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

麦克米伦：项目 3；

• 批准号: 8881715
• 负责人: MICHAEL A. WHITE
• 金额: $ 6.29万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8881715
• 关键词: Acute; Affinity; Algorithms; Area; Bioinformatics; Biological; Biological Assay; Biological Factors; Biology; Botanicals; Cancer cell line; Cell Line; Chemicals; Cluster Analysis; Communities; DNA; Data; Data Analyses; Data Correlations; Data Set; Databases; Development; Diet; Disease; Dose; Euclidean Space; Experimental Designs; Gene Expression; Generations; Genes; Genetic; Goals; Grant; Guilt; Imagery; Individual; Informatics; Lead; Libraries; Link; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Measures; Messenger RNA; Metadata; Methods; Microscopy; Modeling; Molecular; Molecular Profiling; Non-Small-Cell Lung Carcinoma; Ontology; Output; Pathway interactions; Patient Rights; Phenotype; Phylogenetic Analysis; Process; Reading; Reporter; Reporter Genes; Research Project Grants; Safety; Series; Signal Pathway; Source; Technology; The Cancer Genome Atlas; Therapeutic; Time; Trees; Validation; Variant; abstracting; base; chemical genetics; computerized data processing; cytotoxicity; data visualization; disorder subtype; evidence base; file format; high throughput technology; innovation; insight; interest; metabolomics; molecular scale; research and development; research study; screening; technology development; tool; tool development; transcriptomics; tumor

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.

摘要：技术与研究开发项目＃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的一部分 这个建议。这利用了UTSW和UCSC的数据分析和信息优势来帮助 生成大规模的天然产物和植物学文库的大规模分子注释。 我们可交付的将是数据和数据分析管道，将有助于将细胞学签名定义为 化学实体，将化学实体映射到它们中断的信号通路，有助于识别主节点 天然产物和植物药的正常生物网络和植物网络的目标，并有助于表明 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）天然产物分数的聚类分析和植物活性 亲和力传播聚类（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）使用欧几里得距离分布 在融合中为所有化学和遗传性脑外urli的相似性矩阵产生相似性矩阵，以获得“ 关联相关性。 1）开发基于系统发育算法的直观数据可视化的工具。 这些工具将开发用于可视化不仅由TRD＃1和TRD＃2生成的数据，还可以通过 其他大规模的方法，着眼于自然产品的作用机理。最终目标 该赠款的生物信息学部分是推动有关生物活性的假设的产生 评估化学物质并将这些信息提供给科学界。