Software Tools For Regulatory Analysis of Large Cancer Methylome Datasets

用于大型癌症甲基化数据集监管分析的软件工具

• 批准号: 9126967
• 负责人: Benjamin P Berman
• 金额: $ 34.44万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-13 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9126967
• 关键词: Adoption; Basic Science; Binding; Binding Sites; Bioconductor; Biological Assay; Biology; Cancer cell line; ChIP-seq; Chromatin; Clinical; Clinical Research; Collaborations; Communities; DNA Binding; DNA Maintenance; DNA Methylation; DNA-Protein Interaction; DNase I hypersensitive sites sequencing; Data; Data Files; Data Set; Databases; Defect; Deoxyribonuclease I; Deoxyribonucleases; Disease; Encyclopedia of DNA Elements; Enhancers; Ensure; Epigenetic Process; Feedback; Funding; Galaxy; Gene Expression Regulation; Gene Targeting; Genes; Genomics; Grant; High-Throughput DNA Sequencing; Human; Human Genome; Hypersensitivity; Individual; Institutes; Knowledge; Laboratories; Malignant Neoplasms; Maps; Measures; Metadata; Methods; Methylation; Modification; Molecular; Molecular Conformation; Mutation; Nuclear; Nucleotides; Online Systems; Patients; Play; Primary Neoplasm; Regulator Genes; Regulatory Element; Relative (related person); Research Personnel; Role; Sampling; Site; Software Engineering; Software Tools; Source; Staging; System; Techniques; Technology; Teleconferences; Testis; The Cancer Genome Atlas; Time; Tissues; Transcriptional Regulation; United States National Institutes of Health; Untranslated RNA; Work; base; bisulfite sequencing; cancer genomics; computerized data processing; data integration; digital; epigenome; epigenomics; experience; genome sequencing; genome-wide; histone modification; improved; innovation; meetings; methylation pattern; methylome; open source; patient population; programs; public health relevance; success; tool; transcription factor; transcriptomics; tumor; user-friendly

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.

描述（由申请人提供）：大量吞吐量DNA测序正在迅速改变癌症基因调节的研究。大规模的努力，例如NIH资助的“ DNA元素的百科全书”（编码）已利用测序来绘制人类癌细胞系中全基因组染色质特征的绘制，并使用诸如染色质蛋白免疫沉淀测序（CHIP-SEQ）（CHIP-SEQ）和DNase I Wellessitiver semencentive for Necencence（DATAB detab fornabe detab of detab and dyab and dyab seeq）以及对dhs-seq）的构图（chip-seq）的特征，并制定了。人类基因组中的调节元素。重点是患者样品的大多数癌症基因组学项目都使用DNA甲基化分析，我们和其他人表明，这些甲基化谱与编码数据的整合可以使生物学上相关的表观基因组变化鉴定。但是，大多数癌症生物学家不容易获得所需的软件工具。参考图本身需要对基因调节特征的领域知识，这些特征超出了许多临床研究小组的范围，并且公开可用的数据集通常是异质和经常转移分析管道的结果。我们将开发自动化工具，用于统一各种基因调节数据库，并使用开源R/Bioconductor Framework和我们的开源基于Web的Galaxy系统开发强大但用户友好的甲基化工作流程。标准工作流将使用我们为TCGA项目开发的方法，以从Illumina Infinium或Bisulfite-Seq平台中导入和分析大量的原始甲基化数据文件。我们还将允许进口任意样品元数据，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路比较。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DNase超敏反应数据来定义局灶性染色质状态，以及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实验室的最新工作表明，在顺式调节元件（例如增强子和绝缘子）上的甲基化变化主要由单个转录因子的结合驱动，因此反映了特定转录网络将基因直接靶向。我们将使用编码可提供的芯片序列和DNA结合基序分析来分析单个蛋白质/DNA相互作用位置水平的用户甲基化数据。最后，由于这项工作的成功将以癌症基因组学界的采用程度来衡量，因此我们将与几个大规模的癌症基因组学组一起充当β验证者，并帮助我们改善工作流程。