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dc.contributor.authorGalpert, Deborah
dc.contributor.authorFernández, Alberto
dc.contributor.authorHerrera Triguero, Francisco 
dc.contributor.authorAntunes, Agostinho
dc.contributor.authorMolina-Ruiz, Reinaldo
dc.contributor.authorAgüero-Chapin, Guillermin
dc.identifier.citationGalpert, D. [et al.]. Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers. BMC Bioinformatics (2018) 19:166
dc.description.abstractAbstract 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.es_ES
dc.description.sponsorshipThis work was supported by the following financial sources: Postdoc fellowship (SFRH/BPD/92978/2013) granted to GACh by the Portuguese Fundação para a Ciência e a Tecnologia (FCT). AA was supported by the MarInfo – Integrated Platform for Marine Data Acquisition and Analysis (reference NORTE-01-0145-FEDER-000031), a project supported by the North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).es_ES
dc.publisherBMC (part of Springer Nature)es_ES
dc.rightsAtribución 3.0 España*
dc.subjectOrtholog detectiones_ES
dc.subjectPairwise protein similarity measureses_ES
dc.subjectBig dataes_ES
dc.subjectSupervised classificationes_ES
dc.subjectImbalance dataes_ES
dc.titleSurveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifierses_ES

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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España