Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.
Nucleic Acids Res. 2020 Sep 25;48(17):e98. doi: 10.1093/nar/gkaa639.
We present NetCore, a novel network propagation approach based on node coreness, for phenotype-genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.
我们提出了 NetCore，一种基于节点核心度的新型网络传播方法，用于表型-基因型关联和模块识别。NetCore 通过在带重启动的随机游走过程中使用节点核心度来解决 PPI 网络中的节点度偏差问题，并在传播后实现了基因的重新排序。此外，NetCore 实现了一种半监督方法来识别与表型相关的网络模块，该方法将已知与表型相关的基因锚定为新的候选基因的识别。我们在 11 个不同的 GWAS 性状的基因集上评估了 NetCore，并通过交叉验证显示出与标准基于度的网络传播相比性能有所提高。此外，我们将 NetCore 应用于识别精神分裂症 GWAS 数据和泛癌突变数据的疾病基因和模块。我们将新方法与现有的网络传播方法进行了比较，并显示了与其他方法相比使用 NetCore 的优势。我们提供了一个易于使用的实现，以及一个从 ConsensusPathDB 提取的高可信度 PPI 网络，可应用于各种类型的基因组学数据，以获得基因和功能相关的网络模块的重新排序。
