Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences.
National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Bioinformatics. 2020 May 1;36(9):2821-2828. doi: 10.1093/bioinformatics/btaa034.
Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking.
Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82-0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases.
Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.
Datasets are available as part of the Supplementary data.
Supplementary data are available at Bioinformatics online.
旨在突出条件之间网络变化的差异网络分析是网络生物学中的一个重要范例。然而，差异网络分析方法通常是为了比较两种情况而设计的，很少应用于多个蛋白质相互作用网络（相互作用组）。重要的是，缺乏针对它们的评估的大规模基准。
在这里，我们提出了一个评估通过比较多个人类组织相互作用组进行差异网络分析来突出组织选择性过程和疾病的能力的框架。为此，我们创建了一个包含 6499 个经过精心整理的组织特异性基因本体生物学过程的基准。我们应用了五种方法，包括四种差异网络分析方法，来构建 34 种组织的加权相互作用组。对该基准的严格评估表明，差异分析方法在揭示组织选择性过程方面表现良好（AUC 为 0.82-0.9）。接下来，我们应用差异网络分析来阐明导致组织选择性遗传疾病的基因。为此，我们整理了一个包含 1305 种组织特异性遗传疾病及其表现组织的数据集。关注疾病相关组织相互作用组中差异互动的前 1%的子网络，在 18.6%的病例中导致疾病的基因显著富集，血液、神经、肌肉和心脏疾病的成功率显著较高。
总之，我们提供了一个框架，其中包括广泛的手动整理的组织选择性过程和疾病数据集，可作为基准或阐明组织选择性过程和具有组织选择性功能和临床影响的基因。我们的结果表明，对多个人类组织相互作用组的差异分析是突出具有组织选择性功能和临床影响的过程和基因的有力工具。
数据集可作为补充数据的一部分获得。
补充数据可在生物信息学在线获得。
