Agrawal Piyush, Hannenhalli Sridhar
Department of Medical Research, SRM Medical College Hospital & Research Centre, SRMIST, Kattankulathur, Chennai, India.
Cancer Data Science Lab, National Cancer Institute, NIH, Bethesda, MD, USA.
STAR Protoc. 2024 Dec 20;5(4):103472. doi: 10.1016/j.xpro.2024.103472. Epub 2024 Dec 4.
In a variety of biological contexts, characterizing genes associated with disease etiology and mediating global transcriptomic change is a key initial step. Here, we present a protocol to identify such key genes using our tool "PathExt," a tool that implements a network-based approach. We describe steps for installing libraries, preparing input data and detailed procedures for running PathExt, and characterizing differential pathways and key genes based on ripple centrality scores. For complete details on the use and execution of this protocol, please refer to Agrawal et al..
在各种生物学背景下,表征与疾病病因相关并介导全局转录组变化的基因是关键的第一步。在此,我们展示了一种使用我们的工具“PathExt”来识别此类关键基因的方案,“PathExt”是一种实施基于网络方法的工具。我们描述了安装库、准备输入数据的步骤以及运行PathExt的详细程序,以及基于涟漪中心性得分表征差异途径和关键基因的方法。有关此方案使用和执行的完整详细信息,请参考阿格拉瓦尔等人的研究。