title: A Factor Graph Approach to Automated GO Annotation.
abstract: As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.
authors.affiliation: CIFASIS-Conicet Institute, Rosario, Argentina.|Facultad de Cs. Exactas, Ingeniería y Agrimensura, National University of Rosario, Rosario, Argentina.
authors.forename: Flavio E
authors.initials: FE
authors.lastname: Spetale
authors.affiliation: CIFASIS-Conicet Institute, Rosario, Argentina.|Facultad de Cs. Exactas, Ingeniería y Agrimensura, National University of Rosario, Rosario, Argentina.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Tapia
authors.affiliation: CIFASIS-Conicet Institute, Rosario, Argentina.|Facultad Regional San Nicolás, National Technological University, San Nicolás, Argentina.
authors.forename: Flavia
authors.initials: F
authors.lastname: Krsticevic
authors.affiliation: CIFASIS-Conicet Institute, Rosario, Argentina.
authors.forename: Fernando
authors.initials: F
authors.lastname: Roda
authors.affiliation: CIFASIS-Conicet Institute, Rosario, Argentina.|Facultad de Cs. Exactas, Ingeniería y Agrimensura, National University of Rosario, Rosario, Argentina.|Facultad Regional San Nicolás, National Technological University, San Nicolás, Argentina.
authors.forename: Pilar
authors.initials: P
authors.lastname: Bulacio
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D019295:Computational Biology; D004331:Drosophila melanogaster / Q000235:genetics*; D063990:Gene Ontology; D018551:Solanum lycopersicum / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software