Statistical Analysis of Epigenomics Data

表观基因组数据的统计分析

• 批准号: 8440116
• 负责人: KIMBERLY D SIEGMUND
• 金额: $ 36.55万
• 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/8440116
• 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）： 这笔赠款的主要目标是开发和评估统计分析方法 DNA 甲基化数据，最终目标是了解 DNA 甲基化与基因型、拷贝数变异和基因表达的联合行为。有多种技术可用于研究 DNA 甲基化（参见 Laird 2010 的综述）。我们专注于 Illumina, Inc. 提供的两个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推动的。具体来说，我们提出以下建议： 具体目标 1：使用技术重复和混合样品开发和评估 Illumina Infinium HumanMmethylation BeadArrays 的预处理方法。 一个。开发快速 Gamma-Gamma 卷积模型来校正背景荧光，并将其与最先进的方法进行比较； b.扩展背景校正方法以按 GC 含量进行分层； c.提供 Bioconductor 中数据预处理的代码。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。一个。开发新颖的过滤器来选择用于聚类分析的基因座，考虑结果、DNA甲基化比例，遵循方差为均值函数的 Beta 分布； b.开发一种使用空间平滑和融合套索进行差异甲基化检测的方法。具体目标 3：开发和评估处理全基因组亚硫酸氢盐测序数据的方法。一个。开发和评估基于模型的新型 SNP 基因型识别程序，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.使用体外甲基化和非甲基化 DNA（SSS.1 处理和 WGA）的对照数据集来校准我们的模型，以了解亚硫酸氢盐转化和测序错误中的已知偏差。我们将在目标 1-3 中开发的方法应用于南加州大学表观基因组中心生成的 DNA 甲基化数据，用于癌症、神经系统疾病和自身免疫性疾病的研究，并公开用户友好的开源计算工具。