CHAPTER 5 COMPARATIVE ANALYSIS WITH EXISTING ALGORITHMS

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1 CHAPTER 5 COMPARATIVE ANALYSIS WITH EXISTING ALGORITHMS This chapter presents a comparative analysis of the results produced by the developed self-organizing genetic algorithms with the existing algorithms, applied to Multiple Sequence Alignment. The developed algorithms were validated and various parameters, alignments generated were compared with other existing GA and non- GA algorithms for MSA (Sedaghatinia et al., 2009), (Essoussi et al., 2008) (Gondro et al., 2007). 5.1 EVALUATION OF ALIGNMENTS To prove the efficiency of the proposed algorithms, the alignment results are evaluated by comparing with widely used MSA tools like ClustalW (Thompson et al., 1994), T-Coffee (Noterdame et al., 2000), DiAlign (Morgenstern et al., 1996), Muscle (Edgar, 2004), Mafft (Katoh et al., 2009), Praline (Simossis et al., 2003) and POA (Lee et al., 2002). In addition, a comparison is made among the various developed algorithms, listed below, with reference to GA parameters and alignments. Standard Genetic Algorithm (SGA) Intron Multiple Aligner by Genetic Algorithm (imaga) Self-organizing Algorithm for Multiple Sequence Alignment (SOMSA) Cyclic Algorithm for Multiple Sequence Alignment (C-MSA) 112

2 Auto-Poietic Algorithms for Multiple Sequence Alignment (AP-MSA) Self-organizing Multi-Mutation Operator Algorithm (SOMM-MSA) Further, three variations of SO algorithms are also developed for comparative analysis and the results obtained are highly encouraging and are presented below: Self-organizing Cyclic Crossover (SOCCO) SOCCO is a combination of the developed Cyclic Crossover Operator (CCO) and Self- organizing Binary Shuffler (SOBS). Auto-Poietic Crossover with Cyclic Mutation (ACOSMO) ACOSMO is a combination of the developed Auto-Poietic Crossover Operator (ACO) and Cyclic Self-organizing Binary Shuffler (C-SOBS). SOMSA with Decreasing Mutation Rate (DSOMSA) DSOMSA is a combination of the developed SOCO operator and a variation of C- SOBS. The variant of C-SOBS operator performs mutation initially with an optimal upper limit in the rate range. At each point of convergence instead of termination, the rate is decreased cyclically till it reaches the optimal lower limit Reference Datasets Multiple sequence alignments are the heart of Bioinformatics analysis. A number of benchmarks (BAliBASE (Thompson et al., 2005), OXBench (Raghava et al., 2003), SMART (Ponting et al., 1999)) are available to evaluate the MSA programs (Carroll et al, 2007). These databases leverage structural-alignments to provide a suite of gold standard alignments. They are assumed to be true alignments, and the calculated alignments are evaluated by comparing against them. The publicly available databases are used as the benchmark which is extremely useful in evaluating the quality of sequence alignments. 113

3 5.1.2 MSA Assessment s The performance of MSA programs were evaluated using different scores like Q- score, TC score, Cline score and Modeler score (Mirarab et al., 2011). The statistical analysis of performance of MSA programs involves the comparison of scores achieved in a specific reference set or by the scores of a specific MSA program in different reference set. Q- Q (quality) is the number of correctly aligned residue pairs divided by the number of residue pairs in the reference alignment. Column (TC) Using the same symbols as above, the score C i of the i th column is equal to 1 when all the residues in the column are aligned in the reference alignment, otherwise is equal to 0. Therefore, the column score is: Cline The Cline score was developed to address the issues with and by penalizing both under-alignment and over-alignment, and also crediting regions in the generated alignment that may be shifted by a few positions relative to the reference alignment. 114

4 Modeler The modeler score computes the ratio of correctly aligned residue-pairs with the length of the generated alignment. 5.2 ANALYSIS OF VARIANTS OF SELF-ORGANIZING ALGORITHMS The variants of the proposed algorithm are executed and the resultant score of the final alignment were calculated and compared with SGA. A. Comparative Analysis of DSOMSA and SGA The number of generation is fixed in case of SGA whereas in DSOMSA, it is selforganized based on betterment of alignment resulting in each generation. The number of input sequences ranges from 4 to 8 and its total length vary from 1000 to The comparative results show that, on average, DSOMSA produces better alignments than SGA in less number of generations and time. The results are tabulated in 5.1 to 5.3 and a graphical representation in figure 5.1 to

