K<inf>2</inf>: Efficient alignment-free sequence similarity measurement using the Kendall statistic

Abstract

Alignment-free sequence comparison methods can compute the similarity between a large number of sequences much faster than methods that depend on sequence alignment. We propose a new alignment-free sequence comparison method, called K 2 , based on the non-parametric Kendall statistic. Compared with the state-of-the-art alignment-free comparison methods (e.g., D 2 , D 2 *, D 2 sh, and Chisquare(χ2) statistic), K 2 showed comparative power, demonstrating similar or better performance in computing the edit distance (similarity/dissimilarity) among a huge number of sequences. The K 2 approach was much faster than each of the other methods, especialy, with long sequence lengths.