A New Web 2.0 Platform for Analyzing Genomics Data with Application to the S

用于分析基因组数据并应用于 S 的新 Web 2.0 平台

• 批准号: 8199175
• 负责人: Wayne Johnson
• 金额: $ 34.84万
• 依托单位: MYATT AND JOHNSON, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8199175
• 关键词: Algorithms; Architecture; Benchmarking; Biological; Biological Markers; Cell Line; Cells; Chemotherapy-Oncologic Procedure; Client; Code; Color; Data; Data Set; Databases; Development; Drops; Drug Delivery Systems; Elements; Gene Expression; Genes; Genomics; Goals; Human; Imagery; Institution; Internet; Journals; Lead; Libraries; Link; Malignant Neoplasms; Marketing; Methods; Modeling; Modification; Molecular; Molecular Profiling; Noise; Online Systems; Ontology; Pathway interactions; Peer Review; Performance; Pharmaceutical Preparations; Phase; Population; Probability; Proteomics; Protocols documentation; PubChem; Publishing; ROC Curve; Research; Resistance; Resources; Screening procedure; Series; Services; Specificity; Statistical Methods; Stream; System; Testing; Translating; Visual; Work; analytical method; animation; anticancer research; base; cancer cell; cancer therapy; cancer type; chemotherapy; cytotoxic; drug candidate; drug discovery; drug efficacy; effective therapy; improved; innovation; insight; interest; member; mortality; novel strategies; parallel computing; patient population; pharmacophore; programs; tau Proteins; visual performance; web services

DESCRIPTION (provided by applicant): A major research focus for gaining insight into the molecular basis for cancer, with the goal of identifying new treatments, is the systematic profiling of cancer cells. It has resulted in large volumes of data that include the compound potency and gene expression data. To more effectively find treatments, the data must be integrated and associated in context sensitive ways. The overall objective of this project is to create a web-based, high-performance visual analytics platform for the exploration of compound activity and molecular profile data, through interactive visualization that supports human analytical reasoning. The platform will demonstrate how visual exploration of cancer cell-based data that include genomics, proteomics, cytotoxic potency, and other research data can be used to identify candidate targets and drugs for cancer chemotherapy, and determine factors that contribute to chemoresistance and sensitivity. The specific aims are the following: 1) Improve the ability to navigate, search, and directly explore large data in highly-coordinated multi-view visualizations. The heatmap will incorporate interaction mechanisms from zooming interfaces and animation. A new visualization for results of the Tau Path statistical method will explore pairs of variables, such as compound-gene, where significant associations have been found in some, but not all of the cell lines. 2) Develop a high-performance analytics server for multicore systems. The server will execute concurrent components for significance tests, clustering, correlation, regression, and association analysis. 3) Extend the new Tau Path statistical method. The extensions include improved identification of the subpopulation, conditional testing to identify subpopulations under which associations are significantly greater than for the whole population, and extension to larger populations, such as all compounds of interest, so as to prioritize drug candidates 4) Integrate resources with the platform to enable findings to be rapidly validated and provide additional insights. The resources include biological pathways systems (e.g. KEGG), the Gene Ontology annotations (GO), biological screening databases (e.g. PubChem), and chemoinformatics services (e.g. OpenTox). The impact of the resulting visual analytics platform will be to more rapidly identify hypotheses that lead to the discovery of new cancer treatments. These hypotheses include compounds that may serve as leads in a drug discovery program seeking new chemotherapy and genes that modulate drug potencies in selected cell lines. The project will lead to a visual analytics platform for cancer research and curated public content. PUBLIC HEALTH RELEVANCE: Cancer is a major cause of mortality throughout the world. Many types of cancers lack effective treatments, and new treatment approaches have the potential to positively impact large patient populations. This project will build a new high-performance visual analytics platform to aid in identifying biomarkers predictive of drug efficacy for optimizing cancer chemotherapy.

描述（由申请人提供）：为了深入了解癌症的分子基础，以识别新的治疗方法，一个主要研究重点是对癌细胞进行系统分析。它产生了大量数据，包括化合物效力和基因表达数据。为了更有效地找到治疗方法，必须以上下文敏感的方式集成和关联数据。该项目的总体目标是创建一个基于网络的高性能可视化分析平台，通过支持人类分析推理的交互式可视化来探索化合物活性和分子概况数据。该平台将展示如何使用基于癌细胞的数据（包括基因组学、蛋白质组学、细胞毒性效力和其他研究数据）的可视化探索来识别癌症化疗的候选靶点和药物，并确定导致化疗耐药性和敏感性的因素。具体目标如下： 1）提高在高度协调的多视图可视化中导航、搜索和直接探索大数据的能力。热图将结合缩放界面和动画的交互机制。 Tau 路径统计方法结果的新可视化将探索变量对，例如复合基因，其中在一些但不是所有细胞系中发现了显着关联。 2) 为多核系统开发高性能分析服务器。服务器将执行并发组件以进行显着性测试、聚类、相关性、回归和关联分析。 3）扩展新的Tau Path统计方法。这些扩展包括改进亚群的识别、条件测试以识别关联性显着大于整个人群的亚群，以及扩展到更大的群，例如所有感兴趣的化合物，以便优先考虑候选药物。 4) 将资源与平台整合，使研究结果能够得到快速验证并提供额外的见解。这些资源包括生物途径系统（例如 KEGG）、基因本体注释（GO）、生物筛选数据库（例如 PubChem）和化学信息学服务（例如 OpenTox）。由此产生的可视化分析平台的影响将是更快地识别导致新癌症治疗方法发现的假设。这些假设包括可作为药物发现计划中的先导化合物，寻求新的化疗药物以及在选定细胞系中调节药物效力的基因。该项目将打造一个用于癌症研究和策划公共内容的可视化分析平台。 公共卫生相关性：癌症是全世界死亡的主要原因。许多类型的癌症缺乏有效的治疗方法，而新的治疗方法有可能对大量患者产生积极影响。该项目将建立一个新的高性能视觉分析平台，以帮助识别预测药物疗效的生物标志物，从而优化癌症化疗。