The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Genetics. 2023 May 4;224(1). doi: 10.1093/genetics/iyad042.
The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.
大鼠基因组数据库(RGD,https://rgd.mcw.edu)已经从一个简单的大鼠遗传标记、图谱和基因资源,通过添加多种基因组数据类型以及广泛的疾病和表型注释,并开发工具来有效地挖掘、分析和可视化可用数据,从而为研究人员的假设驱动研究提供支持。利用其强大而灵活的基础设施,RGD 除了大鼠之外,还增加了人类和其他八种模式生物(小鼠、13 条纹地松鼠、龙猫、裸鼹鼠、狗、猪、非洲绿猴/长尾猴和倭黑猩猩)的数据,以增强其转化方面。本文概述了数据库的最新进展,包括基因组、变异和定量表型数据。我们还简要介绍了虚拟比较图谱(VCMap),这是一个更新的工具,用于探索物种之间的同线性,作为 RGD 工具套件的改进,接着讨论了对现有的 PhenoMiner 工具的改进,该工具有助于研究人员在大鼠品系之间查找和比较定量数据。总之,RGD 专注于为研究人员提供一个不断改进、一致和高质量的数据资源,同时提高数据的可重复性,并满足可发现性、可访问性、互操作性和可重复性(FAIR)数据原则。