Skip Navigation



Bioinformatics Advance Access published online on August 4, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn414
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
24/19/2165    most recent
btn414v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ahola, V.
Right arrow Articles by Uusipaikka, E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ahola, V.
Right arrow Articles by Uusipaikka, E.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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, Finland
2Department of Statistics, FI-20014 University of Turku, Finland
3Department of Mathematics, FI-20014 University of Turku, Finland
4Institute of Medical Technology, FI-33014 University of Tampere, Finland
5Tampere University Hospital, FI-33520 Tampere, Finland

*To whom correspondence should be addressed. Virpi Ahola, E-mail: virpi.ahola{at}mtt.fi


  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 database. Reference sequences were re-aligned with the Mafft, Muscle and Probcons alignment programs, and a sum-of-pairs (SP) score was used to measure the quality of the re-alignments. 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 MOS and better than those of NorMD and 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 is available at Bioinformatics online.

Associate Editor: Prof. Burkhard Rost

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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.