C. elegans transcriptional regulatory elements

线虫转录调控元件

• 批准号: 8064423
• 负责人: PAUL Warren STERNBERG
• 金额: $ 48.52万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8064423
• 关键词: Active Sites; Algorithms; Animal Model; Animals; Behavior; Benchmarking; Binding Sites; Bioinformatics; Biological; Biological Assay; Caenorhabditis; Caenorhabditis elegans; Characteristics; Computer Simulation; Computing Methodologies; DNA; DNA Sequence; Data; Development; Disease; Elements; Family; Gene Expression; Genes; Genetic Transcription; Genome; Genomics; Goals; Gold; Learning; Life Cycle Stages; Link; Maps; Measures; Methods; Modeling; Nematoda; Output; Pattern; Phylogenetic Analysis; Physiological; Physiology; Predictive Value; Problem Solving; Production; Property; Regulator Genes; Regulatory Element; Reporter; Research Infrastructure; Research Personnel; Resolution; Siblings; Site; Site-Directed Mutagenesis; Small Interfering RNA; Source Code; Staging; System; Technology; Testing; Time; Transgenic Animals; Transgenic Organisms; Validation; base; cell type; chromatin immunoprecipitation; cofactor; comparative genomics; computerized tools; gene function; genome sequencing; genome-wide; improved; in vivo; programs; public health relevance; research study; transcription factor

DESCRIPTION (provided by applicant): Cis-acting regulatory elements control gene expression and are involved in all aspects of development, behavior and physiology; but no cis-regulatory element map yet exists for any metazoan genome. We therefore propose to identify genome-wide cis-regulatory elements in C. elegans. Among existing model organisms, C. elegans offers a strong combination of biological properties, transgenic technology, comparisons to genomes in four sibling species, and critical computational and bioinformatic infrastructure. We intend to find genomic regulatory elements in genes with widely varying expression patterns, gene functions, and cis-element content that drive expression in diverse developmental stages, cell types and physiological conditions. A pipeline of genomic predictions followed by efficient transgenic reporter assays will allow us to generate and analyze 10 DNA constructs each week. In year 1 we plan to identify hundreds of regulatory elements, with higher numbers in following years as we become better at predicting functional elements. Predicted elements will be assigned statistical scores based on the quality of their supporting computational and experimental data. We will also use chromatin immunoprecipitation analyzed by intense sequencing (ChIP-seq) to find regulatory modules, and compare its accuracy in finding functional sequences to that of predictions based on comparative genomics. The first round of our results from direct tests of predicted elements and ChIP-seq will be combined with external data from modENCODE to improve our predictive algorithms for later cycles of genome-wide element prediction. Our data will be released promptly to WormBase, and all our computational tools are freely available with full source code. PUBLIC HEALTH RELEVANCE: Regulatory DNA sequences that control the time, place, and level of transcription are crucial for normal development, behavior and physiology as well as disease; yet there is no genome-wide map of them for any animal genome, nor are researchers currently able to predict their functional output from their DNA sequences. We will attempt to solve this problem by extensive, reiterated experimental tests of computational predictions in a simple animal genome.

描述（由申请人提供）：顺式调控元件控制基因表达，涉及发育、行为和生理的各个方面；但目前还没有任何后生动物基因组的顺式调控元件图谱。因此，我们建议在秀丽隐杆线虫中鉴定全基因组顺式调控元件。在现有的模式生物中，秀丽隐杆线虫提供了生物学特性、转基因技术、与四个兄弟物种的基因组比较以及关键的计算和生物信息学基础设施的强大组合。我们打算在基因中发现具有广泛不同的表达模式、基因功能和顺式元件含量的基因组调控元件，这些元件在不同的发育阶段、细胞类型和生理条件下驱动表达。通过高效的转基因报告基因分析，我们可以每周生成并分析10个DNA结构。在第一年，我们计划识别数百个调控元件，随着我们在预测功能元件方面做得更好，接下来几年的数量会更多。预测元素将根据其支持的计算和实验数据的质量分配统计分数。我们还将使用染色质免疫沉淀分析，通过高强度测序（ChIP-seq）来寻找调节模块，并将其在寻找功能序列方面的准确性与基于比较基因组学的预测进行比较。第一轮预测元件和ChIP-seq直接测试的结果将与modENCODE的外部数据相结合，以改进我们的全基因组元件预测的后续周期的预测算法。我们的数据将及时发布到WormBase，我们所有的计算工具都是免费的，并提供完整的源代码。