Computational Analysis of Nucleosome Positioning Signals in the Simian Virus 40 Chromatin

To better understand the regulatory role of nucleosomes, it is important to pinpoint their positions in the DNA sequence. In this paper, we present a pattern recognition algorithm to predict the locations of nucleosomes. Based on a number of features of the nucleosomal architecture, a computational framework based on the probabilistic relaxation labeling technique is developed to infer the nucleosome centers along the DNA sequence of simian virus 40 (SV40). Using this method, we can detect about 70% of the SV40 nucleosome locations with high probability (>0.9). The proposed algorithm improves the flexibility and efficiency in nucleosome positioning, and makes it easy to analyze nucleosome structure without expensive wet-lab biological experiments. Our results show that the framework is practicable and has potential in its applications. In fact, only the significant periodicity of DNA dinucleotides is employed in our current algorithm as a nucleosomal feature. We believe that more pattern recognition techniques can be developed to improve the prediction accuracy of nucleosome positions by employing more sequence features.