Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267-0056, USA.
Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221-0025, USA.
Bioinformatics. 2020 Sep 15;36(18):4781-4788. doi: 10.1093/bioinformatics/btaa590.
Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem.
We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail.
Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs.
Supplementary data are available at Bioinformatics online.
信号通路活性的失调是许多人类疾病的病因,调节信号通路的活性通常是首选的治疗策略。从靶向信号通路的角度理解生物活性化学物质的作用机制(MOA)是评估其治疗潜力的必要的第一步。信号通路活性的变化通常不会反映在通路基因表达的变化上,这使得从转录特征(TSes)推断 MOA 成为一个难题。
我们通过整合分析通路网络拓扑、基于通路基因遗传扰动的集成网络细胞特征库 TSes 和扰动的 TSes,开发了一种新的计算方法,用于暗示生物活性化学物质和其他细胞扰动的通路靶标。当当前仅利用扰动的 TSes 的通路分析方法失败时,我们的方法可以准确地预测扰动所靶向的信号通路。
可在 https://github.com/uc-bd2k/paslincs 上获得开源 R 包 paslincs。
补充数据可在生物信息学在线获得。