Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany ; Network Analysis and Graph Theory, Technical University of Kaiserslautern, Kaiserslautern, Germany.
PLoS One. 2013 Sep 9;8(9):e73413. doi: 10.1371/journal.pone.0073413. eCollection 2013.
Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.
近年来高通量、多重分析技术的发展，已经启动了多个项目，这些项目系统地研究了生物网络中两种类型的成分（例如转录因子和启动子序列，或 microRNA(microRNAs) 和 mRNAs）之间的相互作用。从网络生物学的角度来看，这种筛选方法主要试图阐明两种不同类型的生物成分之间的关系，这些关系可以表示为二分图中节点之间的边。然而，不仅要确定不同类型节点之间的调节关系，而且要了解同一类型节点的连接模式，这通常是可取的。特别有趣的是同一类型的两个节点的共现，即它们共同邻居的数量，这是当前高通量筛选分析无法解决的问题。共现给出了这两种生物成分以相同方式受到影响的情况数量。在这里，我们提出了 SICORE，这是一种基于网络的新方法，用于检测具有统计学意义的共现的节点对。我们首先在人工数据集上展示了该方法的稳定性：当随机添加和删除观察值时，即使在大规模实验中超过预期水平的噪声下，我们也能获得可靠的结果。随后，我们基于对蛋白质组筛选数据集的分析，说明了该方法的可行性，以揭示 EGFR 驱动的细胞周期信号系统中靶向蛋白质的人类 microRNAs 的调节模式。由于统计上显著的共现可能表明功能协同作用和 canalization 的机制，因此在药物靶标识别和治疗开发中具有很大的潜力，我们提供了一个独立于平台的 SICORE 实现，带有图形用户界面，作为高通量筛选分析的新工具。
