Abstract
BackgroundThe discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users’ test datasets.ResultsHere, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses.ConclusionWebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users’ test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks.Availabilityhttp://www.nwpu-bioinformatics.com/WebNetCoffee
Highlights
The discovery of functionally conserved proteins is a tough and important task in system biology
Global network alignment is an efficient framework to systematically identify functionally conserved proteins from different species. It aims to search for an optimal global node map for all nodes in different protein-protein interaction (PPI) networks
To make the task of global alignment for multiple networks (GAMN) easier to be done for non-expert users, here, we present a web server WebNetCoffee based on the NetCoffee algorithm, which can fast and accurately search for a global node map for multiple PPI networks
Summary
Since proteins in different sources labeled by different identifiers, we convert all different sources identifiers into Uniprot identifier in the format acession:version, which is commonly used in many famous databases such as IntAct, QuikGO [32], UniProtKB/Swiss-Prot [33] and the NCBI protein databases [34]. This identifiers were further used to query the GO annotation in our web tools. The package of BLASTP [35] was performed to
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