Computational and Experimental Modeling of Epigenetic DNA Methylation

表观遗传 DNA 甲基化的计算和实验模型

• 批准号: 9188819
• 负责人: Wei Li
• 金额: $ 39.13万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-19 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9188819
• 关键词: Acceleration; Affect; Algorithmic Analysis; Algorithms; Awareness; Bioinformatics; Biological; Case Study; Cellular biology; ChIP-seq; Cloud Computing; Code; Collaborations; Communities; Computer Simulation; Computing Methodologies; DNA Methylation; DNA Modification Methylases; DNMT3a; Data; Data Analyses; Data Set; Development; Disease; Disease model; Enhancers; Epigenetic Process; Experimental Models; Galaxy; Gene Expression; Genes; Genetic Transcription; Genetic Variation; Genome; Gold; Graph; Growth; Hematopoietic Neoplasms; Hematopoietic stem cells; Human; Methods; Methylation; Modeling; Modification; Mus; Mutation; Normal Cell; Nucleic Acid Regulatory Sequences; Nucleotides; Outcome; Play; Protocols documentation; Recurrence; Resolution; Role; Sampling; Scientist; Series; Speed; Statistical Methods; Technology; Testing; Validation; Variant; base; biological systems; bisulfite; bisulfite sequencing; carcinogenesis; cloud based; cost; deep sequencing; density; epigenetic regulation; experimental study; flexibility; follow-up; genome-wide analysis; human disease; improved; in vivo; indexing; insight; next generation; next generation sequencing; novel; personalized diagnostics; personalized medicine; programs; public health relevance; reference genome; success; tool; transcription factor; transcriptome sequencing; web interface; whole genome

DESCRIPTION (provided by applicant): DNA methylation, an epigenetic modification affecting the organization and function of the genome, plays a critical role in both normal development and disease. Bisulfite based conversion of unmethylated Cs to Ts followed by deep sequencing (BS-seq) has emerged as the gold standard to study the genome-wide DNA methylation at single-nucleotide resolution. While progress in next-generation sequencing (NGS) allows increasingly affordable whole-genome BS-seq (WGBS), interpretation of the resulting massive amount of data requires efficient bioinformatics methods. In this proposal, we will develop a series of novel bioinformatics methods for BS-seq data analysis. First, building on the early success of our BSMAP program, we will develop the next generation of bisulfite aligner. We will construct a bisulfite- and SNP-"aware" genome indexing for read mapping with IUPAC code and dynamic Burrows-Wheeler transformation (DBWT). We will also distinguishing CpG methylation from C/T SNP and use GPU hardware acceleration to improve the mapping speed. Second, we will develop a powerful differential methylation analysis algorithm that can take into account both sampling variation from sequencing and biological variation between replicates. We will also introduce a novel metric for evaluating both the statistical and biological significance of differential methylation. This model will have enough power to detect single-CpG resolution differential methylation in low-CpG-density regulatory regions, such as enhancers, with as low as 5-10 fold sequencing depth. Third, we will develop a comprehensive BS-seq data analysis pipeline using the Galaxy web interface and cloud computing. We will integrate all the BS-seq tools we are developing and other public algorithms on a continuous basis according to the emerging needs of the epigenetic community. This pipeline will empower experimental biologists to perform most analyses on their own. These bioinformatics methods will undergo extensive testing and experimental validation by our collaborators. Although focused on CpG methylation using conventional BS-seq in this proposal, our bioinformatics methods can be immediately used in other modified BS-seq protocols, such as oxBS-Seq and TAB-Seq recently developed for 5mC and 5hmC, respectively. Finally, as a case study, we will apply these new methods to unravel the in vivo role of DNA methylation in hematopoietic malignancies. These experiments and follow-up validations will also enable us to improve the efficacy of our bioinformatics methods.

描述（由申请人提供）：DNA 甲基化是一种影响基因组组织和功能的表观遗传修饰，在正常发育和疾病中发挥着关键作用。基于亚硫酸氢盐的未甲基化 Cs 转化为 Ts，然后进行深度测序 (BS-seq) 已成为以单核苷酸分辨率研究全基因组 DNA 甲基化的黄金标准。虽然新一代测序 (NGS) 的进步使得全基因组 BS-seq (WGBS) 的价格越来越便宜，但对由此产生的大量数据的解释需要高效的生物信息学方法。在本提案中，我们将开发一系列用于 BS-seq 数据分析的新型生物信息学方法。首先，在 BSMAP 计划早期成功的基础上，我们将开发下一代亚硫酸氢盐对准器。我们将使用 IUPAC 代码和动态 Burrows-Wheeler 转换 (DBWT) 构建亚硫酸氢盐和 SNP“感知”基因组索引，用于读取映射。我们还将区分CpG甲基化和C/T SNP，并使用GPU硬件加速来提高作图速度。其次，我们将开发一种强大的差异甲基化分析算法，可以同时考虑到 来自测序的采样变异和重复之间的生物变异。我们还将引入一种新的指标来评估差异甲基化的统计和生物学意义。该模型将有足够的能力以低至 5-10 倍的测序深度检测低 CpG 密度调节区域（例如增强子）中的单 CpG 分辨率差异甲基化。第三，我们将使用 Galaxy Web 界面和云计算开发全面的 BS-seq 数据分析流程。我们将根据表观遗传学界​​的新需求，持续集成我们正在开发的所有 BS-seq 工具和其他公共算法。该管道将​​使实验生物学家能够自行进行大多数分析。这些生物信息学方法将由我们的合作者进行广泛的测试和实验验证。尽管本提案重点关注使用传统 BS-seq 的 CpG 甲基化，但我们的生物信息学方法可以立即用于其他修改后的 BS-seq 协议，例如最近分别针对 5mC 和 5hmC 开发的 oxBS-Seq 和 TAB-Seq。最后，作为一个案例研究，我们将应用这些新方法来揭示 DNA 甲基化在造血系统恶性肿瘤中的体内作用。这些实验和后续验证也将使我们能够提高生物信息学方法的效率。