiments or assays and the annotation itself can be input into analyses aimed at identifying over-enriched terms.Narrative: Microarray technology has been used to understand the molecular basis of diseases including heart, lung, blood, and sleep disorders and cancer. We will develop software applications to demonstrate the feasibility and utility of using microarray annotations to drive meta-analyses and quality control (QC) of experiments. The applications will be tested on files from the public repository ArrayExpress but are meant to work with appropriate files from any source. To the best of our knowledge, the usage of annotations for this purpose has not been explored previously and therefore the risk of the proposal is that it is exploring uncharted territory and the severity and type of pitfalls are unclear. The potential high impact of the proposed applications to the bench biologist is the ability to generate additional insights from their microarray results. Moreover, these methods would eventually be extensible to annotated experiments employing high-throughput technologies other than microarrays. This can further facilitate integration of data of different types to address a scientific question of interest. These benefits may encourage better annotation of experiments and use of standards.

描述（由申请人提供）：微阵列实验的荟萃分析需要使用元数据注释，然而这些注释通常是一个障碍，因为它们通常需要大量的人工评估。将来自不同实验中的测定的数据组合用于分析是具有挑战性的，因为它需要适当地转换这些数据，使得它们处于平等的地位。特别是，需要格外小心，以确保结果不是由混杂因素驱动的，而是由生物相关因素驱动的。注释的考虑可以通过指导实验的选择、测定（在每个实验内）、数据转换和分析程序来改进荟萃分析。我们建议开发软件，将提取注释用于元数据分析，并应激励更好的注释微阵列实验使用既定的标准。标准化的实验注释可以使用MGED本体（MO）生成，并且可以基于法师（微阵列基因表达）标准从具有覆盖MIAME（关于微阵列实验的最小信息）检查表的信息的文件中提取。基于法师的文件中也提供了标准化的基于MO的检测注释，但进一步的相关信息（如治疗描述）也存在于这些文件的自由文本注释字段中。因此，为了得到完全标准化的测定注释，需要比仅仅提取与它们相关联的MO术语更多的工作。我们的第一个目标是开发软件，将提取注释直接从适当的法师字段或解析他们需要从自由文本描述。注释将用于基于共享注释生成实验和测定之间的相异性度量。当来自不同实验或测定的术语用于相同注释（例如，生物体部分）来自不同来源。我们的第二个目标是开发基于这些测量的注释计算的软件，例如，找到与查询实验/测定相关的实验或测定，或者基于它们的注释对实验或测定进行聚类（与基于基因表达谱的聚类相反）。这些聚类可以用作组织实验/测定和进行基因表达谱的荟萃分析的基础。此外，注释为基础的相异性措施可用于评估现有的（基因表达谱为基础的）集群的实验或检测和注释本身可以输入到分析，旨在确定过度富集terms.Narrative：微阵列技术已被用来了解疾病的分子基础，包括心脏，肺，血液，睡眠障碍和癌症。我们将开发软件应用程序来证明使用微阵列注释来驱动实验的荟萃分析和质量控制（QC）的可行性和实用性。这些应用程序将在来自公共存储库ArrayExpress的文件上进行测试，但也意味着可以使用来自任何来源的适当文件。据我们所知，以前没有探讨过为此目的使用注释，因此该提案的风险在于它正在探索未知领域，缺陷的严重性和类型尚不清楚。所提出的应用程序对实验室生物学家的潜在高影响是能够从他们的微阵列结果中产生额外的见解。此外，这些方法最终将可扩展到采用高通量技术而不是微阵列的注释实验。这可以进一步促进不同类型数据的整合，以解决感兴趣的科学问题。这些好处可能会鼓励更好地注释实验和使用标准。

项目成果

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments.

• DOI: 10.1093/database/bar045
• 发表时间: 2011
• 期刊: Database : the journal of biological databases and curation
• 作者: Zheng J;Stoyanovich J;Manduchi E;Liu J;Stoeckert CJ Jr
• 通讯作者: Stoeckert CJ Jr