K2 and K2*: efficient alignment-free sequence similarity measurement based on Kendall statistics

Motivation: Alignment-free sequence comparison methods can compute the pairwise similarity between a huge number of sequences much faster than sequence-alignment based methods. Results: We propose a new non-parametric alignment-free sequence comparison method, called K-2, based on the Kendall statistics. Comparing to the other state-of-the-art alignment-free comparison methods, K-2 demonstrates competitive performance in generating the phylogenetic tree, in evaluating functionally related regulatory sequences, and in computing the edit distance (similarity/dissimilarity) between sequences. Furthermore, the K-2 approach is much faster than the other methods. An improved method, K-2*, is also proposed, which is able to determine the appropriate algorithmic parameter (length) automatically, without first considering different values. Comparative analysis with the state-of-the-art alignment-free sequence similarity methods demonstrates the superiority of the proposed approaches, especially with increasing sequence length, or increasing dataset sizes.