@misc{10481/55857,
year = {2018},
url = {http://hdl.handle.net/10481/55857},
abstract = {Abstract
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.},
organization = {This 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).},
publisher = {BMC (part of Springer Nature)},
keywords = {Ortholog detection},
keywords = {Pairwise protein similarity measures},
keywords = {Big data},
keywords = {Supervised classification},
keywords = {Imbalance data},
title = {Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers},
author = {Galpert, Deborah and Fernández, Alberto and Herrera Triguero, Francisco and Antunes, Agostinho and Molina-Ruiz, Reinaldo and Agüero-Chapin, Guillermin},
}