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Bioinformatics Advance Access originally published online on January 29, 2004
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Bioinformatics 20(6) © Oxford University Press 2004; all rights reserved.

On the significance of sequence alignments when using multiple scoring matrices

Florian Frommlet 1,*, Andreas Futschik 2 and Malgorzata Bogdan 3

1 Department of Medical Statistics and 2 Department of Statistics, University of Vienna, A-1010 Vienna, Austria and 3 Institute of Mathematics, Wroclaw University of Technology, 50-370 Wroclaw, Poland

Received on July 14, 2003 ; revised on August 29, 2003 ; accepted on September 8, 2003
Advance Access Publication January 29, 2004

Motivation: Pairwise local sequence alignment is commonly used to search data bases for sequences related to some query sequence. Alignments are obtained using a scoring matrix that takes into account the different frequencies of occurrence of the various types of amino acid substitutions. Software like BLAST provides the user with a set of scoring matrices available to choose from, and in the literature it is sometimes recommended to try several scoring matrices on the sequences of interest.

The significance of an alignment is usually assessed by looking at E-values and p-values. While sequence lengths and data base sizes enter the standard calculations of significance, it is much less common to take the use of several scoring matrices on the same sequences into account. Altschul proposed corrections of the p-value that account for the simultaneous use of an infinite number of PAM matrices. Here we consider the more realistic situation where the user may choose from a finite set of popular PAM and BLOSUM matrices, in particular the ones available in BLAST. It turns out that the significance of a result can be considerably overestimated, if a set of substitution matrices is used in an alignment problem and the most significant alignment is then quoted.

Results: Based on extensive simulations, we study the multiple testing problem that occurs when several scoring matrices for local sequence alignment are used. We consider a simple Bonferroni correction of the p-values and investigate its accuracy. Finally, we propose a more accurate correction based on extreme value distributions fitted to the maximum of the normalized scores obtained from different scoring matrices. For various sets of matrices we provide correction factors which can be easily applied to adjust p- and E-values reported by software packages.

Availability: The code used for our simulations is available on http://mailbox.univie.ac.at/Florian.Frommlet/MSMsoftware.htm/

Contact: Florian.Frommlet{at}univie.ac.at

* To whom correspondence should be addressed.


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