High definition in vivo footprinting via single molecule sequencing

通过单分子测序进行高清体内足迹

• 批准号: 7326241
• 负责人: MICHAEL O DORSCHNER
• 金额: $ 34.79万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-10 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7326241
• 关键词: Base Sequence; Binding Sites; Biological; Biological Assay; Complement; Condition; Count; DNA Sequence; Data; Databases; Deoxyribonuclease I; Development; Elements; Ensure; Frequencies; Genome; Goals; Hypersensitivity; Indium; Ligation; Light; Literature; Maps; Mediating; Methods; Modification; Nucleotides; Numbers; Polymerase Chain Reaction; Positioning Attribute; Protein Binding; Proteins; Regulatory Element; Reproducibility; Research Personnel; Resolution; Sensitivity and Specificity; Site; Standards of Weights and Measures; System; Techniques; Technology; Time; base; cell type; dimethyl sulfate; in vivo; next generation; novel; programs; single molecule; tool

DESCRIPTION (provided by applicant): We plan to develop a high-throughput, high-resolution in vivo footprinting method based on next generation single molecule DNA sequencing technology. Single molecule sequencing will be used in place of conventional methods to detect cleavage induced by three modifying agents: DMS (dimethyl sulfate), DNase I and UV light. With a simple modification to standard ligation-mediated PCR (LM-PCR), the precise sequence of protein binding sites can be determined by sequencing short cleavage signature tags and simply counting the number of times a tag terminates at each nucleotide position within a designated 'footprint window'. This counting method will convert the cumbersome, 'band intensity' analysis of classical footprinting methods to an absolute, frequency-based approach that can be readily analyzed by an automated analysis pipeline. The project commences with the development and demonstration of the technology and its large-scale use. First, optimal conditions will be developed and applied to generate cleavage maps of previously footprinted 'control' segments (DMS, DNase I and UV) in a multiplexed format. With optimized parameters established, the assay will be used for the de novo footprinting of putative cis regulatory elements, as indicated by DNase I hypersensitivity. Within this aim we will also determine the sensitivity and specificity of the HD (High Definition) tag footprinting assay as well as assess the biological and technical reproducibility of the system. > 100 tag-generated footprints will be validated each year by classical footprinting techniques to ensure accuracy of the data. Finally, we will determine the scalability of the HD tag footprinting system and apply it on a large scale to the analysis of putative functional elements within the ENCODE regions. DNase I hypersensitive sites identified from four cell types will be footprinted and data compared to existing ENCODE data types. A high-throughput method capable of generating high resolution DNase I, DMS and UV footprints will provide a powerful tool for identifying the protein interacting sequences within novel cis-regulatory elements of any eukaryotic genome. High-resolution tag footprinting will contribute to the goals of the ENCODE project by complementing existing assays and creating a large volume of precise sequence-based data that can be used to further annotate functional elements in the genome.

描述（由申请人提供）： 我们计划开发一种基于下一代单分子DNA测序技术的高通量、高分辨率的体内足迹法。将使用单分子测序代替常规方法来检测由三种修饰剂诱导的切割：DMS（硫酸二甲酯）、DNA酶I和UV光。通过对标准连接介导的PCR（LM-PCR）的简单修改，蛋白质结合位点的精确序列可以通过对短切割特征标签进行测序并简单地计数标签在指定的“足迹窗口”内终止于每个核苷酸位置的次数来确定。这种计数方法将传统足迹法的繁琐的“带强度”分析转换为绝对的、基于频率的方法，该方法可以通过自动分析管道进行分析。该项目首先是开发和示范该技术及其大规模使用。首先，将开发最佳条件并应用于以多重格式生成先前足迹化的“对照”区段（DMS、DNA酶I和UV）的裂解图谱。 在确定优化参数后，该试验将用于推定顺式调控元件的从头足迹分析，如DNA酶I超敏反应所示。在此目标下，我们还将确定HD（高清晰度）标签足迹测定的灵敏度和特异性，并评估系统的生物学和技术再现性。每年将通过经典足迹技术验证超过100个标签生成的足迹，以确保数据的准确性。最后，我们将确定HD标签足迹系统的可扩展性，并将其大规模应用于分析ENCODE区域内的推定功能元件。将对从四种细胞类型中鉴定的DNA酶I超敏位点进行足迹分析，并将数据与现有的ENCODE数据类型进行比较。能够产生高分辨率DNase I、DMS和UV足迹的高通量方法将为鉴定任何真核基因组的新型顺式调控元件内的蛋白质相互作用序列提供强有力的工具。高分辨率标签足迹将有助于ENCODE项目的目标，通过补充现有的检测方法，并创建大量精确的基于序列的数据，可用于进一步注释基因组中的功能元件。