Journal
GENOMICS
Volume 113, Issue 1, Pages 582-592Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2020.09.054
Keywords
Sequence analysis; DNA N6-methyladenine; Machine learning; RFECV; Stacking
Funding
- National Research Foundation of Korea(NRF) - Korea government (MSIT) [2020R1A2C2005612]
- Brain Research Program of the National Research Foundation (NRF) - Korean government (MSIT) [NRF-2017M3C7A1044816]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2019R1A6A3A01094685]
- National Research Foundation of Korea [2019R1A6A3A01094685, 2020R1A2C2005612] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study proposes a machine learning technique to identify DNA N6-methyladenine (6 mA) sites in Rosa chinensis and Fragaria vesca. By using recursive feature elimination with cross-validation strategy to extract optimal feature subset from five different DNA sequence encoding schemes, a double layers of machine learning-based stacking model was trained to create a bioinformatics tool named 'i6mA-stack'.
DNA N6-methyladenine (6 mA) is an epigenetic modification that plays a vital role in a variety of cellular processes in both eukaryotes and prokaryotes. Accurate information of 6 mA sites in the Rosaceae genome may assist in understanding genomic 6 mA distributions and various biological functions such as epigenetic inheritance. Various studies have shown the possibility of identifying 6 mA sites through experiments, but the procedures are time-consuming and costly. To overcome the drawbacks of experimental methods, we propose an accurate computational paradigm based on a machine learning (ML) technique to identify 6 mA sites in Rosa chinensis (R.chinensis) and Fragaria vesca (F.vesca). To improve the performance of the proposed model and to avoid overfitting, a recursive feature elimination with cross-validation (RFECV) strategy is used to extract the optimal number of features (ONF) subset from five different DNA sequence encoding schemes, i.e., Binary Encoding (BE), Ring-Function-Hydrogen-Chemical Properties (RFHC), Electron-Ion-Interaction Pseudo Potentials of Nucleotides (EIIP), Dinucleotide Physicochemical Properties (DPCP), and Trinucleotide Physicochemical Properties (TPCP). Subsequently, we use the ONF subset to train a double layers of ML-based stacking model to create a bioinformatics tool named 'i6mA-stack'.
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