Abstract
AbstractMotivation: High-throughput methods for detecting molecular interactions have lead to a plethora of biological network data with much more yet to come, stimulating the development of techniques for biological network alignment. Analogous to sequence alignment, efficient and reliable network alignment methods will improve our understanding of biological systems. Network alignment is computationally hard. Hence, devising efficient network alignment heuristics is currently one of the foremost challenges in computational biology. Results: We present a superior heuristic network alignment algorithm, called Matching-based GRAph ALigner (M-GRAAL), which can process and 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. This is efficient in resolving ties in similarity measures and in finding a combination of similarity measures yielding the largest biologically sound alignments. When used to align protein-protein interaction (PPI) networks of various species, M-GRAAL exposes the largest known functional and contiguous regions of network similarity. Hence, we use M-GRAAL’s alignments to predict functions of un-annotated proteins in yeast, human, and bacteria C. jejuni and E. coli. Furthermore, using M-GRAAL to compare PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship and our phylogenetic tree is the same as sequenced-based one.
Highlights
1.1 BackgroundMany complex systems can be represented using networks
We present a novel algorithm for global network alignment, called Matching-based GRAph ALigner (M-GRAAL), that outperforms all previous approaches
We demonstrate that M-GRAAL exposes large subnetworks common across species
Summary
Many complex systems can be represented using networks. The most prominent examples are biological, social, informational, physical, and transportation networks. Proteins almost never perform their functions alone, but they “cooperate” with other proteins by forming physical bonds and create huge networks of protein-protein interactions (PPIs) Understanding these complex networks is one of the foremost challenges of the post-genomic era. Local network alignment algorithms aim to find small subnetworks corresponding to pathways, or protein complexes, conserved in PPI networks of different species. Global network alignment algorithms based purely on network topology, called GRAAL (Kuchaiev et al, 2010) and H-GRAAL (Milenkovic et al, 2010), have been designed. They can align networks of any type, biological ones, since they do not rely on sequence similarity information between nodes. GRAAL is a seed-and-extend approach, while H-GRAAL is based on the Hungarian algorithm (Kuhn, 1955) for solving the assignment problem
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