Genome-wide structural organization of proteins within human gene regulatory complexes

项目摘要/摘要 人类基因组的DNA序列告诉我们组成蛋白质的组成 健康细胞，但也改变了产生疾病细胞的成分。蛋白质生产是如何被控制的 通过对编码它们的基因进行调控，对健康和患病的细胞至关重要。 准确地知道基因调控蛋白在哪里结合，并在整个基因组中组织起来，包括 它们之间的相互作用，告诉我们基因是如何被调控和被错误调控的。因为在那里 可能有数千种不同的调节蛋白和数千种不同的人类 细胞类型和环境反应是各种调节蛋白亚群的产物， 基因调控事件的整个“宇宙”是相当重要的，因此，识别起来相当昂贵。一 基因组数据分析的主要瓶颈之一是高效和可扩展的可视化方法。这个 PEGR开源平台将提供对任何数量的人类细胞测序的编程访问 来自NGS处理的任何阶段的数据集，管道分析结果可用于高吞吐量 机器学习测试与开发。该项目将通过自动化的 对来自小型和大型数据集的结果进行分析和可视化。该架构将包括 以下功能：1)基于云的安全元数据管理系统，可灌输 实验的严密性、重复性和数据共享；2)基于Galaxy的自动化表观基因组数据 为共同表观基因组的标准化处理提供易于使用的“向导”的处理管道 数据类型；以及3)易于部署的开源软件包，作为传播数据、工具、 以及通过云服务进行发现。

Project Summary/Abstract The DNA sequence of the human genome informs us as to the composition of proteins that make up healthy cells, but also altered compositions that create diseased cells. How protein production is controlled through the regulation of the genes that encode them is of critical importance for healthy and diseased cells. Knowing precisely where gene regulatory proteins bind, and are organized throughout the genome, including their interactions with each other, informs us as to how genes are regulated and mis-regulated. Since there are potentially thousands of different kinds of regulatory proteins and thousands of different kinds of human cell types and environmental responses that are a product of various subsets of regulatory proteins, the entire “universe” of gene regulatory events is quite substantial and consequently, quite costly to identify. One of the main bottlenecks in analysis of genomic data is efficient and scalable visualization approaches. The PEGR open source platform will provide programmatic access to any number of human cell sequenced datasets, from any stage of NGS processing, with the pipeline analysis results available for high-throughput machine learning testing and development. This project will empower discovery through the automated analysis and visualization of results from both small- and large-scale datasets. This architecture will include the following features: 1) a secure, cloud-based, metadata management system that instills best practices of experimental rigor, reproducibility, and data sharing; 2) automated Galaxy-based epigenomic data processing pipelines that provide easy-to-use “wizards” for standardized processing of common epigenomic data types; and 3) an easily de-ployable, open source software package as a means to disseminate data, tools, and discoveries via cloud services.