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Bioinformatics Advance Access originally published online on August 4, 2008
Bioinformatics 2008 24(19):2165-2171; doi:10.1093/bioinformatics/btn414
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Model-based prediction of sequence alignment quality

Virpi Ahola 1,2,*, Tero Aittokallio 3, Mauno Vihinen 4,5 and Esa Uusipaikka 2

1Biotechnology and Food Research, MTT Agrifood Research Finland, FI-31600 Jokioinen, 2Department of Statistics, 3Department of Mathematics, FI-20014, University of Turku, 4Institute of Medical Technology, FI-33014, University of Tampere and 5Tampere University Hospital, FI-33520 Tampere, Finland

*To whom correspondence should be addressed.


  Abstract

Motivation: Multiple sequence alignment (MSA) is an essential prerequisite for many sequence analysis methods and valuable tool itself for describing relationships between protein sequences. Since the success of the sequence analysis is highly dependent on the reliability of alignments, measures for assessing the quality of alignments are highly requisite.

Results: We present a statistical model-based alignment quality score. Unlike other quality scores, it does not require several parallel alignments for the same set of sequences or additional structural information. Our quality score is based on measuring the conservation level of reference alignments in Homstrad. Reference sequences were realigned with the Mafft, Muscle and Probcons alignment programs, and a sum-of-pairs (SP) score was used to measure the quality of the realignments. Statistical modelling of the SP score as a function of conservation level and other alignment characteristics makes it possible to predict the SP score for any global MSA. The predicted SP scores are highly correlated with the correct SP scores, when tested on the Homstrad and SABmark databases. The results are comparable to that of multiple overlap score (MOS) and better than those of normalized mean distance (NorMD) and normalized iRMSD (NiRMSD) alignment quality criteria. Furthermore, the predicted SP score is able to detect alignments with badly aligned or unrelated sequences.

Availability: The method is freely available at http://www.mtt.fi/AlignmentQuality/

Contact: virpi.ahola{at}mtt.fi

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Burkhard Rost

Received on May 20, 2008; revised on July 25, 2008; accepted on August 1, 2008

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