Department of Surgical and Medical Sciences, University Magna Graecia, Catanzaro, 88100 Italy.
Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications (iCeNSA), ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.
Brief Bioinform. 2018 May 1;19(3):472-481. doi: 10.1093/bib/bbw132.
Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.
类似于基因组序列比对,它允许在保守序列区域之间跨物种传递生物知识,生物网络比对可用于指导不同物种的分子网络的保守区域之间的知识转移。因此,生物网络比对可以用来将基于序列的同源性的传统概念重新定义为基于网络的同源性的新概念。类似于基因组序列比对,存在局部和全局生物网络比对。在这里,我们调查了每种网络比对类型的突出和最新的计算方法,并讨论了它们的(优缺点)。然后,由于最近表明这两种方法类型是互补的,因为它们捕获了细胞功能的不同方面,因此我们讨论了协调这两种网络比对类型的必要性,并提出了朝着这个方向迈出的第一步。我们以该主题的一些开放性研究问题结束,并评论了网络比对在计算生物学以外的其他领域的有用性。