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

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.

Figure 1.

PAcAlCI scheme. The architecture is developed into four modules: input dataset, feature extraction, feature selection and LS-SVM prediction.

Figure 2.

Evolution of the MRE. The number of features progressively increases in ascendant relevance order. Training and test errors are shown.

Figure 3.

Distribution of relative errors for training and test sets. The corresponding LS-SVM prediction was performed using 10 features.

Figure 4.

Distribution of relative errors for training and test sets. Low accuracies were previously filtered to improve the LS-SVM prediction, avoiding prediction with high errors ().

Figure 5.

Intersection of suitable and predicted methodologies (Venn diagrams) corresponding to the four alignments whose accuracies are shown in Table 3.