Segmented K-mer and its application on similarity analysis of mitochondrial genome sequences.
K-mer-based approach has been widely used in similarity analyses so as to discover similarity/dissimilarity among different biological sequences. In this study, we have improved the traditional K-mer method, and introduce a segmented K-mer approach (s-K-mer). After each primary sequence is divided into several segments, we simultaneously transform all these segments into corresponding K-mer-based vectors. In this approach, it is vital how to determine the optimal combination of distance metric with the number of K and the number of segments, i.e., (K(⁎), s(⁎), and d(⁎)). Based on the cascaded feature vectors transformed from s(⁎) segmented sequences, we analyze 34 mammalian genome sequences using the proposed s-K-mer approach. Meanwhile, we compare the results of s-K-mer with those of traditional K-mer. The contrastive analysis results demonstrate that s-K-mer approach outperforms the traditionally K-mer method on similarity analysis among different species.
Department of Mathematics, School of Science, Anhui Science and Technology University, Fengyang, Anhui 233100, China. yhj551@gmail.com
Hong-Jie
HJ
Yu
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D003627:Data Interpretation, Statistical; D008040:Genetic Linkage; D054629:Genome, Mitochondrial; D006801:Humans; D008928:Mitochondria / Q000235:genetics*; D017422:Sequence Analysis, DNA