Local graph alignment and motif search in biological networks
Local graph alignment and motif search in biological networks
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DOI：
10.1073/pnas.0305199101
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发表时间：
2004-10-12
影响因子：
11.1
通讯作者：
Lässig, M
中科院分区：
文献类型：
--
作者：
Berg, J;Lässig, M
相互作用网络在后基因组分子生物学中具有核心重要性，越来越多的数据可以通过高通量方法获得。例如基因调控网络或蛋白质相互作用图谱。分析这些数据的主要挑战是从网络的拓扑结构中读取生物功能。拓扑基序，即，在网络的不同位置重复出现的模式，最近已被确定为分子信息处理的基本模块。在这篇文章中，我们讨论了从相似但不一定相同的模式家族中衍生出来的图案。我们建立了一个统计模型的发生，这样的图案，从中我们得到一个评分函数的统计意义。基于这个评分函数，我们开发了一个搜索算法的拓扑图案称为图对齐，一个程序与一些类比序列比对。将该算法应用于大肠杆菌基因调控网络。
Interaction networks are of central importance in postgenomic molecular biology, with increasing amounts of data becoming available by high-throughput methods. Examples are gene regulatory networks or protein interaction maps. The main challenge in the analysis of these data is to read off biological functions from the topology of the network. Topological motifs, i.e., patterns occurring repeatedly at different positions in the network, have recently been identified as basic modules of molecular information processing. in this article, we discuss motifs derived from families of mutually similar but not necessarily identical patterns. We establish a statistical model for the occurrence of such motifs, from which we derive a scoring function for their statistical significance. Based on this scoring function, we develop a search algorithm for topological motifs called graph alignment, a procedure with some analogies to sequence alignment. The algorithm is applied to the gene regulation network of Escherichia coli.