Global multiple protein-protein interaction network alignment by combining pairwise network alignments.
Global multiple protein-protein interaction network alignment by combining pairwise network alignments.
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DOI：
10.1186/1471-2105-16-s13-s11
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发表时间：
2015
影响因子：
3
通讯作者：
Singh R
中科院分区：
文献类型：
--
作者：
Dohrmann J;Puchin J;Singh R
近年来，大量的蛋白质相互作用数据已经成为可能，因此迫切需要强大的分析技术。在这种情况下，寻找不同蛋白质-蛋白质相互作用网络（PPIN）之间具有生物学意义的对应关系的问题特别令人感兴趣。一个物种的PPIN可以通过PPIN比对过程与其他物种进行比较。这种比对可以提供对物种进化和网络组件功能确定等基本问题的见解，以及对靶标识别和疾病传播机制阐明等转化问题的见解。此外，多个PPINs可以同时对齐，扩大了结果的分析意义。虽然有几种网络配对对齐算法，但很少有方法能够实现多网络对齐。我们提出了SMAL，一种基于支架对齐原理的MNA算法。SMAL能够在线性时间内将任何全局配对对齐算法的结果转换为MNA。使用这种方法，我们基于组合来自许多公开可用的（成对）网络对齐器的成对对齐来构建多个网络对齐。我们使用来自完整储存库的8个物种的PPINs测试了small，并采用了许多措施来评估性能。此外，作为实验研究的一部分，我们比较了SMAL在对齐多达8个输入ppin时的有效性，并检查了支架网络选择对对齐的影响。small的一个关键优势在于它能够通过使用原生MNA实现不存在的成对网络对齐器来创建MNA。实验表明，SMAL的性能可与现有方法（如IsoRankN和SMETANA）的本机MNA实现相媲美。但是，就计算时间而言，small要快得多。SMAL还能够保留原生配对的许多重要特征，如对齐节点和边的数量，以及对齐节点的功能和同源相似性。速度、灵活性和在对齐新网络时保留先前通信的能力，使small成为对齐多个大型网络的令人信服的选择。
A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster. SMAL was also able to retain many important characteristics of the native pairwise alignments, such as the number of aligned nodes and edges, as well as the functional and homologene similarity of aligned nodes. The speed, flexibility and the ability to retain prior correspondences as new networks are aligned, makes SMAL a compelling choice for alignment of multiple large networks.