A novel algorithm for CpG island detection in human genome based on clustering and chaotic particle swarm optimization

Abstract

CpG islands provide a major role in the genome and are used for prediction of promoter regions. They are abnormally methylated in cancer cells and can be used as tumor markers. However, current techniques for identifying CpG islands suffer from various drawbacks. In this paper, we propose a novel algorithm to detect CpG islands by combining clustering techniques with complementary chaotic particle swarm optimization. Clustering techniques are used to find the locations of potential CpG island candidates in the genome while Complementary Chaotic PSO is used to find the best location of a CpG island in a cluster candidate without being trapped in local optimum solution. This combination can successfully overcome the drawbacks of each method while maintaining their advantages. The proposed method called 3C-PSO provides a high sensitivity detection of CpG islands in the human genome. To evaluate its performance, we used six sequences from NCBI, and five measures of performance: sensitivity (SN), specificity (SP), accuracy (ACC), performance coefficient (PC), and correlation coefficient (CC). We compared our approach to the existing methods of CpG islands detection in the human genome. The obtained results have shown that 3C-PSO competes with and even outperforms these methods.