IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka 560012, India.
Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India.
Bioinformatics. 2021 Jun 9;37(9):1254-1262. doi: 10.1093/bioinformatics/btaa941.
Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs.
We provide a computational tool-PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls.
The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt.
Supplementary data are available at Bioinformatics online.
转录组通常用于确定特定表型相关的基因。目前的方法主要集中在差异表达基因（DEGs）上，尽管人们认识到表型是通过基因和蛋白质之间的相互作用网络产生的，其中许多基因和蛋白质可能没有差异表达。此外，许多实际应用缺乏足够的样本或适当的对照来稳健地识别具有统计学意义的 DEGs。
我们提供了一种计算工具-PathExt，与差异基因不同，当有对照时，它可以识别差异活跃的路径，否则则识别最活跃的路径，在一个整合的组学生物网络中。包含这些路径的子网络，称为 TopNet，捕获了特定生物学背景下最相关的基因和过程。TopNet 形成了一个连通良好的图，反映了生物系统中的紧密协调。PathExt 的两个关键优势是：(i) 即使只有一个样本可用，它也可以提取特征基因和途径；(ii) 即使没有适当的对照，它也可以用于研究系统。我们通过两个不同的案例研究来证明 PathExt 的实用性，以描述：(i) 结核分枝杆菌暴露于 18 种抗菌药物后的反应，每种暴露只有一个转录组样本；(ii) 使用 39 个人组织的转录组数据来描述组织相关基因和过程。总的来说，PathExt 是一种通用的工具，可以在任何有兴趣的条件下，对任何组学整合的生物网络中的相关基因进行优先级排序，即使只有一个样本或没有适当的对照。
PathExt 的源代码可在 https://github.com/NarmadaSambaturu/PathExt 上获得。
补充数据可在生物信息学在线获得。
