Wilson Stephen J, Wilkins Angela D, Lin Chih-Hsu, Lua Rhonald C, Lichtarge Olivier
Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA,
Pac Symp Biocomput. 2017;22:336-347. doi: 10.1142/9789813207813_0032.
Advances in cellular, molecular, and disease biology depend on the comprehensive characterization of gene interactions and pathways. Traditionally, these pathways are curated manually, limiting their efficient annotation and, potentially, reinforcing field-specific bias. Here, in order to test objective and automated identification of functionally cooperative genes, we compared a novel algorithm with three established methods to search for communities within gene interaction networks. Communities identified by the novel approach and by one of the established method overlapped significantly (q < 0.1) with control pathways. With respect to disease, these communities were biased to genes with pathogenic variants in ClinVar (p ≪ 0.01), and often genes from the same community were co-expressed, including in breast cancers. The interesting subset of novel communities, defined by poor overlap to control pathways also contained co-expressed genes, consistent with a possible functional role. This work shows that community detection based on topological features of networks suggests new, biologically meaningful groupings of genes that, in turn, point to health and disease relevant hypotheses.
细胞、分子和疾病生物学的进展依赖于基因相互作用和通路的全面表征。传统上，这些通路是人工策划的，这限制了它们的有效注释，并且可能强化特定领域的偏差。在这里，为了测试功能协同基因的客观和自动识别，我们将一种新算法与三种既定方法进行了比较，以在基因相互作用网络中搜索群落。通过新方法和一种既定方法识别出的群落与对照通路有显著重叠（q < 0.1）。在疾病方面，这些群落偏向于ClinVar中有致病变异的基因（p ≪ 0.01），并且通常来自同一群落的基因是共表达的，包括在乳腺癌中。由与对照通路重叠较少定义的新群落的有趣子集也包含共表达基因，这与可能的功能作用一致。这项工作表明，基于网络拓扑特征的群落检测提出了新的、具有生物学意义的基因分组，进而指向与健康和疾病相关的假设。
