BACKGROUND Omics-profiling is a collection of increasingly prominent approaches that result in large-scale biological datasets, for instance capturing an organism's behavior and response in an environment. It can be daunting to manually analyze and interpret such large datasets without some programming experience. Additionally, with increasing amounts of data; management, storage and sharing challenges arise. RESULTS Here, we present ShinyOmics, a web-based application that allows rapid collaborative exploration of omics-data. By using Tn-Seq, RNA-Seq, microarray and proteomics datasets from two human pathogens, we exemplify several conclusions that can be drawn from a rich dataset. We identify a protease and several chaperone proteins upregulated under aminoglycoside stress, show that antibiotics with the same mechanism of action trigger similar transcriptomic responses, point out the dissimilarity in different omics-profiles, and overlay the transcriptional response on a metabolic network. CONCLUSIONS ShinyOmics is easy to set up and customize, and can utilize user supplied metadata. It offers several visualization and comparison options that are designed to assist in novel hypothesis generation, as well as data management, online sharing and exploration. Moreover, ShinyOmics can be used as an interactive supplement accompanying research articles or presentations.

中文翻译：

---

ShinyOmics：组学数据的协作探索。


背景技术组学分析是产生大规模生物数据集的日益突出的方法的集合，例如捕获生物体在环境中的行为和响应。如果没有一些编程经验，手动分析和解释如此大的数据集可能会令人望而生畏。此外，随着数据量的增加；管理、存储和共享方面的挑战随之而来。结果在这里，我们展示了 ShinyOmics，这是一个基于网络的应用程序，可以快速协作探索组学数据。通过使用来自两种人类病原体的 Tn-Seq、RNA-Seq、微阵列和蛋白质组学数据集，我们举例说明了可以从丰富的数据集中得出的几个结论。我们鉴定了在氨基糖苷类应激下上调的蛋白酶和几种伴侣蛋白，表明具有相同作用机制的抗生素会触发相似的转录组反应，指出不同组学特征的差异，并将转录反应叠加在代谢网络上。结论 ShinyOmics 易于设置和定制，并且可以利用用户提供的元数据。它提供了多种可视化和比较选项，旨在帮助生成新假设以及数据管理、在线共享和探索。此外，ShinyOmics 可以用作研究文章或演示文稿的交互式补充。