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i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome

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dc.contributor.authorHasan, MM-
dc.contributor.authorManavalan, B-
dc.contributor.authorKhatun, MS-
dc.contributor.authorKurata, H-
dc.date.accessioned2022-10-28T05:28:45Z-
dc.date.available2022-10-28T05:28:45Z-
dc.date.issued2020-
dc.identifier.issn0141-8130-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/22425-
dc.description.abstractOne of the most important epigenetic modifications is N4-methylcytosine, which regulates many biological processes including DNA replication and chromosome stability. Identification of N4-methylcytosine sites is pivotal to understand specific biological functions. Herein, we developed the first bioinformatics tool called i4mC-ROSE for identifying N4-methylcytosine sites in the genomes of Fragaria vesca and Rosa chinensis in the Rosaceae, which utilizes a random forest classifier with six encoding methods that cover various aspects of DNA sequence information. The i4mC-ROSE predictor achieves area under the curve scores of 0.883 and 0.889 for the two genomes during cross-validation. Moreover, the i4mC-ROSE outperforms other classifiers tested in this study when objectively evaluated on the independent datasets. The proposed i4mC-ROSE tool can serve users' demand for the prediction of 4mC sites in the Rosaceae genome. The i4mC-ROSE predictor and utilized datasets are publicly accessible at http://kurata14.bio.kyutech.ac.jp/i4mC-ROSE/.-
dc.language.isoen-
dc.subject.MESHAlgorithms-
dc.subject.MESHComputational Biology-
dc.subject.MESHCytosine-
dc.subject.MESHDNA Methylation-
dc.subject.MESHDatabases, Genetic-
dc.subject.MESHEpigenesis, Genetic-
dc.subject.MESHEpigenomics-
dc.subject.MESHGenome, Plant-
dc.subject.MESHMachine Learning-
dc.subject.MESHROC Curve-
dc.subject.MESHReproducibility of Results-
dc.subject.MESHRosaceae-
dc.subject.MESHWeb Browser-
dc.titlei4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome-
dc.typeArticle-
dc.identifier.pmid31805335-
dc.subject.keywordDNA methylation-
dc.subject.keywordLinear regression-
dc.subject.keywordMachine learning-
dc.subject.keywordN4-methylcytosine site-
dc.subject.keywordSequence encoding-
dc.contributor.affiliatedAuthorManavalan, B-
dc.type.localJournal Papers-
dc.identifier.doi10.1016/j.ijbiomac.2019.12.009-
dc.citation.titleInternational journal of biological macromolecules-
dc.citation.volume157-
dc.citation.date2020-
dc.citation.startPage752-
dc.citation.endPage758-
dc.identifier.bibliographicCitationInternational journal of biological macromolecules, 157. : 752-758, 2020-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.identifier.eissn1879-0003-
dc.relation.journalidJ001418130-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Physiology
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