Integrative network alignment reveals large regions of global network similarity in yeast and human. Motivation: High-throughput methods for detecting molecular interactions have produced large sets of biological network data with much more yet to come. Analogous to sequence alignment, efficient and reliable network alignment methods are expected to improve our understanding of biological systems. Unlike sequence alignment, network alignment is computationally intractable. Hence, devising efficient network alignment heuristics is currently a foremost challenge in computational biology. Results: We introduce a novel network alignment algorithm, called Matching-based Integrative GRAph ALigner (MI-GRAAL), which can integrate any number and type of similarity measures between network nodes (e.g. proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity and structural similarity. Hence, we resolve the ties in similarity measures and find a combination of similarity measures yielding the largest contiguous (i.e. connected) and biologically sound alignments. MI-GRAAL exposes the largest functional, connected regions of protein–protein interaction (PPI) network similarity to date: surprisingly, it reveals that 77.7% of proteins in the baker’s yeast high-confidence PPI network participate in such a subnetwork that is fully contained in the human high-confidence PPI network. This is the first demonstration that species as diverse as yeast and human contain so large, continuous regions of global network similarity. We apply MI-GRAAL’s alignments to predict functions of un-annotated proteins in yeast, human and bacteria validating our predictions in the literature. Furthermore, using network alignment scores for PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship. This is the first time that phylogeny is exactly reconstructed from purely topological alignments of PPI networks.

References in zbMATH (referenced in 3 articles )

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  1. Patsolic, Heather G.; Park, Youngser; Lyzinski, Vince; Priebe, Carey E.: Vertex nomination via seeded graph matching (2020)
  2. Ibragimov, Rashid; Malek, Maximilian; Guo, Jiong; Baumbach, Jan: GEDEVO: an evolutionary graph edit distance algorithm for biological network alignment (2013)
  3. Kuchaiev, Oleksii; Przulj, Natasa: Integrative network alignment reveals large regions of global network similarity in yeast and human (2011) ioport