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Open Access Research article

Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

Antonio Gómez123, Juan Cedano1, Isaac Amela1, Antoni Planas3, Jaume Piñol1 and Enrique Querol1*

Author Affiliations

1 Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular. Universitat Autònoma de Barcelona. 08193 Bellaterra, Barcelona. Spain

2 Department of Systems Biology, Universitat de Vic, Vic, Spain

3 Laboratory of Biochemistry, Institut Químic de Sarrià, Universitat Ramon Llull, 08017 Barcelona, Spain

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BMC Systems Biology 2011, 5:49  doi:10.1186/1752-0509-5-49

Published: 12 April 2011

Abstract

Background

Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful.

Results

In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium.

Conclusions

To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.