Context-Sensitive Search of Human Expression Compendia

描述（由申请人提供）：基因表达实验是功能基因组学数据的一个丰富而强大的来源，有成千上万的微阵列和越来越多的高通量RNA测序研究公开可用，大多数询问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控表征的前景，包括在特定组织、细胞系和疾病状态中，并且可以推进对形成人类疾病基础的调控变化的理解和建模。然而，这些数据在很大程度上仍未得到充分利用，因为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，以产生新的假设和指导实验。这种情况类似于搜索引擎出现之前的互联网——生物学研究人员必须先验地知道哪些数据集与她所问的生物学问题有关，反映她感兴趣的组织/细胞谱系特定信号，并准确地测量与她感兴趣的途径相关的基因表达。显然，我们需要一种方法，使生物学研究人员能够利用他们的领域特定知识来指导他们对公共人类表达数据的探索，使他们能够提出假设并指导实验，解决具有挑战性的生物医学问题。这样的系统应该使用户能够有效地探索与他们感兴趣的生物学问题相关的自动识别数据集，利用后生动物的复杂性，包括细胞谱系和疾病特异性信号，并允许研究人员安全地将他们未发表的数据包括在分析中。为了应对这些挑战，该提案描述了一个“谷歌式”的公共搜索引擎，用于使用新颖的搜索算法和利用云计算技术构建的大量基因表达数据。该系统实现了一种新颖的基于查询的上下文敏感算法，用于搜索大型表达纲要，该纲要利用了后生生物的复杂性，包括细胞谱系复杂性和人类表达研究固有的疾病方面。此外，人类样本异质性的挑战将通过开发新的分层学习方法来解决，以预测基于纲要的细胞谱系或组织特异性基因表达，并在每个数据集中识别这些信号。这将使用户能够探索组织特异性表达，也将与搜索算法集成以提高搜索准确性。提出的算法，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，并将通过实验对最佳预测进行测试。这些方法将在一个用户友好的公共搜索系统中实现，该系统将利用云计算提供强大的交互式查询响应，并将使生物学研究人员能够以特定于上下文的、集成的和安全的方式探索已发表的数据集和他们自己的预发表数据集。

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.