ArticleChromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology2013
An analysis and prediction of nucleosome positioning based on information content.
Article in Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 8 citations in OpenAlex.
- [Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Article
- Sequence analysis of origins of replication in the Saccharomyces cerevisiae genomes.Frontiers in microbiology · 2014Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nucleosome positioning plays a key role in the regulation of many biological processes. In this study, the statistical difference of information content was investigated in nucleosome and linker DNA regions across eukaryotic organisms. By analyzing the information redundancy, D k , in Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans genomes, the short-range dominance of nucleotide correlation in nucleosome and linker DNA regions was confirmed. Significant difference of the D k value between the nucleosome and linker DNA regions was also found. The underlying reason for many successful oligonucleotide-based predictions of nucleosome positioning in eukaryotic model organisms may be attributed to the short-range dominance of nucleotide correlation in the nucleosome and linker DNA regions. When applying power spectrum analysis to the nucleosome and linker DNA regions, some obvious differences in sequence periodic signals were observed. The parameter F k was introduced to describe particular base correlation. Furthermore, the support vector machine combining F k was used to classify nucleosome and linker DNA regions in Homo sapiens, Oryzias latipes, C. elegans, Candida albicans, and S. cerevisiae. Independent test demonstrated that a good performance can be achieved by using this algorithm. This result further revealed that base correlation information has an important role in nucleosome positioning.
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