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Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques.

Department of Computer Architecture and Computer Technology, Department of Applied Mathematics, University of Granada (UGR), 18071 Granada, Medical Genome Project, Andalusian Human Genome Sequencing Centre (CASEGH), 41092 Seville and Chromatin and Disease Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet, Barcelona 08907, Spain.
Nucleic Acids Research (Impact Factor: 9.11). 10/2012; DOI: 10.1093/nar/gks919
Source: PubMed

ABSTRACT Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.

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Available from: Francisco Manuel Ortuño Guzmán, May 15, 2014
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