Selective recognition of
            <i>N</i>
            4-methylcytosine in DNA by engineered transcription-activator-like effectors

Rathi, Preeti; Maurer, Sara; Summerer, Daniel

doi:10.1098/rstb.2017.0078

Cited by 36 publications

(31 citation statements)

References 55 publications

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“…Gene-specific editing in vitro and in vivo is a hot topic, and the different methodologies available are compared by Rots [4]. One of these, the selective recognition of N4-methylcytosine by engineered transcription activators, is reported by Summerer [5]. While DNA is relatively faithful to the four Watson-Crick bases and variants are relatively rare, the opposite is true with non-coding RNAs, which rarely consist of only the canonical adenine, guanine, cytosine and uracil bases.…”

Section: What Is Heritable and What Is Not?mentioning

confidence: 99%

Epigenetics: the first 25 centuries

Ganesan

2018

Phil. Trans. R. Soc. B

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One contribution of 18 to a discussion meeting issue 'Frontiers in epigenetic chemical biology'. Epigenetics is a natural progression of genetics as it aims to understand how genes and other heritable elements are regulated in eukaryotic organisms. The history of epigenetics is briefly reviewed, together with the key issues in the field today. This themed issue brings together a diverse collection of interdisciplinary reviews and research articles that showcase the tremendous recent advances in epigenetic chemical biology and translational research into epigenetic drug discovery.

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Section: What Is Heritable and What Is Not?mentioning

confidence: 99%

Epigenetics: the first 25 centuries

Ganesan

2018

Phil. Trans. R. Soc. B

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“…Dynamic DNA modifications, such as methylation and demethylation have an essential role in the regulation of gene expression. DNA methylation as a heritable epigenetic marker is one type of chemical modification of DNA, which alters genetic performance without altering the DNA sequence [ 1 , 2 ]. Several researches have shown that it has the ability to change DNA protein interactions, DNA conformation, DNA stability, and chromatin structure.…”

Section: Introductionmentioning

confidence: 99%

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

Wahab

Mahmoudi

Kim³

et al. 2020

Cells

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N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.

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“…As an essential epigenetic modification, DNA base methylation expands the DNA content and plays crucial roles in regulating various cellular processes [ 1 – 3 ]. According to the location where a methylated group occurs in the DNA sequence, there are many kinds of DNA base methylation.…”

Section: Introductionmentioning

confidence: 99%

Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

et al. 2020

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Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

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Selective recognition of N 4-methylcytosine in DNA by engineered transcription-activator-like effectors

Cited by 36 publications

References 55 publications

Epigenetics: the first 25 centuries

Epigenetics: the first 25 centuries

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

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