Sparse Structure Identification from High-Dimensional Epigenomic Data

描述(由申请人提供)：越来越多的证据支持这样一种假设，即组蛋白修饰的不同组合赋予不同的功能特异性。识别不同的组蛋白修饰模式，并将它们与基因组的功能元件联系起来，在表观遗传学中具有重要意义。高通量的实验技术，如芯片-芯片和芯片-序列，产生了大量的组蛋白修饰数据。然而，目前的实验和计算方法只能在非常有限的程度上探索这些数据。该项目的长期目标是开发新的统计方法，用于从组蛋白修饰数据中识别稀疏结构。施加稀疏性是处理具有噪声信息和小样本大小的超高维数据的理想方法。提出了四个具体的目标，包括(1)识别基因组上新的功能位点；(2)在不同调控元件之间的准确传播；(3)识别调控中组蛋白修饰之间的相互作用；(4)揭示染色质信号的预测DNA基序。为了实现这些目标，将开发新的稀疏统计方法，包括结合变量选择的高维聚类方法、基于稀疏协方差估计的降维分类方法、对多个功能元素的图形模型的联合估计以及多响应多预测回归方法。该项目将通过两名统计学家和一名生物化学家之间的合作进行。建议的方法将通过已公布的数据集和表观基因组路线图项目提供的数据集进行验证和应用，其中一项主要指标涉及这些数据集。 公共卫生相关性：表观遗传修饰，如组蛋白修饰，在调节基因表达方面起着关键作用，在许多疾病中已经观察到异常的表观遗传修饰。对这些修饰进行严格的统计表征和理解可以极大地促进新疗法的发展。

DESCRIPTION (provided by applicant): Evidence is accumulating to support the hypothesis that different combinations of histone modifications confer different functional specificities. Identification of various histone modification patterns and linking them with functional elements of the genome is of great interest in epigenetics. High-throughput experimental techniques, such as ChIP-chip and ChIP-Seq, lead to a rich amount of histone modification data. However, current experimental and computational methods have only been able to explore these data to a very limited extent. This project bears a long-term objective of developing novel statistical methods for sparse structure identification from histone modification data. Imposing sparsity is an ideal way for handling extremely high-dimensional data with noisy information and small sample size. Four specific aims are proposed, including (1) identification of new functional sites on the genome; (2) accurate dissemination between different regulatory elements; (3) identification of the interaction between histone modifications in regulation; (4) uncovering the predictive DNA motifs of the chromatin signature. Novel sparse statistical methods will be developed to achieve these aims, including a high-dimensional clustering method combined with variable selection, a classification method featured by sparse covariance estimation based dimension reduction, a joint estimation of graphical models for multiple functional elements, and a multi-response multi-predictor regression method. This project will be conducted through the collaboration between two statisticians and a biochemist. The proposed methods will be validated through and applied to both published datasets and those provided by the epigenome roadmap project in which one of the PIs is involved. PUBLIC HEALTH RELEVANCE: Epigenetic modifications such as histone modifications play critical roles in regulating gene expression and aberrant epigenetic modifications have been observed in many diseases. A statistically rigorous characterization and understanding of such modifications can greatly facilitate development of new therapeutics.