Global multiple protein-protein interaction network alignment by combining pairwise network alignments

Abstract

BackgroundA 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.ResultsWe 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.ConclusionsA 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.

Pairwise Versus Multiple Global Network Alignment

“…We continue our discussion from Section 2.4.2 of the main paper on how we combine the pairwise alignments over every network pair in the given set into a multiple alignment, i.e., how we produce alignments from the ME-P-P and ME-M-P categories. is procedure was inspired by Dohrmann et al (2015). Given pairwise alignments of k networks G 1 (V 1 , E 1 Dohrmann et al (2015) produce a multiple alignment of the k networks as follows.…”

“…is procedure was inspired by Dohrmann et al (2015). Given pairwise alignments of k networks G 1 (V 1 , E 1 Dohrmann et al (2015) produce a multiple alignment of the k networks as follows. First, they select a "scaffold" network G r among the k networks, namely the network whose sum of "topological similarities" to the remaining k − 1 networks is maximized; one of the suggested "topological similarity" measures is Graphlet Degree Distribution (GDD) agreement (Pržulj, 2007).…”

“…However, since both PNA and MNA have the same goal of acrossspecies knowledge transfer, we argue that there is a need to compare them. is is especially true because early evidence suggests that aligning each pair of considered networks via PNA and then combining the pairwise alignments into a multiple alignment spanning all of the networks can be superior to directly aligning all networks via MNA (Dohrmann et al, 2015).We use five network sets: one synthetic network set with known true node mapping, and four real-world network sets with unknown true node mapping. For each network, we use only its largest connected component.…”

Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution

“…Indeed, these graphs can: (i) provide a systematic description of both composite and component genes in genomes (and metagenomes); (ii) be used to polarize fusion and fission events (by comparing the taxonomical distribution of genes hosts in associated component and composite gene families); (iii) be directly used to compare the relative conservation of overlapping component and composite sequences, for example, to determine whether domains found in different combination have different rates of evolution. The detection of composite genes using sequence-similarity networks can further contribute to understanding the rules of evolution of other biological networks, such as protein–protein interaction networks [56] . For instance, when, as a result of exon- or domain-shuffling, composite genes produce novel combinations of domains of interaction, composite genes can introduce novel nodes and edges in protein–protein interactions.…”

Proceedings of the 2015 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference

“…The manuscript by Dohrmann et al . [ 9 ] addresses and important issue of multiple network alignment, which is broadly useful for comparing pairwise networks derived from different sources. The authors developed a Scaffold-based Multiple Network Aligner (SMAL), which uses a star-like alignment topology and does not rely on native multiple network alignment implementations.…”