Li Wenyuan, Dai Chao, Liu Chun-Chi, Zhou Xianghong Jasmine
Program in Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
J Comput Biol. 2012 Jun;19(6):710-30. doi: 10.1089/cmb.2012.0025.
Current network analysis methods all focus on one or multiple networks of the same type. However, cells are organized by multi-layer networks (e.g., transcriptional regulatory networks, splicing regulatory networks, protein-protein interaction networks), which interact and influence each other. Elucidating the coupling mechanisms among those different types of networks is essential in understanding the functions and mechanisms of cellular activities. In this article, we developed the first computational method for pattern mining across many two-layered graphs, with the two layers representing different types yet coupled biological networks. We formulated the problem of identifying frequent coupled clusters between the two layers of networks into a tensor-based computation problem, and proposed an efficient solution to solve the problem. We applied the method to 38 two-layered co-transcription and co-splicing networks, derived from 38 RNA-seq datasets. With the identified atlas of coupled transcription-splicing modules, we explored to what extent, for which cellular functions, and by what mechanisms transcription-splicing coupling takes place.
当前的网络分析方法都聚焦于一种或多种相同类型的网络。然而，细胞是由多层网络（例如转录调控网络、剪接调控网络、蛋白质-蛋白质相互作用网络）组织而成的，这些网络相互作用并相互影响。阐明这些不同类型网络之间的耦合机制对于理解细胞活动的功能和机制至关重要。在本文中，我们开发了第一种用于跨多个两层图进行模式挖掘的计算方法，其中两层代表不同类型但相互耦合的生物网络。我们将识别网络两层之间频繁耦合簇的问题表述为基于张量的计算问题，并提出了一种有效的解决方案来解决该问题。我们将该方法应用于从38个RNA测序数据集衍生而来的38个两层共转录和共剪接网络。借助已识别的转录-剪接耦合模块图谱，我们探究了转录-剪接耦合在何种程度上、针对哪些细胞功能以及通过何种机制发生。
