Statistical Methods for Integrated Gene Regulation Analyses

统计方法论的快速发展主要是由于需要描述、建模和分析从各种科学和工程学科产生的复杂的大规模数据集。为了充分利用现有和已有的大量基因调控数据，本研究的目的是(1)开发预测模型方法，结合序列分析、基因表达数据和蛋白质结合数据；(2)开发一个完整的贝叶斯模型，用于从头识别多个相关物种中的顺式调控模块(调节蛋白质和DNA序列之间相互作用的多个序列基序的组合模式)。对于第一个项目，研究了许多现代统计学习方法的使用，如Boosting、随机森林、MARS和BART，用于检测有影响的序列信号和预测蛋白质-DNA相互作用。提出了多水平模型，将协变量中的不确定性纳入统计学习框架，并开发了有效的计算算法来进行推理。第二个项目的统计方面涉及通过耦合随机变量链来建模多个相互作用的随机过程。利用二维动态规划和先进的蒙特卡罗技术，如回火和等能量跳跃，开发了有效的算法来解决基于该模型的贝叶斯推理问题，该研究有望对分子生物学、遗传学和医学科学的各个领域产生直接和即时的影响，而基因调控分析在其中扮演着关键的角色。除了方法学的发展外，还将提供算法和软件，供生物学家使用他们自己的实验数据。这些项目中的许多统计组成部分，如隐马尔可夫模型的耦合和高级蒙特卡罗抽样与动态规划的设计，预计也将对统计学和其他计算科学作出重大贡献。

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.