Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
Institute of Biosciences and Medical Technologies (BioMediTech), Tampere, Finland.
Bioinformatics. 2018 Jun 15;34(12):2136-2138. doi: 10.1093/bioinformatics/bty063.
Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps.
INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM.
Supplementary data are available at Bioinformatics online.
通过基于网络的方法来检测和解释基因表达数据中的响应模块是一项常见但繁琐的任务。它通常需要应用几种在不同软件包中实现的计算方法，迫使生物学家编写复杂的分析管道。在这里，我们介绍了 INfORM（网络响应模块推断），这是一个 R shiny 应用程序，它使非专家用户能够检测、评估和选择具有高统计和生物学意义的基因模块。INfORM 是一种用于从使用多种算法推断的共识基因网络中识别具有生物学意义的响应模块的综合工具。它可以通过直观的图形用户界面访问，使用户能够从计算步骤中抽象出来。
INfORM 可在 https://github.com/Greco-Lab/INfORM 上免费供学术使用。
补充数据可在 Bioinformatics 在线获得。
