ArticleNAR genomics and bioinformatics2023
On the prediction of non-CG DNA methylation using machine learning.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.Computational and structural biotechnology journal · 2026Article
- Reproductive Dysfunction and Testicular Epigenetic Alterations in Male Rats with Experimental Amebiasis: Evaluation of SPATA6 Methylation and Therapeutic Outcomes.Acta parasitologica · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.Briefings in bioinformatics · 2025Article
- Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model.Briefings in bioinformatics · 2025Article
- Predicting Differentially Methylated Cytosines in TET and DNMT3 Knockout Mutants via a Large Language Model.bioRxiv : the preprint server for biology · 2024Article
- Structural basis of water-mediated cis Watson-Crick/Hoogsteen base-pair formation in non-CpG methylation.Nucleic acids research · 2024Article
- SSE-TSR: An Approach to Integrate Secondary Structure Elements Into Triangular Spatial Relationships for Protein Classification.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.