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Identification of DNA N6-methyladenine sites by integration of sequence features.


ABSTRACT: BACKGROUND:An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N6-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources and time, especially given the rapid accumulation of sequencing data. RESULTS:In this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron-ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets. CONCLUSIONS:p6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https://github.com/Konglab404/p6mA.

SUBMITTER: Wang HT 

PROVIDER: S-EPMC7038560 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Identification of DNA N<sup>6</sup>-methyladenine sites by integration of sequence features.

Wang Hao-Tian HT   Xiao Fu-Hui FH   Li Gong-Hua GH   Kong Qing-Peng QP  

Epigenetics & chromatin 20200224 1


<h4>Background</h4>An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N<sup>6</sup>-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources  ...[more]

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