De Maeyer Dries, Weytjens Bram, De Raedt Luc, Marchal Kathleen
Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, B-3001 Leuven, Belgium.
Genome Biol Evol. 2016 Jan 23;8(3):481-94. doi: 10.1093/gbe/evw010.
In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html.
在克隆系统中，从分子网络的角度解释驱动基因有助于理解这些驱动基因如何引发适应性表型。要获得这种基于网络的理解，取决于驱动基因的正确识别。在克隆系统中，独立进化的品系可以通过影响相同的分子途径获得相似的适应性表型，这种现象在分子途径水平上被称为平行性。这意味着成功的驱动基因识别取决于从分子网络的角度解释突变基因。因此，驱动基因识别和获得基于网络的适应性表型理解是两个相互混淆的问题，理想情况下应该同时解决。在本研究中，提出了一种基于网络的eQTL方法，该方法可以同时解决驱动基因识别和基于网络的解释问题。该方法以具有相似适应性表型的独立进化品系的耦合基因型-表达表型数据（eQTL数据）和特定生物体的全基因组相互作用网络作为输入。在途径水平上搜索突变一致性被定义为一个子网推断问题，该问题包括从全基因组相互作用网络中推断出一个子网，该子网能最好地将含有突变的基因与差异表达基因连接起来。根据与差异表达基因的连通性，将突变基因优先作为驱动基因。基于半合成数据和两个公开可用的数据集，我们展示了基于网络的eQTL方法在确定驱动基因优先级以及深入了解适应性表型背后的分子机制方面的潜力。该方法可在http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html获取。
