STRUCTURAL VISUALIZATION IN BIOINFORMATICS

生物信息学中的结构可视化

• 批准号: 7959254
• 负责人: KAY A ROBBINS
• 金额: $ 9.18万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7959254
• 关键词: Bioinformatics; Budgets; Computer Retrieval of Information on Scientific Projects Database; Data; Development; Equilibrium; Funding; Genes; Grant; Housekeeping Gene; Human Resources; Imagery; Institution; Methods; Microarray Analysis; Modeling; Neurosciences Research; Online Systems; Paper; Pattern; Play; Reporting; Research; Research Personnel; Resources; Role; Sampling; Software Tools; Source; Techniques; United States National Institutes of Health; Work; base; cancer microarray; research study; technical report

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. There has been no change in the support of key personnel since the last reporting period. The estimated unobligated balance will not be greater than 25% of the current year's budget. A. Specific Aims: The specific aims of this grant are as stated in the original proposal. B. Progress this year: The majority of the work in the final year of the grant was focused on Specific Aims 1D, 2A and 2B. Specific aim 1D refers to provided platform-wide analysis of microarray data in order to extract patterns. Specific aim 2A and Specific aim 2B refer to the development of web-based software tools to do structural analysis and visualization. We have developed several analysis techniques based on the cumulative distribution functions (CDF) of the microarray samples to detect outliers, unusual expression patterns, and expression patterns that are typical across all experiments. These methods are structural analysis techniques in that they analyze the CDFs against the background of the typical platform-wide CDF. We developed a method of calculating a "gene effect" which is a baseline expression level associated with each gene independent of experimental condition. These effects can only be calculated by looking at a large number of samples. Genes with high gene effect are likely to be housekeeping genes and so gene effect models provide an independent method of determining housekeeping genes. When looking for genes that are highly expressed, the novelty of the expression is determined to some extent by the value of its gene effect. We have shown that gene effects are consistent across platforms. These results have been reported in several technical reports and in 2 papers that are undergoing revision. We are working to show that gene effects play an important role in the analysis of differential expression and are applying these results in several large scale cancer microarray studies.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 自上次报告所述期间以来，关键人员的支助没有变化。估计未支配余额将不超过本年度预算的25%。 A. 具体目标： 这笔赠款的具体目的如原提案所述。 B。 今年的进展： 在赠款的最后一年，大部分工作集中在具体目标1D，2A和2B。具体目标1D是指提供微阵列数据的平台范围分析，以提取模式。具体目标2A和具体目标2B是指开发基于网络的软件工具来进行结构分析和可视化。 我们已经开发了几种分析技术的基础上的累积分布函数（CDF）的微阵列样品检测离群值，不寻常的表达模式，表达模式是典型的所有实验。这些方法是结构分析技术，因为它们在典型的平台范围CDF的背景下分析CDF。我们开发了一种计算“基因效应”的方法，该“基因效应”是独立于实验条件的与每个基因相关的基线表达水平。这些影响只能通过观察大量样本来计算。具有高基因效应的基因可能是管家基因，因此基因效应模型提供了确定管家基因的独立方法。当寻找高表达的基因时，表达的新奇在一定程度上取决于其基因效应的价值。我们已经证明，基因效应在不同平台上是一致的。这些结果已在几份技术报告和两份正在修订的论文中报告。我们正在努力证明基因效应在差异表达分析中起着重要作用，并将这些结果应用于几项大规模癌症微阵列研究。