CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2014 vol.11 Issue No.04 - July-Aug.
Issue No.04 - July-Aug. (2014 vol.11)
Guangwei Xu , Sch. of Comput. Sci. & Technol., Donghua Univ., Shanghai, China
Nucleosomes are basic elements of chromatin structure. The positioning of nucleosomes along a genome is very important to dictate eukaryotic DNA compaction and access. Current computational methods have focused on the analysis of nucleosome occupancy and the positioning of well-positioned nucleosomes. However, fuzzy nucleosomes require more complex configurations and are more difficult to predict their positions. We analyzed the positioning of well-positioned and fuzzy nucleosomes from a novel structural perspective, and proposed WaveNuc, a computational approach for inferring their positions based on continuous wavelet transformation. The comparative analysis demonstrates that these two kinds of nucleosomes exhibit different propeller twist structural characteristics. Well-positioned nucleosomes tend to locate at sharp peaks of the propeller twist profile, whereas fuzzy nucleosomes correspond to broader peaks. The sharpness of these peaks shows that the propeller twist profile may contain nucleosome positioning information. Exploiting this knowledge, we applied WaveNuc to detect the two different kinds of peaks of the propeller twist profile along the genome. We compared the performance of our method with existing methods on real data sets. The results show that the proposed method can accurately resolve complex configurations of fuzzy nucleosomes, which leads to better performance of nucleosome positioning prediction on the whole genome.
Bioinformatics, Nucleosomes, Continuous wavelet transforms, Genomics, DNA, Computational biology,genome analysis, Nucleosome positioning, structural feature, continuous wavelet transformation
Guangwei Xu, "A Novel Wavelet-Based Approach for Predicting Nucleosome Positions Using DNA Structural Information", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.11, no. 4, pp. 638-647, July-Aug. 2014, doi:10.1109/TCBB.2014.2306837