Weighted measures based on maximizing deviation for alignment-free sequence comparison

Abstract

Alignment-free sequence comparison is becoming fairly popular in many fields of computational biology due to less requirements for sequence itself and computational efficiency for a large scale of sequence data sets. Especially, the approaches based on k-tuple like D2, D2S and D2∗ are used widely and effectively. However, these measures treat each k-tuple equally without accounting for the potential importance differences among all k-tuples. In this paper, we take advantage of maximizing deviation method proposed in multiple attribute decision making to evaluate the weights of different k-tuples. We modify D2, D2S and D2∗ with weights and test them by similarity search and evaluation on functionally related regulatory sequences. The results demonstrate that the newly proposed measures are more efficient and robust compared to existing alignment-free methods.