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Sequence Signatures of Nucleosome Positioning in Caenorhabditis elegans

Our recent investigation in the protist Trichomonas vaginalis suggested a DNA sequence periodicity with a unit length of 120.9 nt, which represents a sequence signature for nucleosome positioning. We now extended our observation in higher eukaryotes and identified a similar periodicity of 175 nt in...

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Detalles Bibliográficos
Autores principales: Chen, Kaifu, Wang, Lei, Yang, Meng, Liu, Jiucheng, Xin, Chengqi, Hu, Songnian, Yu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054450/
https://www.ncbi.nlm.nih.gov/pubmed/20691394
http://dx.doi.org/10.1016/S1672-0229(10)60010-1
Descripción
Sumario:Our recent investigation in the protist Trichomonas vaginalis suggested a DNA sequence periodicity with a unit length of 120.9 nt, which represents a sequence signature for nucleosome positioning. We now extended our observation in higher eukaryotes and identified a similar periodicity of 175 nt in length in Caenorhabditis elegans. In the process of defining the sequence compositional characteristics, we found that the 10.5-nt periodicity, the sequence signature of DNA double helix, may not be sufficient for cross-nucleosome positioning but provides essential guiding rails to facilitate positioning. We further dissected nucleosome-protected sequences and identified a strong positive purine (AG) gradient from the 5′-end to the 3′-end, and also learnt that the nucleosome-enriched regions are GC-rich as compared to the nucleosome-free sequences as purine content is positively correlated with GC content. Sequence characterization allowed us to develop a hidden Markov model (HMM) algorithm for decoding nucleosome positioning computationally, and based on a set of training data from the fifth chromosome of C. elegans, our algorithm predicted 60%-70% of the well-positioned nucleosomes, which is 15%-20% higher than random positioning. We concluded that nucleosomes are not randomly positioned on DNA sequences and yet bind to different genome regions with variable stability, well-positioned nucleosomes leave sequence signatures on DNA, and statistical positioning of nucleosomes across genome can be decoded computationally based on these sequence signatures.