Limits...
Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty.

Agrawal A, Huang X - BMC Bioinformatics (2009)

Bottom Line: Further, the results of pairwise statistical significance using multiple parameter sets are shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH.The fact that the homology detection performance does not degrade when using multiple parameter sets is a strong evidence for the validity of the assumption that the alignment score distribution follows an extreme value distribution even when using multiple parameter sets.Pairwise statistical significance using multiple parameter sets can be effectively used to determine the relatedness of a (or a few) pair(s) of sequences without performing a time-consuming database search.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, IA 50011-1041, USA. ankitag@iastate.edu

ABSTRACT

Background: Accurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, a comparative study of pairwise statistical significance with database statistical significance was conducted. In this paper, we extend the earlier work on pairwise statistical significance by incorporating with it the use of multiple parameter sets.

Results: Results for a knowledge discovery application of homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives better coverage than using a single parameter set, at least at some error levels. Further, the results of pairwise statistical significance using multiple parameter sets are shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH. Using non-zero parameter set change penalty values give better performance than zero penalty.

Conclusion: The fact that the homology detection performance does not degrade when using multiple parameter sets is a strong evidence for the validity of the assumption that the alignment score distribution follows an extreme value distribution even when using multiple parameter sets. Parameter set change penalty is a useful parameter for alignment using multiple parameter sets. Pairwise statistical significance using multiple parameter sets can be effectively used to determine the relatedness of a (or a few) pair(s) of sequences without performing a time-consuming database search.

Show MeSH
Empirical justification of parameter set change penalty. Coverage vs. Parameter Set Change Penalty plots at different errors per query for the substitution matrix combination of BLOSUM45 and BLOSUM62. Poor coverage is obtained if the parameter set change penalty is zero. The coverage is better and steady for non-zero values of parameter set change penalty.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2665049&req=5

Figure 5: Empirical justification of parameter set change penalty. Coverage vs. Parameter Set Change Penalty plots at different errors per query for the substitution matrix combination of BLOSUM45 and BLOSUM62. Poor coverage is obtained if the parameter set change penalty is zero. The coverage is better and steady for non-zero values of parameter set change penalty.

Mentions: The coverage vs. parameter set change penalty plot for the substitution matrix combination of BLOSUM45 and BLOSUM62 is illustrated in Figure 5. The curve shows a poor coverage performance for the case when the parameter set change penalty is not charged, i.e., when the alignment algorithm is freely allowed to change the parameter set during alignment without charging any penalty. This can be explained by the fact that the algorithm would try to mathematically maximize the alignment score by changing the parameter set as frequently as possible, which may produce more biologically irrelevant alignments. A similar phenomenon is also observed when very low gap penalty is used [28]. The coverage performance clearly improves for non-zero values of parameter set change penalty, which provides its empirical justification.


Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty.

Agrawal A, Huang X - BMC Bioinformatics (2009)

Empirical justification of parameter set change penalty. Coverage vs. Parameter Set Change Penalty plots at different errors per query for the substitution matrix combination of BLOSUM45 and BLOSUM62. Poor coverage is obtained if the parameter set change penalty is zero. The coverage is better and steady for non-zero values of parameter set change penalty.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2665049&req=5

Figure 5: Empirical justification of parameter set change penalty. Coverage vs. Parameter Set Change Penalty plots at different errors per query for the substitution matrix combination of BLOSUM45 and BLOSUM62. Poor coverage is obtained if the parameter set change penalty is zero. The coverage is better and steady for non-zero values of parameter set change penalty.
Mentions: The coverage vs. parameter set change penalty plot for the substitution matrix combination of BLOSUM45 and BLOSUM62 is illustrated in Figure 5. The curve shows a poor coverage performance for the case when the parameter set change penalty is not charged, i.e., when the alignment algorithm is freely allowed to change the parameter set during alignment without charging any penalty. This can be explained by the fact that the algorithm would try to mathematically maximize the alignment score by changing the parameter set as frequently as possible, which may produce more biologically irrelevant alignments. A similar phenomenon is also observed when very low gap penalty is used [28]. The coverage performance clearly improves for non-zero values of parameter set change penalty, which provides its empirical justification.

Bottom Line: Further, the results of pairwise statistical significance using multiple parameter sets are shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH.The fact that the homology detection performance does not degrade when using multiple parameter sets is a strong evidence for the validity of the assumption that the alignment score distribution follows an extreme value distribution even when using multiple parameter sets.Pairwise statistical significance using multiple parameter sets can be effectively used to determine the relatedness of a (or a few) pair(s) of sequences without performing a time-consuming database search.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, IA 50011-1041, USA. ankitag@iastate.edu

ABSTRACT

Background: Accurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, a comparative study of pairwise statistical significance with database statistical significance was conducted. In this paper, we extend the earlier work on pairwise statistical significance by incorporating with it the use of multiple parameter sets.

Results: Results for a knowledge discovery application of homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives better coverage than using a single parameter set, at least at some error levels. Further, the results of pairwise statistical significance using multiple parameter sets are shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH. Using non-zero parameter set change penalty values give better performance than zero penalty.

Conclusion: The fact that the homology detection performance does not degrade when using multiple parameter sets is a strong evidence for the validity of the assumption that the alignment score distribution follows an extreme value distribution even when using multiple parameter sets. Parameter set change penalty is a useful parameter for alignment using multiple parameter sets. Pairwise statistical significance using multiple parameter sets can be effectively used to determine the relatedness of a (or a few) pair(s) of sequences without performing a time-consuming database search.

Show MeSH