Statistical Comparative Study of Multiple Sequence Alignment Scores of Iterative Refinement Algorithms

Abstract

Iterative refinement algorithm is a useful method to improve the alignment results. In this paper, we evaluated different iterative refinement algorithms statistically. There are four iterative refinement algorithms: remove first (RF), bestfirst (BF), random (RD), and tree-based (Tb) iterative refinement algorithm. And there are two scoring functions for measuring the iteration judgment step: log expectation (LE) and weighted sum-of-pairs (SP) scores. There are two sequence clustering methods: neighbor-joining (NJ) method and unweighted pair-group method with arithmetic mean (UPGMA). We performed comprehensive analyses of these alignment strategies and compared these strategies using BAliBASE SP (BSP) score. We observed the behavior of scores from the view point of cumulative frequency (CF) and other basic statistical parameters. Ultimately, we tested the statistical significance of all alignment results by using Friedman nonparametric analysis of variance (ANOVA) test for ranks and Scheffé multiple comparison test.