Statistical Analysis of Epigenomics Data

表观基因组数据的统计分析

• 批准号: 8641410
• 负责人: KIMBERLY D SIEGMUND
• 金额: $ 35.91万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-26 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8641410
• 关键词: Aberrant DNA Methylation; Affect; Alleles; Autoimmune Diseases; Behavior; Bioconductor; California; Cells; Cluster Analysis; Code; Copy Number Polymorphism; DNA; DNA Methylation; Data; Data Set; Detection; Disease Outcome; Embryonic Development; Epigenetic Process; Fluorescence; Gene Expression; Genome; Genotype; Goals; Grant; Guanine + Cytosine Composition; Human; In Vitro; Joints; Lasso; Malignant Neoplasms; Measures; Methods; Methylation; Modeling; Outcome; Peptide Signal Sequences; Phenotype; Process; Reading; Resolution; Role; SNP genotyping; Sample Size; Sampling; Sequence Analysis; Shotguns; Single Nucleotide Polymorphism; Statistical Methods; Technology; Time; Universities; Variant; X Inactivation; base; bisulfite; cell type; computerized data processing; computerized tools; cost effective; epigenetic variation; epigenome; epigenomics; human disease; method development; methylome; nervous system disorder; novel; open source; public health relevance; tool; user-friendly

DESCRIPTION (provided by applicant): The primary objective of this grant is to develop and evaluate methods for the statistical analysis of DNA methylation data, with the ultimate goal of understanding the joint behavior of DNA methylation with genotype, copy number variation, and gene expression. A wide variety of technologies are available for studying DNA methylation (see Laird 2010 for a review). We focus on statistical method development for two different platforms provided by Illumina, Inc. All our aims are motivated by ongoing studies at the University of Southern California Epigenome Center. Specifically, we propose the following: Specific Aim 1: To develop and evaluate preprocessing methods for Illumina's Infinium HumanMethylation BeadArrays using technical replicates and mixed samples. a. To develop a fast Gamma-Gamma convolution model to correct for background fluorescence, and compare it with state-of-the art methods; b. To extend background correction methods to stratify by GC content; c. To provide code for data preprocessing in Bioconductor. Specific Aim 2: To develop and evaluate statistical tools for exploring condition-specific variation in DNA methylation. a. To develop novel filters to select loci for cluster analysis that consider the outcome, proportion DNA methylation, to follow a Beta distribution with variance a function of the mean; b. To develop a method for differential methylation detection using spatial smoothing and the fused lasso. Specific Aim 3: To develop and evaluate methods for processing whole-genome bisulfite-seq data. a. To develop and evaluate a novel model-based SNP genotype caller for bisulfite sequence data. This tool will simultaneously extract DNA methylation content for downstream analysis; b. To calibrate our model for known biases in bisulfite conversion and sequencing errors using control data sets of in vitro methylated and unmethylated DNA (SSS.1-treated and WGA). We will apply the methods developed in Aims 1-3 to DNA methylation data generated at the USC Epigenome center in studies of cancer, neurological disorder, and autoimmune disease, and make user-friendly, open-source computational tools publicly available.

描述（由申请人提供）： 该补助金的主要目的是开发和评估统计分析方法 的DNA甲基化数据，最终目标是了解基因型，拷贝数变异和基因表达的DNA甲基化的联合行为。各种各样的技术可用于研究DNA甲基化（参见Laird 2010的综述）。我们专注于Illumina，Inc.提供的两种不同平台的统计方法开发。我们所有的目标都是由南加州大学表观基因组中心正在进行的研究所激发的。具体而言，我们提出以下建议：具体目标1：使用技术重复和混合样本开发和评估Illumina的Infinium HumanMethylation BeadArrays的预处理方法。 a.开发快速伽马-伽马卷积模型以校正背景荧光，并将其与现有技术方法进行比较; B.将背景校正方法扩展到通过GC含量分层; c.为Bioconductor中的数据预处理提供代码。具体目标2：开发和评估用于探索DNA甲基化条件特异性变异的统计工具。a.开发新的过滤器以选择用于聚类分析的基因座，其考虑结果，DNA甲基化比例，以遵循方差为平均值函数的Beta分布; B.建立一种基于空间平滑和融合lasso的差异甲基化检测方法。具体目标3：开发和评估用于处理全基因组亚硫酸氢盐测序数据的方法。a.开发和评估一种新的基于模型的SNP基因型识别器，用于亚硫酸氢盐序列数据。该工具将同时提取DNA甲基化含量用于下游分析; B.使用体外甲基化和未甲基化DNA（SSS.1处理和WGA）的对照数据集校准我们的模型，以确定亚硫酸氢盐转化和测序错误中的已知偏倚。我们将把目标1-3中开发的方法应用于南加州大学表观基因组中心在癌症、神经系统疾病和自身免疫性疾病研究中生成的DNA甲基化数据，并使用户友好的开源计算工具公开可用。