Badshah Irbaz I, Cutillas Pedro R
Centre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, UK.
Methods Mol Biol. 2025;2932:203-229. doi: 10.1007/978-1-0716-4566-6_11.
Pathway inference methods allow the mapping of biochemical networks, the discovery of signaling components, and the assignment of functions to understudied proteins and genes. Literature and automated text mining have been successfully used to reconstruct metabolic and signaling circuits, while gene regulatory networks may be inferred from gene expression data. As an alternative approach to map members of proliferative pathways, functional pathway inference analysis (FPIA) is based on the premise that genes producing similar phenotypes following perturbation across multiple cell lines belong to a common pathway. We have demonstrated this concept with the use of gene dependency datasets that allow the provision of probabilistic values of pathway membership for thousands of genes. Here, we provide a detailed protocol for the implementation of FPIA in the cordial R package. As an illustration of how FPIA may be used to identify new pathway members, we present a step-by-step description of its use for the investigation of genes functionally associated to PI3K and TP53.
通路推断方法可用于绘制生化网络、发现信号传导成分，以及为研究较少的蛋白质和基因赋予功能。文献和自动文本挖掘已成功用于重建代谢和信号传导回路，而基因调控网络可从基因表达数据中推断出来。作为绘制增殖通路成员的另一种方法，功能通路推断分析（FPIA）基于这样一个前提，即在多个细胞系中受到干扰后产生相似表型的基因属于共同通路。我们已经通过使用基因依赖性数据集证明了这一概念，该数据集可为数千个基因提供通路成员的概率值。在这里，我们提供了在“cordial”R包中实施FPIA的详细方案。作为FPIA如何用于识别新通路成员的示例，我们逐步描述了其用于研究与PI3K和TP53功能相关基因的用途。
