Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers

• Autor(a) principal: Galpert D.
• Data de Publicação: 2018
• Outros Autores: Fernández A., Herrera F., Antunes A., Molina-Ruiz R., Agüero-Chapin G.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/120462
• topic: Alignment; Classification (of information); Decision trees; Feature extraction; Forestry; Proteins; Yeast; Amino acid compositions; Classification approach; Classification performance; Classification quality; Imbalance datum; Orthologs; Similarity measure; Supervised classification; Big data; proteome; Saccharomyces cerevisiae protein; algorithm; decision tree; metabolism; procedures; protein database; Saccharomyces cerevisiae; sequence analysis; Algorithms; Databases, Protein; Decision Trees; Proteome; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Sequence Analysis, Protein

Background: The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. Results: The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were alignment-free features related to amino acid composition. Conclusions: The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures. However, the similarity of their classification performance to that of traditional ortholog detection methods encourages the evaluation of other alignment-free protein pair descriptors in future research. © 2018 The Author(s).