Computational and Experimental Modeling of Epigenetic DNA Methylation

项目总结/摘要 DNA甲基化是一种影响基因组结构和功能的表观遗传修饰， 在正常发育和疾病中的重要作用。基于亚硫酸氢盐将未甲基化的Cs转化为Ts 随后进行深度测序（BS-seq）已成为研究全基因组DNA的金标准 甲基化在单核苷酸分辨率。虽然下一代测序（NGS）的进展允许 越来越负担得起的全基因组BS-seq（WGBS），解释由此产生的大量数据 需要有效的生物信息学方法。在这个建议中，我们将开发一系列新颖的生物信息学 BS-seq数据分析方法。首先，在我们的BSMAP计划早期成功的基础上，我们将开发 下一代亚硫酸氢盐校准器我们将构建一个亚硫酸氢盐和SNP“感知”基因组索引， 用IUPAC代码和动态Burrows-Wheeler变换（DBWT）进行读段映射。我们还将 区分CpG甲基化和C/T SNP，并使用GPU硬件加速来改进映射 速度其次，我们将开发一种功能强大的差异甲基化分析算法， 来自测序的取样变异和重复之间的生物变异。我们还将介绍一个 一种新的度量标准，用于评估差异甲基化的统计学和生物学意义。该模型 将有足够的能力来检测低CpG密度调控中的单CpG分辨率差异甲基化。 区域，例如增强子，测序深度低至5-10倍。第三，我们将制定一个 综合BS-seq数据分析管道使用银河网络接口和云计算。我们将 根据我们正在开发的所有BS-seq工具和其他公共算法， 表观遗传学社区的新兴需求。这条管道将使实验生物学家能够 大部分分析都是自己做的。这些生物信息学方法将进行广泛的测试， 实验验证我们的合作者。尽管使用常规BS-seq 在这个提议中，我们的生物信息学方法可以立即用于其他修改的BS-seq协议， 如最近分别针对5 mC和5 hmC开发的oxBS-Seq和TAB-Seq。最后，作为案例研究，我们 将应用这些新的方法来解开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我们生物信息学的功效 方法.

Project Summary / Abstract 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.