5 Table 5.1 Comparative Analysis of Performance of DSOMSA and SGA on Balibase Datasets Dataset SGA DSOMSA Total Sequences Length Generations Generations RV11_BB RV11_BB RV11_BB RV11_BBS RV11_BBS RV12_BB RV12_BBS RV12_BBS RV12_BBS RV11_BBS RV11_BBS RV11_BB RV11_BBS RV11_BB RV11_BBS RV11_BBS RV11_BBS RV11_BBS RV12_BBS RV12_BBS Figure 5.1 Comparative Analysis of Performance of DSOMSA and SGA on Balibase Datasets 116

6 Table 5.2 Comparative Analysis of Performance of DSOMSA and SGA on Oxbench Datasets Dataset Sequences Length SGA Generations Generations DSOMSA 4t s s S s s t t Figure 5.2 Comparative Analysis of Performance of DSOMSA and SGA on Oxbench Datasets 117

7 Table 5.3 Comparative Analysis of Performance of DSOMSA and SGA on SMART Datasets Dataset SGA Sequences Length Generations DSOMSA Generations Crf Chsh Fimac Net pep Calcitonin Clb AXH NMU SORB Thy Figure 5.3 Comparative Analysis of Performance of DSOMSA and SGA on SMART Datasets 118

8 Performance Analysis on Consecutive Execution of DSOMSA To analyze the changes in the resulting score and the self-organizing convergence point, the algorithm was executed for ten times using the model dataset 469 with three sequences from Oxbench benchmark alignment suite and the scores, number of generation were tabulated in 5.4. A graphical representation indicating the changes in the modeler score against execution is given in figure 5.4. This analysis shows that the alignment produced and the number of generations varies for different execution. From the result, it is observed that best score was generated more number of times proving the efficiency of the algorithm. Table 5.4 Impact of Execution on (DSOMSA) Execution Exact Match Alignment length Generations Modeler

9 Figure 5.4 Impact of Execution on (DSOMSA)Procedural Analysis of DSOMSA To explain the self-organizing procedure of DSOMSA, a model dataset 469 with three sequences from Oxbench benchmark was used as input. In DSOMSA, the crossover rate (Rc) is fixed and the mutation rate (Rm) is allowed to change decreasingly from 80% to 1% based on the improvement in the alignment score which has an impact on the termination of the execution thereby the number of generations. The execution starts with Rm=80% and generation 1 produced an alignment with Modeler score In next generation (2) by the concept of selforganization implemented in the procedure Rm decreased to 78% resulting a score of Generation 5 with Rm=72% resulting in score of Next generation continued with Rm 72% resulting further no increase in the score. Hence by the concept of self-organization Rm decreased to 70%. Self-organizing decreasing in Rm continues till the lower limit of Rm (1%) is reached. Mutation rate reaches the lower limit 1%, when generation=46 and the score of the output alignment is as shown in the table 5.5. A Graph explaining the above procedure with generation against score was given in figure 5.5. It is evident from the table that for 469 dataset, five improvements occurred in the execution/ procedure to obtain the final score of

10 Table 5.5 Impact of Self-organizing Procedure (DSOMSA) Generation Crossover Rate Mutation Rate (in (in percentage) percentage) EM of elite Modeler score

11 Figure 5.5 Impact of Self-organizing Procedure (DSOMSA) B. Comparative Analysis of SOCCO and SGA The number of generation is fixed in case of SGA whereas in SOCCO, it is selforganized based on betterment of alignment resulting in each generation. The number of input sequences ranges from 4 to 8 and its total length vary from 1000 to The comparative results show that, on average, SOCCO produces better alignments than SGA in less number of generations and time. The results are tabulated in 5.6 to 5.8. A graphical representation in figure 5.6 to

12 Table 5.6 Comparative Analysis of Performance of SOCCO and SGA on Balibase Datasets Dataset Sequences Total Length SGA Generations SOCCO Generations RV11_BB RV11_BB RV11_BB RV11_BBS RV11_BBS RV12_BB RV12_BBS RV12_BBS RV12_BBS RV11_BBS RV11_BBS RV11_BB RV11_BBS RV11_BB RV11_BBS Figure 5.6 Comparative Analysis of Performance of SOCCO and SGA on Balibase Datasets 123

13 Table 5.7 Comparative Analysis of Performance of SOCCO and SGA on Oxbench Datasets Dataset SGA Sequences Length Generations SOCCO Generations 4t s s S s s t t Figure 5.7 Comparative Analysis of Performance of SOCCO and SGA on Oxbench Datasets 124

14 Table 5.8 Comparative Analysis of Performance of SOCCO and SGA on SMART Datasets Dataset Sequences Length SGA Generations SOCCO Generations Crf Chsh Fimac Net pep Calcitonin Clb AXH NMU SORB Thy Figure 5.8 Comparative Analysis of Performance of SOCCO and SGA on SMART Datasets 125

