Model-based prediction of sequence alignment quality

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. © The Author 2008. Published by Oxford University Press. All rights reserved.