Phylogenetic analysis of DNA sequences based on -word and rough set theory

Among alignment-free methods for sequence comparison, the model of k -word frequencies is a well-developed one. However, most existing word-based methods neglect relationships among k -word frequencies, while a few others focus on the correlation of k -words but ignore the word frequency itself. In this paper, we propose a new k -word method which succeeds in conquering the two problems. ns of characteristic sequences of a DNA sequence, we construct a 3 × 2 k dimensional complete word-based vector. Then we present a feature selection scheme based on rough set theory (RST) to extract the most informative k -words and use only these selected features to represent the DNA sequence. To evaluate the effectiveness of our method, we test it by phylogenetic analysis on three datasets. The first one is used as a training set, by which 869 top ranked k -words are selected. The other two are used as the testing set. The results demonstrate that the proposed method can capture more important information and is more efficient for molecular phylogenetic analysis.