%0 Journal Article
%T Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks
%A Antonio G¨®mez
%A Juan Cedano
%A Isaac Amela
%A Antoni Planas
%A Jaume PiŁżol
%A Enrique Querol
%J BMC Systems Biology
%D 2011
%I BioMed Central
%R 10.1186/1752-0509-5-49
%X 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.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.Sequence similarity has proven to be useful for many years in attempting to annotate genomes [1,2]. A simple way to infer the possible function of a protein is to use an alignment procedure such as PSI-BLAST [3], to find possible homologues in sequence databases, such as UniProt [4]. However, sequence homology has its limitations. Only a fraction of newly discovered sequences have identifiable homologous genes in current databases, and its viability is limited to cases where substantial sequence similarity to annotated proteins can be found [5]. Moreover, the growing number of annotations extrapolated from sequence similarity is prone to errors [6-8], hence, new bioinformatics methods are developed to complement traditional sequence homology-based methods.The development of high throughput technologies has resulted in large amounts of predicted Protein-Protein Interaction networks (PPI) for different genomes and, subsequently, methods using PPI data for functional inference [6,9-12] have been developed. It has been demonstrated that we may be able to use the semantics of gene annotations [13,14] and that we can obtain greater precision to predict new annotations using Gene Ontology (GO) information inside PPI [9,10,15]. Several semantic similarity measures using the GO database have been applied to gene products annotated with high ratios of prediction accuracy [
%U http://www.biomedcentral.com/1752-0509/5/49