An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm

Abstract

One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality.

Keywords: bioinformatics; crossover operator; genetic algorithm; multiple sequence alignment; mutation operator.

Table 1. Summary of the test results of proposed method

Table 2. Average Computation Times(s) comparison over Ref. 1, 2, 3, 4 and 5

Table 3. Experimental results with Ref. 1 datasets of BAliBase 2.0

Table 4. Experimental results with Ref. 3 datasets of BAliBase 2.0

Table 5. Experimental results with Ref. 2 datasets of BAliBase 2.0

Table 6. Performance evaluation of the proposed algorithm with hill climbing approach and randomly generated population through guide tree

Figure 1. Example of a multiple sequence alignment

Figure 2. One point crossover I

Figure 3. One point crossover II

Figure 4. Exchange Mutation operator

Figure 5. Reverse Mutation operator

Figure 6. Position mutation operator

Figure 7. Inverse mutation operator

Figure 8. Bar graph comparison result of scores between proposed and other methods over Ref. 1

Figure 9. Bar graph comparison result of scores between proposed and other methods over Ref. 1

Figure 10. Bar graph comparison result of scores between proposed and other methods over Ref. 3

Figure 11. Bar graph comparison result of scores between proposed and other methods over Ref. 3

Figure 12. Bar graph comparison result of scores between proposed and other methods over Ref. 2

Figure 13. Average score comparison between proposed and other methods over Ref. 1

Figure 14. Average score comparison between proposed and other methods over Ref. 3

Figure 15. Average score comparison between proposed and other methods over Ref. 2