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Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition

MOTIVATION: Nucleosome positioning plays important roles in many eukaryotic intranuclear processes, such as transcriptional regulation and chromatin structure formation. The investigations of nucleosome positioning rules provide a deeper understanding of these intracellular processes. RESULTS: Nucle...

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Detalles Bibliográficos
Autor principal: Awazu, Akinori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860184/
https://www.ncbi.nlm.nih.gov/pubmed/27563027
http://dx.doi.org/10.1093/bioinformatics/btw562
Descripción
Sumario:MOTIVATION: Nucleosome positioning plays important roles in many eukaryotic intranuclear processes, such as transcriptional regulation and chromatin structure formation. The investigations of nucleosome positioning rules provide a deeper understanding of these intracellular processes. RESULTS: Nucleosome positioning prediction was performed using a model consisting of three types of variables characterizing a DNA sequence—the number of five-nucleotide sequences, the number of three-nucleotide combinations in one period of a helix, and mono- and di-nucleotide distributions in DNA fragments. Using recently proposed stringent benchmark datasets with low biases for Saccharomyces cerevisiae, Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster, the present model was shown to have a better prediction performance than the recently proposed predictors. This model was able to display the common and organism-dependent factors that affect nucleosome forming and inhibiting sequences as well. Therefore, the predictors developed here can accurately predict nucleosome positioning and help determine the key factors influencing this process. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.