MacMillan: Project 3; Technology Development and Research Project #3: Bioinformatics (White)

翻译后摘要：技术和研究开发项目#3：生物信息学负责 开发工具和方法，用于分析来自其他技术项目的数据， 这个提议。这利用了UTSW和UCSC的数据分析和信息学优势， 为天然产物和植物的大型文库提供大规模的分子和化学注释。 我们的交付成果将是一个数据和数据分析管道，将有助于定义细胞学特征， 化学实体，将化学实体映射到它们中断的信号通路，帮助识别 正常和患病的生物网络，天然产品和植物药的目标，并帮助表明 这些化合物的潜在治疗/饮食应用。这将通过使用一系列 数据分析方法包括：1）通过以下方法对天然产物级分和植物活性进行聚类分析： 仿射传播聚类（APC）算法。该算法可以应用于由 细胞学分析、无偏代谢组学和融合。2)利用欧氏距离分布 为FUSION中的所有化学和遗传干扰源生成相似性矩阵，以获得“有罪 关联相关1)开发基于系统发生树算法的直观数据可视化工具。 这些工具的开发不仅是为了可视化TRD#1和TRD#2生成的数据， 其他大规模的研究天然产物作用机制的方法。的最终目标 生物信息学的组成部分，这一赠款是推动产生的生物活性的假设， 评估化学品，并将此信息提供给科学界。

Abstract: Technology and Research Development Project #3: Bioinformatics is responsible for the development of tools and methods for the analysis of data from the other technology projects that are part of this proposal. This takes advantage of the data analytics and informatics strengths of UTSW and UCSC to help produce a broad-scale molecular and chemical annotation of large libraries of natural products and botanicals. Our deliverable will be a data and data analytics pipeline that will help define a cytological signature to chemical entities, map chemical entities to signaling pathways they interrupt, help identify principal nodes in normal and diseased biological networks that natural products and botanicals target, and help indicate potential therapeutic/dietary applications for these compounds. This will be accomplished by using a series of data analysis methods including 1) Clustering analysis of natural products fractions and botanical activity by the affinity propagation clustering (APC) algorithm. This algorithm can be applied to data features generated by cytological profiling, unbiased metabolomics and FUSION. 2) Use of Euclidean distance distribution to generate a similarity matrix for all of the chemical and genetic perturbagens in FUSION to obtain “guilt by association correlations. 1) Develop tools for intuitive data visualization based on phylogenetic tree algorithms. These tools will be developed for visualization of not only the data generated by TRD#1 and TRD#2, but by other large scale methods looking at natural product mechanisms of action. The ultimate goal of the bioinformatics component of this grant is to drive the generation of hypotheses on the biological activity of the chemical being evaluated and provide this information to the scientific community.