Abstract
BackgroundDuring procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment’s accuracy, however, was ignored by these researches.ResultsA novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM’s parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods.ConclusionsWe propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment’s accuracy.
Highlights
During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment
Using Multiple sequence alignment (MSA) to find sequence differences can assist in the construction and annotation of biological ontologies, for example, the largest ontology in the world, Gene Ontology [2], on which researchers conduct a lot of works [3,4,5,6,7]
The results indicated that ProbPFP got the maximum mean scores among the two benchmark datasets SABmark [40] and OXBench [45], along with the second highest mean score on the dataset BAliBASE [46]
Summary
During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Multiple sequence alignment is a foundermental conception. It aim to align more than two biomolecular sequences and applied for various biological analysis tasks, for example, protein structure prediction and phylogenetic inference [1]. Using a progressive method to align a set of sequences, first of all, for each paired sequence, we need to do pairwise alignment, to compute the distance of the pair. A guide tree was generated on the basis of the distance matrix. On the ground of the provided order, which offered by the guide tree, profile-profile alignment was executed progressively
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