Campus Units

Computer Science

Document Type

Conference Proceeding

Conference

Second International Workshop on Data and Text Mining in Bioinformatics (DTMBio) 2008

Publication Version

Published Version

Publication Date

2009

Journal or Book Title

BMC Bioinformatics

Volume

10

Issue

Suppl 3

First Page

S1

DOI

10.1186/1471-2105-10-S3-S1

Conference Title

Second International Workshop on Data and Text Mining in Bioinformatics (DTMBio) 2008

Conference Date

October 30, 2008

City

Napa Valley, CA, USA

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.

Comments

This proceeding was published as Agrawal, Ankit, and Xiaoqiu Huang. "Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty." In BMC Bioinformatics 10 (2009): S1, doi: 10.1186/1471-2105-10-S3-S1. From Second International Workshop on Data and Text Mining in Bioinformatics (DTMBio) 2008 Napa Valley, CA, USA. 30 October 2008.

Rights

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Copyright Owner

Agrawal and Huang

Language

en

File Format

application/pdf

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