Brademan Dain R, Miller Ian J, Kwiecien Nicholas W, Pagliarini David J, Westphall Michael S, Coon Joshua J, Shishkova Evgenia
Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Morgridge Institute for Research, Madison, WI 53715, USA.
Patterns (N Y). 2020 Oct 9;1(7). doi: 10.1016/j.patter.2020.100122.
Researchers now generate large multi-omic datasets using increasingly mature mass spectrometry techniques at an astounding pace, facing new challenges of "Big Data" dissemination, visualization, and exploration. Conveniently, web-based data portals accommodate the complexity of multi-omic experiments and the many experts involved. However, developing these tailored companion resources requires programming expertise and knowledge of web server architecture-a substantial burden for most. Here, we describe Argonaut, a simple, code-free, and user-friendly platform for creating customizable, interactive data-hosting websites. Argonaut carries out real-time statistical analyses of the data, which it organizes into easily sharable projects. Collaborating researchers worldwide can explore the results, visualized through popular plots, and modify them to streamline data interpretation. Increasing the pace and ease of access to multi-omic data, Argonaut aims to propel discovery of new biological insights. We showcase the capabilities of this tool using a published multi-omics dataset on the large mitochondrial protease deletion collection.
研究人员现在正以惊人的速度，使用日益成熟的质谱技术生成大量多组学数据集，面临着 “大数据” 传播、可视化和探索的新挑战。方便的是，基于网络的数据门户适应了多组学实验的复杂性以及众多相关专家的需求。然而，开发这些量身定制的配套资源需要编程专业知识和网络服务器架构知识，这对大多数人来说是一项沉重的负担。在这里，我们介绍了Argonaut，这是一个简单、无需编码且用户友好的平台，用于创建可定制的交互式数据托管网站。Argonaut对数据进行实时统计分析，并将其组织成易于共享的项目。世界各地的合作研究人员可以探索通过流行图表可视化的结果，并对其进行修改以简化数据解释。Argonaut旨在加快获取多组学数据的速度并提高其便捷性，推动新生物学见解的发现。我们使用已发表的关于大型线粒体蛋白酶缺失集合的多组学数据集展示了该工具的功能。
