by Antonio Gómez, Juan Cedano, Isaac Amela, Antoni Planas, Jaume Piñol, Enrique Querol
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.
Reference:
Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks (Antonio Gómez, Juan Cedano, Isaac Amela, Antoni Planas, Jaume Piñol, Enrique Querol), In BMC Systems Biology, BioMed Central Ltd, volume 5, 2011.
Bibtex Entry:
@article{Gomez2011,
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.},
author = {G\'{o}mez, Antonio and Cedano, Juan and Amela, Isaac and Planas, Antoni and Pi\~{n}ol, Jaume and Querol, Enrique},
doi = {10.1186/1752-0509-5-49},
issn = {1752-0509},
journal = {BMC Systems Biology},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
number = {1},
pages = {49},
publisher = {BioMed Central Ltd},
title = {{Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks}},
url = {http://www.biomedcentral.com/1752-0509/5/49},
volume = {5},
year = {2011}
}