Context-Sensitive Search of Human Expression Compendia

人类表达概要的上下文相关搜索

• 批准号: 8024978
• 负责人: OLGA G TROYANSKAYA
• 金额: $ 39.11万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-28 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8024978
• 关键词: Address; Algorithms; Animals; Area; Biological; Biological Assay; Biology; Caenorhabditis elegans; Cell Line; Cell Lineage; Cells; Characteristics; Clinical; Collaborations; Collection; Complex; Computer software; DNA Repair; Data; Data Analyses; Data Collection; Data Set; Databases; Development; Disease; Future; Gene Expression; Gene Expression Regulation; Genes; Genome; Goals; Gold; Heterogeneity; Histocompatibility Testing; Human; Human Biology; Internet; Knowledge; Lead; Learning; Machine Learning; Measures; Medical; Methods; Modeling; Online Systems; Organism; Pathway interactions; Pattern; Performance; Pharmaceutical Preparations; Pharmacotherapy; Publications; Publishing; RNA Sequences; Reaction Time; Research Personnel; Role; Sampling; Sea; Secure; Services; Signal Transduction; Source; Source Code; Specificity; Structure; System; Technology; Testing; Tissue-Specific Gene Expression; Tissues; Update; Work; base; biological systems; disease phenotype; functional genomics; gene function; human disease; human tissue; improved; interest; novel; open source; preference; research study; response; tool; user-friendly

DESCRIPTION (provided by applicant): Gene expression experiments are an abundant and robust source of functional genomics data, with thousands of microarray and a growing number of high throughput RNA sequencing studies publicly available, most interrogating clinical and biological systems relevant to disease. They hold the promise of data-driven characterization of gene function and regulation, including in specific tissues, cell lines, and disease states, and can advance the understanding and modeling of regulatory changes that form the basis of human disease. However, these data remain largely underutilized, as biology researchers do not have effective tools to explore and analyze the entire data collection to generate novel hypotheses and direct experiments. The situation is similar to that of the Internet before the search engines - a biology researcher has to know a priori which datasets pertain to the biological question she is asking, reflect the tissue/cell-lineage specific signals of interest to her, and accurately measure the expression of genes related to her pathways of interest. There is a clear need for methods that will enable biology researchers to use their domain-specific knowledge to direct their exploration of public human expression data, enabling them to generate hypotheses and direct experiments addressing challenging biomedical questions. Such a system should provide users with ability to effectively explore automatically identified datasets relevant to their biological question of interest, leverage metazoan complexity including cell lineage and disease specific signals, and allow the researcher to securely include their unpublished data in the analysis. To address these challenges, this proposal describes a "Google-style" public search engine for large collections of gene expression data built using novel search algorithms and leveraging cloud-computing technologies. This system implements a novel query-based context-sensitive algorithm for search of large expression compendia that exploits the complexity of metazoan organisms, including cell-lineage complexity and disease aspects inherent to human expression studies. Furthermore, the challenge of heterogeneity in human samples will be addressed by developing novel hierarchical learning methods to predict cell-lineage or tissue-specific gene expression based on the compendium and to identify these signals in each dataset. This will enable users to explore tissue-specific expression and also will be integrated with the search algorithm to improve search accuracy. Proposed algorithms, search engine, and user interface will be extensively evaluated in close collaboration with biology researchers, and top predictions will be tested experimentally. These methods will be implemented in a user-friendly public search system that will leverage cloud computing to provide robust interactive query response and will enable biology researchers to explore both published data collections and their own pre-publication datasets in a context-specific, integrated, and secure manner. PUBLIC HEALTH RELEVANCE: We will develop a "Google-style" search engine for massive collections of human gene expression data. Our system will enable researchers to use their domain knowledge to explore the entirety of public human expression data to generate hypotheses and direct experiments addressing a diverse range of challenging biomedical questions. Public availability of our system will advance genome-level understanding of human biology and facilitate development of novel drugs, therapies, and personalized medical treatments.

描述（由申请人提供）：基因表达实验是功能基因组学数据的丰富且可靠的来源，其中成千上万的微阵列和越来越多的高吞吐量RNA测序研究公开可用，大多数与疾病相关的临床和生物系统。它们具有数据驱动的基因功能和调节的表征，包括在特定的组织，细胞系和疾病状态中，并可以提高对构成人类疾病基础的调节变化的理解和建模。但是，由于生物学研究人员没有有效的工具来探索和分析整个数据收集以产生新的假设和直接实验，因此这些数据仍然在很大程度上被充分利用。这种情况与搜索引擎之前的互联网相似 - 一名生物学研究人员必须知道一个与她所问的生物学问题有关的数据集，反映了她与她的感兴趣的特定特定信号，并准确地衡量了与她的感兴趣途径相关的基因的表达。明确的方法需要使生物学研究人员使用特定领域的知识来指导他们对公共人类表达数据的探索，从而使他们能够产生假设并直接实验，以解决具有挑战性的生物医学问题。这样的系统应为用户提供有效探索与其感兴趣的生物学问题相关的自动识别数据集，利用复合谱系和特定于疾病信号的复杂性，并允许研究人员在分析中牢固地包含其未公开数据。为了应对这些挑战，该提案描述了使用新颖的搜索算法构建的大量基因表达数据的“ Google风格”公共搜索引擎，并利用了云计算技术。该系统实现了一种基于查询的新型上下文敏感算法，用于搜索大型表达汇编，该算法利用了后生动物的复杂性，包括细胞分离的复杂性和人类表达研究固有的疾病方面。此外，通过开发新型的层次学习方法来预测基于纲要的细胞循环或组织特异性基因表达，并在每个数据集中识别这些信号，从而解决了人类样本中异质性的挑战。这将使用户能够探索组织特异性的表达，并将与搜索算法集成以提高搜索准确性。拟议的算法，搜索引擎和用户界面将与生物学研究人员密切合作进行广泛评估，并将通过实验测试最佳预测。这些方法将在用户友好的公共搜索系统中实现，该系统将利用云计算来提供强大的交互式查询响应，并使生物学研究人员能够以特定于上下文，集成和安全的方式探索已发布的数据收集及其自己的预公开数据集。 公共卫生相关性：我们将开发一个“ Google Style”搜索引擎，以大量人类基因表达数据收集。我们的系统将使研究人员能够利用其领域知识来探索全部公开人类表达数据，以产生假设并直接实验，以解决各种挑战性的生物医学问题。我们系统的公众可用性将提高基因组级别对人类生物学的了解，并促进新型药物，疗法和个性化医疗治疗的发展。