Statistical Methods for Integrated Gene Regulation Analyses

统计方法的快速发展主要是由描述，建模和分析从各种科学和工程学科产生的复杂大规模数据集的必要性驱动的。为了充分利用基因调控中现有的和输入的大量数据，本提案的目标是（1）开发预测建模方法，以结合联合收割机序列分析、基因表达数据和蛋白质结合数据;以及（2）建立一个完整的贝叶斯模型，用于从头鉴定顺式调控模块（介导调节蛋白和DNA序列之间相互作用的多个序列基序的组合模式）。在第一个项目中，研究了许多当代统计学习方法的使用，例如boosting，随机森林，MARS和BART，用于检测有影响力的序列信号和预测蛋白质-DNA相互作用。提出了多层次模型，将协变量的不确定性纳入统计学习框架，并开发了有效的计算算法进行推理。第二个项目的统计学方面涉及通过随机变量的耦合链来模拟多个相互作用的随机过程。利用二维动态规划和先进的Monte Carlo技术，如回火和等能量跳跃的高效算法被开发为具有挑战性的贝叶斯推理的建议model.The拟议的研究预计将有直接和即时的影响，在分子生物学，遗传学和医学科学的各个领域，基因调控分析发挥关键作用。除了方法学的发展，算法和软件将提供给生物学家使用自己的实验数据。这些项目中的许多统计组件，如隐马尔可夫模型的耦合和先进的蒙特卡罗抽样与动态规划的设计，预计将大大有助于统计和其他计算科学。

The fast development in statistical methodology is mostly driven by the necessity to describe, model and analyze complex large-scale data sets generated from various scientific and engineering disciplines. In order to make full use of available and incoming large amount of data in gene regulation, this proposal aims (1) to develop predictive modeling approaches to combine sequence analyses, gene expression data, and protein binding data; and (2) to develop a full Bayesian model for de novo identification of cis-regulatory modules (combinatorial patterns of multiple sequence motifs that mediate the interactions between regulatory proteins and DNA sequences) in multiple related species. For the first project, the use of many contemporary statistical learning methods is investigated, such as boosting, random forests, MARS and BART, for detecting influential sequence signals and predicting protein-DNA interactions. Multi-level models are proposed to incorporate the uncertainty in covariates into a statistical learning framework and efficient computational algorithms are developed for the inference. The statistical aspects of the second project involve modeling multiple interacting stochastic processes by coupling chains of random variables. Efficient algorithms that utilize two-dimensional dynamic programming and advanced Monte Carlo techniques such as tempering and equi-energy jumps are developed for the challenging Bayesian inference on the proposed model.The proposed research is expected to have direct and immediate impact on various fields in molecular biology, genetics, and medical sciences, in which gene regulation analyses play critical roles. In addition to methodological development, algorithms and software will be delivered for biologists to use on their own experimental data. Many statistical components in these projects, such as the coupling of hidden Markov models and the design of advanced Monte Carlo sampling with dynamic programming, are expected to contribute significantly to statistics and other computational sciences as well.