Software Tools For Regulatory Analysis of Large Cancer Methylome Datasets

描述（由申请人提供）：大规模高通量DNA测序正在迅速改变癌症基因调控的研究。大规模的努力，如美国国立卫生研究院资助的“DNA元件百科全书”（ENCODE），利用染色质免疫沉淀测序（ChIP-seq）和DNA酶I超敏测序（DHS-seq）等转化技术，利用测序来绘制人类癌细胞系的全基因组染色质特征，并在人类基因组基因调控元件的综合数据库方面取得了重大进展。大多数专注于患者样本的癌症基因组学项目使用DNA甲基化谱，我们和其他人已经表明，将这些甲基化谱与ENCODE数据整合可以识别生物学相关的表观基因组变化。然而，所需的软件工具对大多数癌症生物学家来说并不容易获得。参考图谱本身需要基因调控特征的领域知识，这超出了许多临床研究小组的范围，而公开可用的数据集往往是异构和频繁变化的分析管道的结果。我们将开发用于统一各种基因调控数据库的自动化工具，并使用开源的R/BioConductor框架和开源的基于web的Galaxy系统开发功能强大且用户友好的甲基化工作流程。标准工作流程将使用我们为TCGA项目开发的方法，从Illumina Infinium或bisulite -seq平台导入和分析大量原始甲基化数据文件。我们还将允许导入任意样本元数据，以便用户可以在癌症亚型或临床协变量之间进行双向或多向比较。我们的工作流程将由对染色质景观的最新理解驱动，其中包括使用组蛋白修饰和dna酶超敏性数据来定义病灶染色质状态，以及使用Hi-C（核构象）和复制时间来定义核拓扑结构域。我们的实验室和其他人最近的工作表明，顺式调控元件（如增强子和绝缘子）的甲基化变化主要是由单个转录因子的结合驱动的，因此反映了特定转录网络对基因的直接靶向。我们将使用ENCODE提供的ChIP-seq和DNA结合基序分析来分析单个蛋白质/DNA相互作用位点水平上的用户甲基化数据。最后，由于这项工作的成功将由癌症基因组学社区内的采用程度来衡量，我们将聘请几个大型癌症基因组学小组作为beta测试者，并帮助我们改进工作流程。

DESCRIPTION (provided by applicant): Massively high throughput DNA sequencing is quickly changing the study of gene regulation in cancer. Large- scale efforts such as the NIH-funded "Encyclopedia of DNA Elements" (ENCODE) have exploited sequencing to map genome-wide chromatin features in human cancer cell lines using transformative technologies such as Chromatin Immunoprecipitation sequencing (ChIP-seq) and DNase I hypersensitivity sequencing (DHS-seq), and have made great strides toward a comprehensive database of gene regulatory elements in the human genome. The majority of cancer genomics projects focusing on patient samples use DNA methylation profiling, and we and others have shown that integration of these methylation profiles with ENCODE data can enable the identification of biologically-relevant epigenomic changes. However, the software tools required are not readily available to most cancer biologists. The reference maps themselves require a domain knowledge of gene regulatory features that is beyond the scope of many clinical research groups, and the publically available datasets are too often the result of heterogeneous and frequently shifting analysis pipelines. We will develop automated tools for unifying the various gene regulatory databases, and develop powerful yet user-friendly methylation workflows using the open-source R/BioConductor framework and our open-source, web-based Galaxy system. Standard workflows will use the methods we have developed for the TCGA project to import and analyze large numbers of raw methylation data files from either the Illumina Infinium or Bisulfite-seq platforms. We will also allow import of arbitrary sample metadata so users can perform two-way or multi-way comparisons between cancer subtypes or clinical covariates. Our workflows will be driven by the most current understanding of the chromatin landscape, which includes using histone modifications and DNase hypersensitivity data to define focal chromatin state, and Hi-C (nuclear conformation) and replication timing to define nuclear topological domains. Recent work by our lab and others suggests that methylation changes at cis-regulatory elements such as enhancers and insulators are driven primarily by binding of individual transcription factors, and thus reflect direct targeting of genes by specific transcriptional networks. We will use combined ChIP-seq and DNA binding motif analyses available from ENCODE to analyze user methylation data at the level of the individual protein/DNA interaction site. Finally, because the success of this effort will be measured by the degree of adoption within the cancer genomics community, we will engage several large- scale cancer genomics groups to act as beta testers and help us improve our workflows.