Computational Tools for Bayesian Mixture Modeling

描述(由申请人提供)： 转录调控是生命系统调节蛋白质水平的重要机制之一。基因表达的失调是许多化学物质毒性作用的基础，而基因表达的变化往往是疾病的可靠标志。了解基因表达调控机制可能会提高我们有效治疗人类疾病和预测环境毒物影响的能力。通过微阵列数据的聚类分析识别共表达的基因组一直是表征基因表达模式的常用方法。目前用于集群分析的计算工具在量化观察到的模式的再现性方面是不够的。我们建议开发计算工具，用于从功能基因组数据中高效和可重复地提取具有生物学意义的模式。 拟议的计算程序将基于贝叶斯无限混合模型。这种方法可以有效利用数据中的信息，并评估观察到的模式的再现性。当共表达的基因簇被用作表征这些基因的共同调节的起点时，对聚类分析中的不确定性的精确建模将特别有益。功能基因组数据和基因组调控序列的联合建模将促进这两种数据类型之间的最佳信息交换。在赠款的R21部分，将验证基于贝叶斯无限混合模型的计算工具。在R33部分，将验证联合表达-序列数据模型，所有计算程序将被合并到用户友好的公共领域软件包中。该软件的主要功能将是一个直观的图形用户界面，以及直接访问、操作和分析不同类型数据的能力。除了新开发的计算方法外，该软件还将纳入其他相关的统计技术，用于关联基因表达和顺式调控元件数据。通过使用该软件，生物医学研究人员将能够对基因表达模式和与这些模式相关的调控元件做出可靠和可重复性的结论。

DESCRIPTION (provided by applicant): Transcriptional regulation is one of the crucial mechanisms used by living systems to regulate protein levels. Disregulation of gene expression underlies toxic effects of many chemicals, and gene expression changes are often reliable markers of a disease. Understanding of gene expression regulation mechanisms is likely to improve our ability to effectively treat human disease and predict effects of environmental toxicants. Identifying groups of co-expressed genes by the cluster analysis of microarrays data has been a commonly used approach for characterizing patterns of gene expression. Currently used computational tools for cluster analysis are inadequate with respect to quantifying reproducibility of observed patterns. We propose to develop computational tools for efficient and reproducible extraction of biologically significant patterns from functional genomics data. Proposed computational procedures will be based on the Bayesian infinite mixture model. This approach allows for efficient use of information in the data and for assessing reproducibility of observed patterns. Precise modeling of uncertainty in cluster analysis will especially be beneficial when clusters of co-expressed genes are used as a starting point in characterizing the co-regulation of such genes. Joint modeling of functional genomics data and genomic regulatory sequences will facilitate optimal information exchange between these two data types. During the R21 portion of the grant, computational tools based on the Bayesian infinite mixture model will be validated. During the R33 portion, joint expression-sequence data models will be validated and all computational procedures will be incorporated in a user-friendly public domain software package. Key features of the software will be an intuitive graphical user interface and ability to directly access, manipulate and analyze diverse types of data. In addition to newly developed computational methods, the software will incorporate other relevant statistical techniques for correlating gene expression and cis-regulatory elements data. By using this software, biomedical researchers will be able to make reliable and reproducible conclusions about gene expression patterns and regulatory elements associate with these patterns.