Pairwise statistical significance of local sequence alignment using multiple parameter sets

Ankit Agrawal; Xiaoqiu Huang

doi:10.1145/1458449.1458462

Pairwise statistical significance of local sequence alignment using multiple parameter sets

Ankit Agrawal^*, Xiaoqiu Huang

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives significantly better coverage than using a single parameter set, at least at some error levels. Also, the fact that the performance does not degrade when using multiple parameter sets is a strong evidence that the assumption that the score distribution follows an extremevalue distribution is valid even when using multiple parameter sets. Results of pairwise statistical significance usingmultiple parameter sets are further shown to be significantly better than database statistical significance estimates reported by BLAST&PSI-BLAST,&comparable&at times significantly better than SSEARCH.

Original language	English (US)
Title of host publication	Proceedings of the 2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08
Pages	53-59
Number of pages	7
DOIs	https://doi.org/10.1145/1458449.1458462
State	Published - Dec 1 2008
Event	2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08 - Napa Valley, CA, United States Duration: Oct 26 2008 → Oct 30 2008

Other

Other	2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08
Country/Territory	United States
City	Napa Valley, CA
Period	10/26/08 → 10/30/08

Keywords

Database statistical significance
Homologs
Pairwise statistical significance
Parameter set
Sequence alignment

ASJC Scopus subject areas

Decision Sciences(all)
Business, Management and Accounting(all)

Access to Document

10.1145/1458449.1458462

Cite this

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title = "Pairwise statistical significance of local sequence alignment using multiple parameter sets",

abstract = "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. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives significantly better coverage than using a single parameter set, at least at some error levels. Also, the fact that the performance does not degrade when using multiple parameter sets is a strong evidence that the assumption that the score distribution follows an extremevalue distribution is valid even when using multiple parameter sets. Results of pairwise statistical significance usingmultiple parameter sets are further shown to be significantly better than database statistical significance estimates reported by BLAST&PSI-BLAST,&comparable&at times significantly better than SSEARCH.",

keywords = "Database statistical significance, Homologs, Pairwise statistical significance, Parameter set, Sequence alignment",

author = "Ankit Agrawal and Xiaoqiu Huang",

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booktitle = "Proceedings of the 2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08",

note = "2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08 ; Conference date: 26-10-2008 Through 30-10-2008",

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Agrawal, A & Huang, X 2008, Pairwise statistical significance of local sequence alignment using multiple parameter sets. in Proceedings of the 2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08. pp. 53-59, 2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08, Napa Valley, CA, United States, 10/26/08. https://doi.org/10.1145/1458449.1458462

Pairwise statistical significance of local sequence alignment using multiple parameter sets. / Agrawal, Ankit; Huang, Xiaoqiu.

Proceedings of the 2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM'08. 2008. p. 53-59.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Agrawal, Ankit

AU - Huang, Xiaoqiu

PY - 2008/12/1

Y1 - 2008/12/1

N2 - 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. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives significantly better coverage than using a single parameter set, at least at some error levels. Also, the fact that the performance does not degrade when using multiple parameter sets is a strong evidence that the assumption that the score distribution follows an extremevalue distribution is valid even when using multiple parameter sets. Results of pairwise statistical significance usingmultiple parameter sets are further shown to be significantly better than database statistical significance estimates reported by BLAST&PSI-BLAST,&comparable&at times significantly better than SSEARCH.

AB - 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. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives significantly better coverage than using a single parameter set, at least at some error levels. Also, the fact that the performance does not degrade when using multiple parameter sets is a strong evidence that the assumption that the score distribution follows an extremevalue distribution is valid even when using multiple parameter sets. Results of pairwise statistical significance usingmultiple parameter sets are further shown to be significantly better than database statistical significance estimates reported by BLAST&PSI-BLAST,&comparable&at times significantly better than SSEARCH.

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KW - Homologs

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KW - Parameter set

KW - Sequence alignment

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ER -

Pairwise statistical significance of local sequence alignment using multiple parameter sets

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