15 Performance Analysis on Consecutive Execution of SOCCO To analyze the changes in the resulting score and the self-organizing convergence point, the algorithm was executed for ten times using the model dataset 469 with three sequences from Oxbench benchmark alignment suite and the scores, number of generation were tabulated in 5.9. A graphical representation indicating the changes in the modeler score against execution is given in figure 5.9. This analysis shows that the alignment produced and the number of generations varies for different execution. From the result, it is observed that best score was generated more number of times proving the efficiency of the algorithm. Table 5.9 Impact of Execution on (SOCCO) Execution Exact Match Alignment length Generations Modeler

16 Figure 5.9 Impact of Execution on (SOCCO) Procedural Analysis of SOCCO To explain the self-organizing procedure of SOCCO, a model dataset 469 with three sequences from Oxbench benchmark was used as input. In SOCCO, the mutation rate (Rm) is fixed and the crossover rate (Rc) is allowed to change from 1% to 80% based on the improvement in the alignment score which has an impact on the termination of the execution thereby the number of generations. The execution starts with Rc =1% and generation (1) produced an alignment with Modeler score In next generation (2) by the concept of self-organization implemented in the procedure Rm increased to 3% resulting a score of Generation 16 with Rc =31% resulting score of Next generation continued with Rc 31% resulting in no further increase in the score. Hence by the concept of self-organization Rc increased to 33%. Self-organizing increases in Rc continues till the upper limit of Rm (80%) is reached. Mutation rate reaches the upper limit 81%, when generation = 43 and score of the output alignment is as shown in the table A graph explaining the above procedure with generation against score was given in figure It is evident from the table that for 469 dataset, two improvements occurred in the execution/ procedure to obtain the final score of

17 Table 5.10 Impact of Self-organizing Procedure (SOCCO) Generation Rate of Cyclic Crossover (in Percentage) Rate of Mutation Operator (in Percentage) EM of elite Modeler score

18 Figure 5.10 Impact of Self-organizing Procedure (SOCCO) Comparative Analysis of ACOSMO and SGA The number of generation is fixed in case of SGA whereas in ACOSMO, it is selforganized based on betterment of alignment resulting in each generation. Various datasets from standard Balibase, Oxbench and Smart benchmark alignment suites were taken and some of them were reported. The number of input sequences ranges from 4 to 16 and its total length vary from 1000 to 7000 are considered. The comparative results show that, on average, ACOSMO produces better alignments than SGA in less number of generations and time. The results are tabulated in 5.11 to 5.13 and a graphical representation is shown in figure 5.11 to

19 Table 5.11 Comparative Analysis of Performance of ACOSMO and SGA on Balibase Datasets Dataset SGA ACOSMO Total sequences Length Generations Generations RV11_BB RV11_BB RV11_BB RV11_BB RV11_BBS RV11_BBS RV12_BB RV20_BBS RV11_BBS RV12_BBS RV12_BBS RV12_BBS RV11_BBS RV11_BBS RV11_BB RV11_BBS RV11_BB RV11_BBS Figure 5.11 Comparative Analysis of Performance of ACOSMO and SGA on Balibase Datasets 130

20 Table 5.12 Comparative Analysis of Performance of ACOSMO and SGA on Oxbench Datasets Dataset sequences Total Length SGA Generations ACOSMO Generations 4t s s S s s t t Figure 5.12 Comparative Analysis of Performance of ACOSMO and SGA on Oxbench Datasets 131

21 Table 5.13 Comparative Analysis of Performance of ACOSMO and SGA on SMART Datasets Dataset SGA Total sequences Length Generations ACOSMO Generations Crf Chsh Fimac Net pep Calcitonin Clb AXH NMU SORB Thy Figure 5.13 Comparative Analysis of Performance of ACOSMO and SGA on SMART Datasets 132

22 Performance Analysis on Consecutive Execution of ACOSMO To analyze the changes in the resulting score and the self-organizing convergence point, the algorithm was executed for ten times using the model dataset 469 with three sequences from Oxbench benchmark alignment suite and the scores, number of generation were tabulated in A graphical representation indicating the changes in the modeler score against execution is given in figure This analysis shows that the alignment produced and the number of generations varies for different execution. From the result, it is observed that best score was generated more number of times proving the efficiency of the algorithm. Table 5.14 Impact of Execution on (ACOSMO) Execution Exact Match Alignment length Generations Modeler

