Generation of an In Vivo Human Genome Transcriptional Enhancer Dataset

体内人类基因组转录增强子数据集的生成

• 批准号: 7941543
• 负责人: Len Alexander Pennacchio
• 金额: $ 35.22万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7941543
• 关键词: Address; Algorithms; Amino Acid Sequence; Area; Automobile Driving; Bioinformatics; Biological; Biological Assay; Biology; Chickens; Clinical; Communities; Complementary DNA; Computer Analysis; Coupled; DNA; Data; Data Set; Databases; Deposition; Development; Elements; Embryo; Enhancers; Exons; Expressed Sequence Tags; Fishes; Functional RNA; Gene Expression; Gene Transfer Techniques; Generations; Genes; Genetic Enhancer Element; Genome; Genomics; Genotype; Grant; Harvest; Human; Human Genome; Human Genome Project; Image; Informatics; Investigation; Knowledge; Morphologic artifacts; Mus; Mutation; Nucleic Acid Regulatory Sequences; Pattern; Peptide Sequence Determination; Persons; Pilot Projects; Property; Rana; Reading; Regulator Genes; Reporter; Reporting; Research Personnel; Resolution; Resources; Rodent; Scientist; Site; Specific qualifier value; Staining method; Stains; System; Takifugu; Testing; Time; Tissues; Training; Transgenic Mice; Transgenic Organisms; Vocabulary; base; beta-Galactosidase; comparative; design; digital; digital imaging; egg; genome sequencing; genome-wide; human DNA; human disease; in vivo; insight; interest; user-friendly; vector; web site

DESCRIPTION (provided by applicant): Our ability to identify the majority of exons in the human genome has been dramatically facilitated by the availability of extensive experimental data (EST, cDNA, and protein sequences) thereby providing training sets for the development of effective algorithms for the cfe novo prediction of such elements. In stark contrast, the vocabulary of gene regulatory regions in the human genome remains poorly defined, in large part, due to the lack of parallel experimental training sets for these sequences. Recent advances in our ability to predict which non-coding sequences have a higher likelihood of acting as transcriptional enhancers based on deep evolutionary conservation have provided some leverage for addressing this problem. In preliminary studies, we have examined 150 extremely conserved non-coding sequences in a transgenic mouse reporter assay and demonstrate that 58 of these sequences have distinct tissue specific enhancer activity. With this background, we propose here to couple our expertise in comparative genomics and high throughput mouse transgenesis to define the enhancer activity of 1,500 deeply conserved non-coding elements located throughout the human genome. We will make the results of our in vivo studies publicly available through an online database with extensive search capabilities, allowing users to bin sequences producing similar expression patterns to identify shared sequence features. These datasets will provide an essential resource for a broad group of investigators in computational, developmental, and clinical biology focused on deciphering the rules that govern human gene expression. Accordingly, this grant aims to classify the gene regulatory properties of non-coding DNA in the human genome through: (1) the characterization of 1,500 extremely conserved human DNA fragments for spatial enhancer activity in transgenic mice and (2) the development of a publicly available in vivo enhancer database to display these results. In addition, to provide the bioinformatic community with a means to test ab initio predictions of enhancers based on their analyses of our data generated in Aim 1, we further propose to (3) test 15-20 predicted enhancers by outside investigators per year in our transgenic mouse system. Lay Person Summary: The generation of the entire human genome sequence serves as a routine starting point for a huge investigator base and has aided in defining the majority of genes in our genome. However, our understanding of the sequences that regulate these genes is meager, despite their presumed alterations in human disease. Here, we propose to leverage human-fish genome comparisons to identify deeply conserved non-gene sequences and to test their ability to act as gene regulatory sequences in transgenic mice. Such a community resource is expected to significantly fill our void in gene regulatory annotation of the human genome and to decipher their mutation as a cause of human disease. .

描述（由申请人提供）：大量实验数据（EST、cDNA 和蛋白质序列）的可用性极大地促进了我们识别人类基因组中大多数外显子的能力，从而为开发用于此类元件的 cfe novo 预测的有效算法提供了训练集。形成鲜明对比的是，人类基因组中基因调控区域的词汇仍然没有明确定义，这在很大程度上是由于缺乏这些序列的平行实验训练集。基于深度进化保守性，我们预测哪些非编码序列更有可能充当转录增强子的能力的最新进展为解决这一问题提供了一些手段。在初步研究中，我们在转基因小鼠报告基因测定中检查了 150 个极其保守的非编码序列，并证明其中 58 个序列具有不同的组织特异性增强子活性。在此背景下，我们建议将我们在比较基因组学和高通量小鼠转基因方面的专业知识结合起来，以确定位于整个人类基因组中 1,500 个深度保守的非编码元件的增强子活性。我们将通过具有广泛搜索功能的在线数据库公开我们的体内研究结果，允许用户对产生相似表达模式的序列进行分类，以识别共享的序列特征。这些数据集将为计算、发育和临床生物学领域的广大研究人员提供重要资源，重点是破译人类基因表达的规则。因此，这笔赠款旨在通过以下方式对人类基因组中非编码 DNA 的基因调控特性进行分类：(1) 对转基因小鼠中空间增强子活性的 1,500 个极其保守的人类 DNA 片段进行表征，以及 (2) 开发一个公开的体内增强子数据库来显示这些结果。此外，为了向生物信息学界提供一种方法，根据他们对目标 1 中生成的数据的分析来测试增强子的从头预测，我们进一步建议 (3) 每年由外部研究人员在我们的转基因小鼠系统中测试 15-20 个预测的增强子。外行总结：整个人类基因组序列的生成是庞大的研究人员基础的常规起点，并有助于定义我们基因组中的大多数基因。然而，尽管推测这些基因会改变人类疾病，但我们对调节这些基因的序列了解甚少。在这里，我们建议利用人类与鱼类基因组比较来识别深度保守的非基因序列，并测试它们在转基因小鼠中充当基因调控序列的能力。这样的社区资源预计将显着填补我们在人类基因组基因调控注释方面的空白，并破译其突变作为人类疾病的原因。 。