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/.

BACKGROUND: Multiple sequence alignment is the foundation of many important applications in bioinformatics that aim at detecting functionally important regions, predicting protein structures, building phylogenetic trees etc. Although the automatic construction of a multiple sequence alignment for a set of remotely related sequences cause a very challenging and error-prone task, many downstream analyses still rely heavily on the accuracy of the alignments. RESULTS: To address the need for an objective evaluation framework, we introduce a statistical score that assesses the quality of a given multiple sequence alignment. The quality assessment is based on counting the number of significantly conserved positions in the alignment using importance sampling method in conjunction with statistical profile analysis framework. We first evaluate a novel objective function used in the alignment quality score for measuring the positional conservation. The results for the Src homology 2 (SH2) domain, Ras-like proteins, peptidase M13, subtilase and beta-lactamase families demonstrate that the score can distinguish sequence patterns with different degrees of conservation. Secondly, we evaluate the quality of the alignments produced by several widely used multiple sequence alignment programs using a novel alignment quality score and a commonly used sum of pairs method. According to these results, the Mafft strategy L-INS-i outperforms the other methods, although the difference between the Probcons, TCoffee and Muscle is mostly insignificant. The novel alignment quality score provides similar results than the sum of pairs method. CONCLUSION: The results indicate that the proposed statistical score is useful in assessing the quality of multiple sequence alignments.

MOTIVATION: Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search. RESULTS: We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods. AVAILABILITY: The algorithms written in C language are available on request from the authors.