Faculty Publications

Document Type

Article

Journal/Book/Conference Title Title

BMC Bioinformatics

Volume

10

Issue

112

Abstract

Background: In the last decade, a significant improvement in detecting remote similarity between protein sequences has been made by utilizing alignment profiles in place of amino-acid strings. Unfortunately, no analytical theory is available for estimating the significance of a gapped alignment of two profiles. Many experiments suggest that the distribution of local profile-profile alignment scores is of the Gumbel form. However, estimating distribution parameters by random simulations turns out to be computationally very expensive.

Results: We demonstrate that the background distribution of profile-profile alignment scores heavily depends on profiles' composition and thus the distribution parameters must be estimated independently, for each pair of profiles of interest. We also show that accurate estimates of statistical parameters can be obtained using the "island statistics" for profile-profile alignments.

Conclusion: The island statistics can be generalized to profile-profile alignments to provide an efficient method for the alignment score normalization. Since multiple island scores can be extracted from a single comparison of two profiles, the island method has a clear speed advantage over the direct shuffling method for comparable accuracy in parameter estimates.

Department

Department of Computer Science

Comments

First published in BMC Bioinformatics, v. 10 n. 112, (2009), 12 pages, published by BioMed Central Ltd. DOI: https://doi.org/10.1186/1471-2105-10-112.

Original Publication Date

2009

DOI of published version

10.1186/1471-2105-10-112

Repository

UNI ScholarWorks, University of Northern Iowa, Rod Library

Date Digital

2009

Copyright

©2009 Alexsandar Poleksic. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Language

EN

File Format

application/pdf

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