IGLOO：整合全局和局部生物网络对齐
IGLOO: Integrating global and local biological network alignment
摘要：
与基因组序列比对类似，生物网络比对（NA）旨在发现不同物种的分子网络（而不是序列）之间的相似区域。NA可以是本地（LNA）或全局（GNA）。LNA旨在识别高度保守的公共子网络，这些子网络通常很小，而GNA旨在识别大型公共子网络，这些子网络通常是次优保守的。我们最近发现LNA和GNA产生互补的结果：LNA具有高功能但低拓扑对齐质量，而GNA具有高拓扑但低功能对齐质量。因此，我们提出了IGLOO，一种新的方法，集成GNA和LNA，希望能够调和两者。我们针对最先进的LNA（NetworkBLAST，NetAligner，AlignNemo和AlignMCL）和GNA（GHOST，NETAL，GEDEVO，MAGNA++，WAVE和L-GRAAL）方法评估IGLOO。我们表明，IGLOO允许拓扑和功能比对质量之间的权衡优于我们的研究中考虑的现有LNA和GNA方法。
英文摘要：
Analogous to genomic sequence alignment, biological network alignment (NA) aims to find regions of similarities between molecular networks (rather than sequences) of different species. NA can be either local (LNA) or global (GNA). LNA aims to identify highly conserved common subnetworks, which are typically small, while GNA aims to identify large common subnetworks, which are typically suboptimally conserved. We recently showed that LNA and GNA yield complementary results: LNA has high functional but low topological alignment quality, while GNA has high topological but low functional alignment quality. Thus, we propose IGLOO, a new approach that integrates GNA and LNA in hope to reconcile the two. We evaluate IGLOO against state-of-the-art LNA (NetworkBLAST, NetAligner, AlignNemo, and AlignMCL) and GNA (GHOST, NETAL, GEDEVO, MAGNA++, WAVE, and L-GRAAL) methods. We show that IGLOO allows for a trade-off between topological and functional alignment quality better than the existing LNA and GNA methods considered in our study.
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