Background All molecular functions and biological processes are carried out by groups of proteins that interact with each other. Metaproteomic data continuously generates new proteins whose molecular functions and relations must be discovered. A widely accepted structure to model functional relations between proteins are protein-protein interaction networks (PPIN), and their analysis and alignment has become a key ingredient in the study and prediction of protein-protein interactions, protein function, and evolutionary conserved assembly pathways of protein complexes. Several PPIN aligners have been proposed, but attaining the right balance between network topology and biological information is one of the most difficult and key points in the design of any PPIN alignment algorithm. Results Motivated by the challenge of well-balanced and efficient algorithms, we have designed and implemented AligNet, a parameter-free pairwise PPIN alignment algorithm aimed at bridging the gap between topologically efficient and biologically meaningful matchings. A comparison of the results obtained with AligNet and with the best aligners shows that AligNet achieves indeed a good balance between topological and biological matching. Conclusion In this paper we present AligNet, a new pairwise global PPIN aligner that produces biologically meaningful alignments, by achieving a good balance between structural matching and protein function conservation, and more efficient computations than state-of-the-art tools.

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In general, a phylogenetic network is a graphical representation of an evolutionary history that involves reticulate events like recombinations, hybridizations, or lateral gene transfers. Tree-child reticulate networks (TC networks) are a special class of phylogenetic networks that allow to represent evolutionary histories where, despite the existence of such reticulate events, every ancestral species has some descendant through mutations. In this paper we establish two equivalent characterizations of the families of clusters of TC networks. These characterizations yield a simple, polynomial-time algorithm that decides whether a given family of clusters on a set of taxa is the family of clusters of some TC network or not, and, when the answer is positive, outputs a TC network that is a minimal reticulate network representing this family of clusters. This algorithm is based on the notion of cluster network introduced by Huson and Rupp, and it has been implemented in a Python package and a companion web tool, which are freely available on the web.

One of the most difficult problems difficult problem in systems biology is to discover protein-protein interactions as well as their associated functions. The analysis and alignment of protein-protein interaction networks (PPIN), which are the standard model to describe protein-protein interactions, has become a key ingredient to obtain functional orthologs as well as evolutionary conserved pathways and protein complexes. Several methods have been proposed to solve the PPIN alignment problem, aimed to match conserved subnetworks or functionally related proteins. However, the right balance between considering network topology and biological information is one of the most difficult and key points in any PPIN alignment algorithm which, unfortunately, remains unsolved. Therefore, in this work, we propose AligNet, a new method and software tool for the pairwise global alignment of PPIN that produces biologically meaningful alignments and more efficient computations than state-of-the-art methods and tools, by achieving a good balance between structural matching and protein function conservation as well as reasonable running times.1 algorithms detected similarities between small subnetworks [16,20,22,23,26]. PathBLAST [16] is a tool developed to search for specific pathways in a PPIN. In contrast to PathBLAST, NetAlign [23] is a web-based tool designed to identify the conserved network substructures between two PPIN. The method used in this tool and in the algorithm presented in [26] is the matching of isomorphic subgraphs. The pairwise alignment method introduced in [20], MaWISh, produces a local alignment of two PPIN by evaluating the similarity of their graph structures through a scoring function that accounts for evolutionary events, while the algorithm introduced in [22] to align PPIN is based on both protein sequence similarity and network topology similarity, and it uses integer quadratic programming. In addition, a new and efficient approach to obtain multiple local alignments is described in [10], based on conserved functional modules. All these methods and algorithms are able to detect and obtain similarities between subnetworks. Actually, the aim of local alignment algorithms is to find regions with the same network structure in the networks under comparison. For every region in one network, an alignment with some region in the other network may be obtained, but it may happen that these local alignments are mutually inconsistent, because the same protein in one network may be matched by different local alignments to different proteins in the other network: then, as a final result, it may happen that these local alignments cannot be extended to a global alignment of the pair of PPIN.In contrast to these local alignment methods, a global alignment algorithm is aimed at finding the best overall alignment between whole PPIN [9]. A global alignment is then a matching mapping between the sets of proteins of two PPIN, in such a way that each protein in one network is matched to one, and only one, protein in the oth...