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Revealing protein-protein interactions and RNA-protein interactions at genome-scale in two weeks

两周内揭示基因组规模的蛋白质-蛋白质相互作用和 RNA-蛋白质相互作用

基本信息

批准号：
10470199
负责人：
Sheng Zhong
金额：
$ 37.7万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-18 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10470199
关键词：
Bar Codes Binding Proteins Bioinformatics Books Computer software Coupled DNA DNA Ligation DNA Sequence DNA mapping Data Data Analyses Development Evaluation Exhibits Experimental Designs Funding Genome Genomics Grouping High-Throughput DNA Sequencing Human Human Genome In Vitro Individual Knowledge Libraries Maps Mass Spectrum Analysis Messenger RNA Methods Molecular Paris, France Protein-Protein Interaction Map Proteins RNA RNA Binding RNA-Binding Proteins RNA-Protein Interaction Reading Records Reporting Reproducibility Research Resources Salvelinus Technology Testing Time Translations Two-Hybrid System Techniques United States National Institutes of Health Variant Yeasts base bioinformatics tool data analysis pipeline experimental study genome-wide high throughput technology improved informatics tool novel strategies programs protein protein interaction success technology development transcriptome sequencing

项目摘要

Revealing protein-protein interactions and RNA-protein interactions at genome-scale in two weeks Abstract Our ability to interpret the human genome is limited by our knowledge of the interaction networks of the products of the genome sequence, including RNAs and proteins. If we had a complete reference map of all DNA-DNA, protein-DNA, RNA-DNA, RNA-RNA, RNA-protein, and protein-protein interactions, we would have a completely new approach to reading the book of the human genome. Recent technology breakthroughs, including those led by NIH Common Fund programs, enabled genome-wide mapping of DNA-DNA, protein-DNA, RNA-DNA, and RNA-RNA interactions en masse. However, genome-scale mapping of RNA-protein interactions (RPI) and protein-protein interactions (PPI) remain laborious and resource-intensive. In this project, we propose two extremely high-throughput genomic-based technologies for mapping human RPI and PPI networks at genome scale. We will develop bioinformatics tools to analyze the data with statistical rigor. Specifically, we propose an “all-vs-all” approach to map PPI and RPI networks. These genomics technologies and their coupled genomic informatics tools will generate reference maps of human PPI and RPI networks. In the long term, such maps will greatly facilitate the interpretation of the functions of the human genome. In Aim 1, we will develop an extremely high-throughput technology called “PPI-seq” to map the PPI network at genome-scale. PPI-seq is expected to be capable of generating a reference map of the human PPI network within a single lab in 2 weeks' time, with an expected yield of ~50,000 high-confidence pairwise PPIs. In Aim 2, we will develop an extremely high- throughput technology called “RPI-seq” to map the RPI network at genome-scale. RPI-seq is expected to be capable of generating a reference map of the human RPI network within single lab in 2 weeks' time. RPI-seq will simultaneous reveal RNA binding proteins (RBP) and the RNAs bound by each RBP.

在两周内揭示基因组水平上的蛋白质-蛋白质相互作用和RNA-蛋白质相互作用摘要我们解释人类基因组的能力受限于我们对产物相互作用网络的了解包括RNA和蛋白质。如果我们有一个完整的DNA-DNA参考图谱，蛋白质-DNA，RNA-DNA，RNA-RNA，RNA-protein和蛋白质-蛋白质相互作用，我们将有一个完全的阅读人类基因组的新方法。最近的技术突破，包括通过NIH共同基金项目，使全基因组范围内的DNA-DNA，蛋白质-DNA，RNA-DNA， RNA-RNA相互作用然而，RNA-蛋白质相互作用（RPI）和蛋白质-蛋白质相互作用（PPI）仍然是费力和资源密集的。在这个项目中，我们提出了两个用于在基因组定位人类RPI和PPI网络的极高通量的基于基因组的技术规模我们将开发生物信息学工具，以统计学的严谨性分析数据。具体来说，我们提出一个 “所有对所有”的方法来映射PPI和RPI网络。这些基因组学技术和它们的耦合基因组学信息学工具将生成人类PPI和RPI网络的参考图。从长远来看，这些地图将极大地促进了对人类基因组功能的解释。在目标1中，我们将制定一个非常高通量技术称为“PPI-seq”，以绘制基因组规模的PPI网络。PPI-seq预计将能够在2周内在单个实验室内生成人类PPI网络的参考图，预期产量约为50，000个高置信度成对PPI。在目标2中，我们将制定一个非常高的- 这是一种称为“RPI-seq”的吞吐量技术，用于在基因组规模上绘制RPI网络。RPI-seq预计将能够在2周内在单个实验室内生成人类RPI网络的参考地图。RPI-seq将同时揭示RNA结合蛋白（RBP）和每个RBP结合的RNA。