Graemlin: General and robust alignment of multiple large interaction networks
Graemlin: General and robust alignment of multiple large interaction networks
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DOI：
10.1101/gr.5235706
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发表时间：
2006-09-01
期刊：
影响因子：
7
通讯作者：
Batzoglou, Serafim
中科院分区：
文献类型：
--
作者：
Flannick, Jason;Novak, Antal;Batzoglou, Serafim
最近蛋白质相互作用网络的激增激发了对网络配对的研究：保守功能模块的跨物种比较。之前的研究已经为这种比较奠定了基础，并在一组精选的稀疏互动网络上展示了它们的力量。然而，最近，新的计算技术已经产生了数百个预测的相互作用网络，其互连密度将现有的比对算法推向了极限。为了在这些新的网络中找到保守的功能模块，我们开发了第一个能够扩展多个网络对齐的算法Gr ae Mlin。Gr ae Mlin的功能进化的显式模型允许对现有的比对评分方案进行泛化，并且可以定位除蛋白质复合体和代谢途径之外的保守网络拓扑。为了评估Gr ae Mlin的性能，我们开发了第一个网络比对的定量基准，这些基准允许比较算法概括保守功能模块的KEGG数据库的能力。我们发现，与以前的方法相比，Gr ae Mlin在提高敏感度的同时获得了显著的可伸缩性。
The recent proliferation of protein interaction networks has motivated research into network alignment: the cross-species comparison of conserved functional modules. Previous studies have laid the foundations for such comparisons and demonstrated their power on a select set of sparse interaction networks. Recently, however, new computational techniques have produced hundreds of predicted interaction networks with interconnection densities that push existing alignment algorithms to their limits. To find conserved functional modules in these new networks, we have developed Gr ae mlin, the first algorithm capable of scalable multiple network alignment. Gr ae mlin's explicit model of functional evolution allows both the generalization of existing alignment scoring schemes and the location of conserved network topologies other than protein complexes and metabolic pathways. To assess Gr ae mlin's performance, we have developed the first quantitative benchmarks for network alignment, which allow comparisons of algorithms in terms of their ability to recapitulate the KEGG database of conserved functional modules. We find that Gr ae mlin achieves substantial scalability gains over previous methods while improving sensitivity.