Research on phylogenetic analysis methods based on forward and reverse sequences: applications of biological sequences in artificial intelligence

With the rapid advancement of artificial intelligence, the field of biological sequence research has also developed rapidly. For overcome the high computational complexity associated with traditional sequence alignment algorithms, sequence alignment-free methods have been proposed. This paper proposes a Whole-genome sequence-based alignment-free method: the RevSeq-Kmer method. By determining the relative positions of k-mers in forward and reverse sequences, we obtain position-weighted measures for k-mers. Subsequently, we calculate frequency information for k-mers, weight this information using Shannon entropy, derive a distance matrix, and construct a phylogenetic tree. We validated our method using two datasets and compared it with the classic CVtree and DLtree approaches. The results demonstrate that our method is reliable and trustworthy.