GLProbs: aligning multiple sequences adaptively

Abstract

This paper proposes a simple and effective approach to improve the accuracy of multiple sequence alignment. We use a natural measure to estimate the similarity of the input sequences, and based on this measure, we align the input sequences differently. For example, for inputs with high similarity, we consider the whole sequences and align them globally, while for those with moderately low similarity, we may ignore the flank regions and align locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool call GLProbs, and compares its performance with a dozen leading alignment tools on three benchmark alignment databases. Our results shows that GLProbs has the best accuracy for almost all testings.