A comparative analysis of progressive multiple sequence alignment approaches using UPGMA and neighbor joining based guide trees (1509.03530v1)

Abstract: Multiple sequence alignment is increasingly important to bioinformatics, with several applications ranging from phylogenetic analyses to domain identification. There are several ways to perform multiple sequence alignment, an important way of which is the progressive alignment approach studied in this work. Progressive alignment involves three steps: find the distance between each pair of sequences; construct a guide tree based on the distance matrix; finally based on the guide tree align sequences using the concept of aligned profiles. Our contribution is in comparing two main methods of guide tree construction in terms of both efficiency and accuracy of the overall alignment: UPGMA and Neighbor Join methods. Our experimental results indicate that the Neighbor Join method is both more efficient in terms of performance and more accurate in terms of overall cost minimization.