On the prediction of non-CG DNA methylation using machine learning.

Autor:	Sereshki S; Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA., Lee N; Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA., Omirou M; Department of Agrobiotechnology, Agricultural Microbiology Laboratory, Agricultural Research Institute, Nicosia 1516, Cyprus., Fasoula D; Department of Plant Breeding, Agricultural Research Institute, Nicosia 1516, Cyprus., Lonardi S; Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.
Jazyk:	angličtina
Zdroj:	NAR genomics and bioinformatics [NAR Genom Bioinform] 2023 May 17; Vol. 5 (2), pp. lqad045. Date of Electronic Publication: 2023 May 17 (Print Publication: 2023).
DOI:	10.1093/nargab/lqad045
Abstrakt:	DNA methylation can be detected and measured using sequencing instruments after sodium bisulfite conversion, but experiments can be expensive for large eukaryotic genomes. Sequencing nonuniformity and mapping biases can leave parts of the genome with low or no coverage, thus hampering the ability of obtaining DNA methylation levels for all cytosines. To address these limitations, several computational methods have been proposed that can predict DNA methylation from the DNA sequence around the cytosine or from the methylation level of nearby cytosines. However, most of these methods are entirely focused on CG methylation in humans and other mammals. In this work, we study, for the first time, the problem of predicting cytosine methylation for CG, CHG and CHH contexts on six plant species, either from the DNA primary sequence around the cytosine or from the methylation levels of neighboring cytosines. In this framework, we also study the cross-species prediction problem and the cross-context prediction problem (within the same species). Finally, we show that providing gene and repeat annotations allows existing classifiers to significantly improve their prediction accuracy. We introduce a new classifier called AMPS (annotation-based methylation prediction from sequence) that takes advantage of genomic annotations to achieve higher accuracy. (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)
Databáze:	MEDLINE
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