Statistical Approaches to Integration of Mass Spectral and Genomic Data of Yeast Histone Modifications

新的统计和分析方法将被开发来研究组蛋白修饰在酿酒酵母中的调节作用。真核细胞中的基因活动受转录因子和染色质结构的共同调控。染色质的基本重复单位是核小体，核小体是一种八聚体，含有四种核心组蛋白的两个拷贝。启动子区域的核小体占据通常会阻断转录因子的结合，从而抑制整体基因表达，而组蛋白修饰的作用更为复杂。组蛋白尾部可以以各种方式修饰，包括乙酰化、甲基化、磷酸化和泛素化。即使是组蛋白乙酰化的调节作用，迄今为止最好的特征修饰，仍然没有完全理解。来自酿酒酵母的质谱和全基因组微阵列数据为研究人员评估组蛋白修饰的调节作用提供了新的机会。研究人员将开发统计学方法来识别组蛋白修饰的靶基因和组蛋白修饰的相关DNA序列特征。研究人员还将开发用于预测组蛋白修饰及其相互作用的计算和统计方法。实验数据的噪声和高维性使得许多传统的统计方法失效。如何仅用一小部分信息变量构建预测模型又增加了一层复杂性。将开发新的统计方法来克服这些挑战。 所提出的方法导致一个统计框架，整合多种类型的蛋白质组和基因组数据。在统计学和计算生物学文献中还没有开发和测试这种整合的完整框架。 所提出的方法可以产生创新的方法来分析非常大量的异质数据，建议在系统生物学定量调查的新路线，并为学生提供机会，参与跨学科的研究。

New statistical and analytical methods will be developed to study regulatory role of histone modifications in Saccharomyces cerevisiae. Gene activities in eukaryotic cells are concertedly regulated by transcription factors and chromatin structure. The basic repeating unit of chromatin is the nucleosome, an octamer containing two copies each of four core histone proteins. While nucleosome occupancy in promoter regions typically occludes transcription factor binding, thereby repressing global gene expression, the role of histone modification is more complex. Histone tails can be modified in various ways, including acetylation, methylation, phosphorylation, and ubiquitination. Even the regulatory role of histone acetylation, the best characterized modification to date, is still not fully understood. Mass spectral and genome-wide microarray data from Saccharomyces cerevisiae have offered new opportunities for investigators to evaluate the regulatory effects of histone modifications. The investigators will develop statistical methods for identifying target genes of histone modifications and associated DNA sequence features of histone modifications. The investigators will also develop computational and statistical methods for predicting histone modifications and their interactions. Experimental data are noisy and high dimensional, which renders many tradition statistical methods ineffective. How to build prediction models with only a small set of informative variables adds another layer of complexity. New statistical methods will be developed to surmount the challenges. The proposed methods lead to a statistical framework for integrating multiple types of proteomic and genomic data. A complete framework for such integration has not been developed and tested in the statistics and computational biology literature. The proposed method can produce innovative methodologies for analyzing very large amounts of heterogeneous data, suggest new lines of quantitative investigations in systems biology, and offer opportunities for students to participant in inter-disciplinary research.