23 Figure 5.14 Impact of Execution on (ACOSMO) Procedural Analysis of ACOSMO To explain the self-organizing procedure of ACOSMO, a model dataset 469 with three sequences from Oxbench benchmark was used as input. In ACOSMO, the crossover rate (Rc) is random depending on the Alignment length and the mutation rate (Rm) is allowed to change from 1% to 80% based on the improvement in the alignment score which has an impact on the termination of the execution thereby the number of generations. The execution starts with Rm=1% and generation 1 produced an alignment with Modeler score In next generation (2) by the concept of self-organization implemented in the procedure Rm increased to 3% resulting a score of Generation 3 retains the same rate Rm=3% resulting a score of and further no increase in the score. Hence by the concept of self-organization Rm increased to 5%. Self-organizing increase in Rm continues till the upper limit of Rm (80%) is reached. Mutation rate reaches the upper limit 81%, when generation = 46 and the score of the output alignment is as shown in the table A Graph explaining the above procedure with generation against score was given in figure It is evident from the table that for 469 dataset, seven improvements occurred in the execution/ procedure to obtain the final score of

24 Table 5.15 Impact of Self-organizing Procedure (ACOSMO) Generation Rate of Cyclic Mutation Operator (in Percentage) EM of elite Modeler score

25 Figure 5.15 Impact of Self-organizing Procedure (ACOSMO) 136

26 5.3 OVERALL COMPARISON OF DEVELOPED ALGORITHMS The standard GA programs for aligning multiple sequences like SGA, imaga and the seven self-organizing algorithms developed for MSA (SOMSA, C-MSA, AP-MSA, SOMM-MSA, DSOMSA, SOCCO and ACOSMO) are compared among each other. This comparative analysis is done to prove their efficiency in terms of the resulting score and process along with the values assigned for important parameters like population size, number of generations, alignment length, and crossover and mutation rates. 137

27 Table 5.16 Comparison of Performance of Developed Algorithms Developed Tools SGA i-maga SOMSA SOCCO C-MSA SOMM-MSA ACOSMO AP-MSA DSOMSA Crossover Mutation Population Alignment Rate Rate Size Length Generation Fixed Fixed Fixed Fixed Random Random User choice User choice Single point/ Single point/ Double point (100) Double point (5) SO - Fixed SO - Cyclic Fixed SO- Process 406 dependent SO - Cyclic SO - Fixed Fixed SO- Process 406 dependent SO - Cyclic SO - Cyclic Fixed SO- Process 406 dependent SO- Process Multiple [5] Fixed SO - Fixed dependent 406 Nor Cyc Inc Dec Rev SO- Process SO - Cyclic Fixed SO - Random 406 dependent SO- Process SO - Fixed SO - Cyclic SO - Random 406 dependent SO - Fixed SO - Cyclic Fixed SO- Process 406 dependent Exact Match Modeler score

28 comparison Modeler SGA i-maga SOMSA SOCCO C-MSA SOMM-MSA ACOSMO AP-MSA DSOMSA Developed tools Figure 5.16 Comparison of Performance of Developed Algorithms 139

29 Table 5.17 Comparison of SOMSA with Existing MSA Tools using Q Dataset ClustalW T-Coffee Di-align MUSCLE Mafft Praline POA SGA SOMSA P R O T E I 1r c havA N 2myr D N A RV12_BBS t AXH

30 141 Figure 5.17 Comparison of SOMSA with Existing MSA Tools using Q

31 Table 5.18 Comparison of SOMSA with Existing MSA Tools using TC P R O T E I N Dataset ClustalW T-Coffee Di-align MUSCLE Mafft Praline POA SGA SOMSA 1r c havA myr RV12_BBS D N 4t A AXH

32 143 Figure 5.18 Comparison of SOMSA with Existing MSA Tools using TC

33 Table 5.19 Comparison of SOMSA with Existing MSA Tools using Cline Dataset ClustalW T-Coffee Di-align MUSCLE Mafft Praline POA SGA SOMSA P 1r R O 451c T E I N 1havA 2myr RV12_BBS D N A 4t AXH

34 145 Figure 5.19 Comparison of SOMSA with Existing MSA Tools using Cline

35 Table 5.20 Comparison of SOMSA with Existing MSA Tools using Modeler Dataset ClustalW T-Coffee Di-align MUSCLE Mafft Praline POA SGA SOMSA P 1r R O 451c T E I N 1havA 2myr D RV12_BBS N A 4t

36 147 Figure 5.20 Comparison of SOMSA with Existing MSA Tools using Modeler

37 Figure 5.21 Sample Output of Developed SGA for MSA (imaga) Figure 5.22 Sample Output of Self-organizing Genetic Algorithms for MSA 148

38 5.4 SUMMARY This chapter discussed the results of the comparative analysis of variants of SOGA developed with SGA and existing algorithms for MSA. In addition, a comparison is also made among the developed algorithms in terms of various parameters and results. From the overall comparative analysis, the algorithms developed showed a better performance than the other existing algorithms in identifying the better alignments. 149