Multiple sequence alignments (MSAs) are currently one of the most powerful procedure in
bioinformatics in order to provide additional information useful to other understanding techniques
such as biological function analyses, structure predictions or next-generation sequencing.
Nevertheless, current MSA methodologies are providing quite different alignments for the same set of
sequences depending on some particular biological features of these sequences. For this reason, the selection of a suitable
tool for aligning a specific set of sequences is an important task which has not been totally solved yet. In this work, we
propose a hierarchical algorithm of several binary classifiers based on support vector machines (SVMs) to predict "a
priori" the MSA tool which will provide the most accurate alignment. Firstly, a set of heterogeneous biological features
related to each set of sequences are retrieved from well-known databases. Subsequently, those most significant features
according to each specific aligner are included in this particular classifier. Finally, the SVM classifiers are joined to
decide the most suitable method according to the quality of each classification. This procedure was assessed by the
benchmark BAliBASE v3.0 and compared against other similar tools, namely AlexSys and PAcAlCI.
Keywords: Feature extraction, feature selection, machine learning, multiple sequence alignments (MSAs), support vector
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