A Hybrid method for effective multiple sequence alignment

Progressive methods for multiple sequence alignment are popular for their simplicity and cost effectiveness. However, it has been shown that they fail in locating the flanking core blocks. To cope with this issue, we describe in this paper a hybrid algorithm that aims to improve the accuracy of progressive global alignments especially in the case of families including sequences with large NC-terminal extension. The key idea is to combine local and global alignments within the same alignment process. This process acts as a progressive method driven by a guide tree where pairwise local or global alignment is performed depending on the significance of the difference between lengths of sequences. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to achieve good quality solutions.