Motivation: Omics data, such as transcriptomics or phosphoproteomics, are broadly used to get a snap-shot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signaling networks. Results: We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of several omics data, either from bulk or single-cell experiments. FUNKI also features different options to visualise the results and run post-analyses, and is mirrored as a scripted version in R. Availability: FUNKI is a free and open-source application built on R and Shiny, available in GitHub at https://github.com/saezlab/ShinyFUNKI under GNU v3.0 license and accessible also in https://saezlab.shinyapps.io/funki/ Contact: pub.saez@uni-heidelberg.de Supplementary information: We provide data examples within the app, as well as extensive information about the different variables to select, the results, and the different plots in the help page.

中文翻译：

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FUNKI：基于交互功能足迹的组学数据分析

动机：组学数据，例如转录组学或磷酸蛋白质组学，被广泛用于获取细胞分子状态的快照。特别是，组学的变化可用于基于已知的受调控目标（我们称为足迹）来估计通路、转录因子和激酶的活性。然后可以使用大型信号网络上的因果推理来估计驱动这些活动的分子路径。结果：我们开发了 FUNKI，一种用于足迹分析的功能性工具包。它提供了一个用户友好的界面，可以轻松快速地分析来自批量或单细胞实验的多个组学数据。FUNKI 还具有不同的选项来可视化结果和运行后分析，并在 R 中镜像为脚本版本。 可用性：FUNKI 是一个基于 R 和 Shiny 构建的免费开源应用程序，