Joint Modeling of Genomic and Functional Genomic Data

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。许多化学物质的毒性作用反映在基因表达的失调中，基因表达的变化通常是疾病的可靠标志。了解基因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来识别共表达基因组已经成为表征基因表达模式的常用方法。这种分析的结果经常被用作解剖驱动共表达的调节机制的起点。这种方法的两个例子是鉴定这种共表达基因的顺式调控区中的共同推定调控基序，以及通过使用基于微阵列的比较基因组杂交将共表达模式与揭示的基因组事件相关联。我们建议开发新的数学模型和相应的计算工具，通过联合建模基因组和功能基因组数据，有效和可重复地提取相关的表达模式，相关的调控基序和基因组畸变。拟议的工作将解决开发一个实用的数学框架，用于综合分析不同类型的基因组和功能基因组数据的问题。建议的计算程序将基于特定上下文的贝叶斯无限混合模型。基因组和功能基因组数据的联合建模将促进各种数据类型之间的最佳信息交换。 建议的模型将通过分析合成和真实世界的数据集进行验证。对应的计算机 计划将免费分发给生物医学界。通过使用这些程序，生物医学研究人员将能够对基因表达模式和相关的调控机制做出可靠和可重复的结论。

DESCRIPTION (provided by applicant): Transcriptional regulation is one of the crucial mechanisms used by living systems to regulate protein levels. Toxic effects of many chemicals are reflected in the dysregulation of gene expression, and gene expression changes are often reliable markers of a disease. Understanding the mechanisms of gene regulation 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 microarray data has been a commonly used approach for characterizing patterns of gene expression. Results of such analyses have often been used as a starting point for dissecting the regulatory mechanism driving the co-expression. Two examples of such approaches are identifying common putative regulatory motifs in cis-regulatory regions of such co-expressed genes and correlating patterns of co-expression with genomic events uncovered through the use of microarray based Comparative Genomic Hybridization. We propose to develop novel mathematical models and corresponding computational tools for efficient and reproducible extraction of relevant expression patterns, related regulatory motifs and genomic aberrations by jointly modeling genomic and functional genomic data. The proposed work will address the issue of developing a practical mathematical framework for an integrated analysis of different types of genomic and functional genomic data. Proposed computational procedures will be based on the context-specific Bayesian infinite mixture model. Joint modeling of genomic and functional genomic data will facilitate optimal information exchange between various data types. Proposed models will be validated by analyzing synthetic and real-world datasets. Corresponding computer programs will be freely distributed to the biomedical community. By using these programs, biomedical researchers will be able to make reliable and reproducible conclusions about gene expression patterns and associated regulatory mechanisms.