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

项目总结/摘要 人类基因组的DNA序列告诉我们， 健康的细胞，但也改变了产生病变细胞的成分。如何控制蛋白质生产 通过调节编码它们的基因，对健康和患病细胞至关重要。 精确地知道基因调控蛋白结合的位置，并在整个基因组中组织，包括 它们之间的相互作用，告诉我们基因是如何调节和错误调节的。由于 可能有成千上万种不同的调节蛋白， 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”是相当大的，因此鉴定起来相当昂贵。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。的 PEGR开源平台将提供编程访问任何数量的人类细胞测序 数据集，来自NGS处理的任何阶段，管道分析结果可用于高吞吐量 机器学习测试和开发。该项目将通过自动化的 分析和可视化来自小型和大型数据集的结果。该架构将包括 以下功能：1）一个安全的、基于云的元数据管理系统， 实验的严谨性、可重复性和数据共享; 2）基于银河系的表观基因组数据自动化 处理管道，为常见表观基因组的标准化处理提供易于使用的“向导” 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， 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.