Creating Comprehensive Maps of Worm and Fly Transcription Factor Binding Sites

创建蠕虫和苍蝇转录因子结合位点的综合图谱

基本信息

批准号：
8566279
负责人：
ROBERT H WATERSTON
金额：
$ 253.33万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-20 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8566279
关键词：
Accounting Amino Acid Sequence Animal Model Animals Antibodies Award Beds Binding Binding Sites Bioinformatics Biological Testing Biology Caenorhabditis elegans Code Communities Data Data Quality Data Set Databases Development Disease Drosophila genus Drosophila melanogaster Elements Foundations Funding Gene Expression Gene Expression Profile Gene Expression Profiling Gene Fusion Gene Targeting Gene Transfer Techniques Genes Genetic Transcription Genome Genomics Grant Health Homologous Gene Human Instruction Life Cycle Stages Link Maps Metric Mus National Human Genome Research Institute Nematoda Organism Pattern Peptide Sequence Determination Phase Principal Investigator Process Production Property Publications RNA Interference Reading Regulatory Element Relative (related person)Research Science Site Staging Standardization Study models Testing Time Transcript Transcription factor genes United States National Institutes of Health Validation Work base computerized data processing enhanced green fluorescent protein fly genome sequencing human disease improved in vivo insight member model organisms databases research study transcription factor transcriptome sequencing

项目摘要

DESCRIPTION (provided by applicant): Our project seeks to identify the regulatory elements recognized by the vast majority of transcription factors (TFs) in the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans. In the initial modENCODE project, an experimental pipeline was developed and applied to -100 TFs in each organism. In this intervening year, we expect to capture data for another 75-100 factors. The present project builds on the advances made by the groups in the initial phase and also combines the production pipelines to increase efficiency and to realize economies of scale. With these improvements, we will generate data sets for another 400 factors from each organism, which when combined with previous work will represent the bulk of all transcription factors in these key model organisms. For both organisms, the overall strategy tags transcription factor genes by fusion with an enhanced Green Fluorescent Protein (eGFP) sequence through recombineering of large insert clones, and introducing the tagged genes into the genome by transgenesis. ChlP-seq using a high quality anti-GFP antibody is performed on the developmental stage(s) with maximal GFP expression, as guided by available RNA-seq expression data. The aligned sequence reads are analyzed to identify candidate binding sites and likely target genes. We will prioritize TFs with human homologs to maximize the broader utility of the data. We will also perform RNAi of 125 TFs in each organism, followed by RNA-seq, to validate called peaks and their assigned target genes. Finally, we will integrate the information for the different data setsto construct regulatory networks implied by the TF binding site data. We will coordinate with ENCODE projects on human TFs, and our data will provide key in vivo and developmental regulatory information that will be essential to delineate both fundamentally conserved as well as human-specific properties of TFs. RELEVANCE (See instructions): Insights from the study of the model organisms Drosophila and C. elegans provide the basis for broad understanding of fundamental processes of animal biology. Because many of their genes have clear relatives in humans, these studies have also led directly to improved understanding of human diseases and in some cases to therapies. Similarly, creating a comprehensive understanding of transcription factor binding sites and building regulatory networks in these key model organisms will create the foundation for understanding human regulatory networks both in health and disease.

描述（由申请人提供）：我们的项目旨在确定果蝇果蝇和线虫秀丽隐杆线虫中绝大多数转录因子（TF）所识别的调控元件。在最初的 modENCODE 项目中，开发了一个实验管道并将其应用于每个生物体中的 -100 个 TF。在这中间的一年里，我们预计会捕获另外 75-100 个因素的数据。目前的项目建立在各集团在初始阶段取得的进展的基础上，并结合了生产管道以提高效率并实现规模经济。通过这些改进，我们将为每个生物体生成另外 400 个因子的数据集，这些数据集与之前的工作相结合将代表这些关键模型生物体中所有转录因子的大部分。对于这两种生物体，总体策略是通过大插入克隆的重组工程与增强的绿色荧光蛋白（eGFP）序列融合来标记转录因子基因，并通过转基因将标记的基因引入基因组。根据可用的 RNA-seq 表达数据的指导，使用高质量抗 GFP 抗体在具有最大 GFP 表达的发育阶段进行 ChlP-seq。分析比对的序列读数以识别候选结合位点和可能的目标基因。我们将优先考虑具有人类同源物的转录因子，以最大限度地提高数据的更广泛效用。我们还将对每个生物体中的 125 个 TF 进行 RNAi，然后进行 RNA-seq，以验证调用的峰及其指定的目标基因。最后，我们将整合不同数据集的信息，构建 TF 结合位点数据隐含的调控网络。我们将与人类转录因子的 ENCODE 项目进行协调，我们的数据将提供关键的体内和发育监管信息，这些信息对于描述转录因子的基本保守特性和人类特异性特性至关重要。相关性（参见说明）：对模式生物果蝇和线虫的研究的见解为广泛理解动物生物学的基本过程提供了基础。由于它们的许多基因在人类中具有明显的亲缘关系，因此这些研究也直接提高了对人类疾病的认识，并在某些情况下促进了治疗方法。同样，对转录因子结合位点的全面了解并在这些关键模型生物体中建立调控网络将为理解人类健康和疾病调控网络奠定基础。