MTopGO: a tool for module identification in PPI Networks

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1 Clinical Scientific Institute Maugeri, Pavia, Italy, Pavia, Italy
2 Department of Informatics, Systems and Communication, University of Milan - Bicocca, Milan, Italy
3 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, United States
4 Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
5 Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, United States
6 BIO5 Institute, University of Arizona, Tucson, United states
7 Department of Medicine, University of Arizona, Tucson, United States
DOI
10.7287/peerj.preprints.3229v1
Published
Accepted
Subject Areas
Bioinformatics
Keywords
PPI Network, Module identification, Gene Ontology
Copyright
© 2017 Vella et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Vella D, Marini S, Vitali F, Bellazzi R. 2017. MTopGO: a tool for module identification in PPI Networks. PeerJ Preprints 5:e3229v1

Abstract

The increasing amount of -omics data leads to development of models to interpret and analyse them. A common approach consists in representing data as PPI Networks. These models can be very complex and informatics tools are needed to analyse them. In this abstract, we present MTopGO, an algorithm of module detection specific for PPI Network, exploiting both the network topological information and the Gene Ontology (GO) knowledge about network proteins. MTopGO output consists in a network partition, where each obtained cluster is labelled with a specific GO term describing its biological nature. In a single step, MTopGO performs a double PPI network analysis; from a topological perspective, through the individuation of a meaningful network partition and, from a biological perspective, through the selection of significant GO terms describing the biological role of network proteins.

Author Comment

This is an abstract which has been accepted for the NETTAB 2017 Workshop