A web platform for integrative genomic analysis in aging

描述（由申请人提供）：高通量技术的最新发展产生了大量的基因组数据，以了解衰老的遗传基础。例如，超过80个微阵列研究已经直接解决了不同模式生物和不同条件下与衰老相关的表达模式。然而，由于缺乏生物信息学资源，无法方便地跨实验室搜索主要阵列信号，因此无法有效利用大量的微阵列数据。我们已经开发了网络数据库基因衰老的Nexus（GAN）免费访问的老年人研究社区查询/分析/可视化各种衰老相关的基因组数据源，特别是微阵列数据。在本提案中，我们将继续GAN的开发。特别是，（1）我们将扩大中央网络数据库的六个物种的老化微阵列数据：人类（H。sapiens）、大鼠（R. norvegicus）、小鼠（M. musculus），“fly”（D. melanogaster）、“worm”（C. elegans）和酵母（S. cerevisiae）。(2)我们将实现一套新的工具，用于跨平台的微阵列数据集成和分析，并实现文献中报道的有用方法。(3)我们将结合其他基因组数据源，如生物学途径，转录调控，蛋白质-蛋白质相互作用，基因组序列和文献知识。通过整合这些数据，用户将能够更好地理解和解释来自阵列分析的结果，将未知基因分配给衰老相关途径，并预测转录调控。GAN可以帮助启动下一阶段评估不同生物体组织中衰老相关基因表达的可能共性。 GAN平台的构建和大量公共衰老基因组数据的相关分析可能构成衰老研究中最大的生物信息学事业之一。GAN可以帮助启动下一阶段评估不同生物体组织中与衰老相关的基因表达的可能共性，并促进对衰老的系统理解。

DESCRIPTION (provided by applicant): The recent development of high-throughput technologies generated enormous genomic data to understand the genetic basis of aging. For example, more than eighty microarray studies have directly addressed aging-related expression patterns in diverse model organisms and under different conditions. However, efficient exploitation of this vast amount of microarray data is frustrated by the lack of bioinformatics resources to enable convenient cross-laboratory searches of primary array signals. We have develop the web-database Gene Aging Nexus (GAN) freely accessible to the biogerontological-geriatric research community to query/analyze/visualize various aging-related genomic data sources, in particular, microarray data. In this proposal, we will continue the development of GAN. In particular, (1) We will expand the central web database for aging microarray data of six species: human (H. sapiens), rat (R. norvegicus), mouse (M. musculus), `fly' (D. melanogaster), `worm' (C. elegans), and yeast (S. cerevisiae). (2) We will implement a set of novel tools for cross-platform microarray data integration and analysis, and also implement useful methods reported in the literature. (3) We will incorporate other genomic data sources, such as biological pathways, transcription regulation, protein-protein interactions, genome sequences, and literature knowledge. By integrating such data, users will be able to better understand and interpret the results derived from array analysis, to assign unknown genes to aging-related pathways, and to predict transcriptional regulation. GAN could help launch the next phase of evaluating possible universals in aging-related gene expression in tissues of diverse organisms. The construction of the GAN platform and the associated analysis of a large number of public aging genomic data could constitute one of the largest bioinformatics undertakings in aging research. GAN could help launch the next phase of evaluating possible universals in aging-related gene expression in tissues of diverse organisms, and facilitates the systems understanding of aging.