Use of image texture analysis to find DNA sequence similarities

Abstract

Sequence similarity analysis is a basic method in computational biological studies. Determining the similarity of biological sequences is a vital step in much research, such as exploring the evolutionary relationship among species, gene function analysis, protein structure prediction, and sequence retrieving. This paper introduces a method that uses the theory of the gray-level co-occurrence matrix, which is important in image texture analysis, to define and calculate the features of a DNA sequence. The proposed method can make a quantitative analysis and compute the defined texture features of a DNA sequence. Using these quantified sequence features, a similarity distance matrix can be computed and phylogenetic relationships also can be inferred. From the quantified features, we found that the DNA sequence of humans has the highest entropy and lowest energy. From human to chimpanzee, orangutan, gorilla, and other species, the entropy decreases and energy increases. The advantage of the proposed method is that it can compute multiple features inherent in each sequence. Furthermore, the defined features can be the key values or tags for each sequence for sequence retrieval and similarity analysis.