Island method for estimating the statistical significance of profile-profile alignment scores.

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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Poleksic A

doi

10.1186/1471-2105-10-112

subject

Has Abstract

pub_date

2009-04-20 00:00:00

pages

112

issn

1471-2105

pii

1471-2105-10-112

journal_volume

10

pub_type

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