UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction

Paolo Perlasca,Giorgio Valentini,Alessandro Petrini,Marco Mesiti,Elena Casiraghi,Marco Frasca,Jessica Gliozzo,Marco Notaro,Cheick Tidiane Ba,Giuliano Grossi

doi:10.1186/s12859-019-2959-2

Abstract

BackgroundOne of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate —in a function-specific fashion— the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships.ResultsWe address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks —e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks.ConclusionsUNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.

Highlights

One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources
The Unbalance-aware network integration and prediction (UNIPred) algorithm (Unbalance-aware Network Integration and Prediction) has been recently proposed [4]: it computes for each input network a function-specific informativeness score, which is used to build the consensus network
The extensive experimental results presented in [4, 6] showed that COst sensitive neural network (COSNet) and UniPred, the predictive algorithms used by UNIPred-WEB, compared favorably with a large set of state-of-the-art network-based methods, including e.g. GeneMANIA-SW [7], the classical label propagation algorithm [8], MS-kNN, one of the top-ranked methods in the recent Critical assessment of functional annotation (CAFA) challenge [1], and the eight best methods of the MouseFunc challenge [9]

Summary

Introduction

One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The visualization starts from a target protein selected by the user, and it allows to interactively personalize the resulting subgraph —the user can expand or reduce the graph size, move nodes, see information associated with nodes and edges, and apply different visualization options (see “Visual analysis and exploration of the integrated networks” section).

Results

Conclusion