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

Abstract

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.

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach

“…The benchmark and independent datasets in this paper are collected from different sources [ 3 , 5 , 6 , 8 , 9 , 16 , 17 , 18 , 19 ] to measure the efficiency of the proposed model for a fair comparison with current predictors. These datasets contain eight different species, namely Caenorhabditis elegans (C. elegans) , Drosophila melanogaster (D. melanogaster) , Arabidopsis thaliana (A. thaliana) , Escherichia coli (E. coli) , Geoalkalibacter subterraneus (G. subterraneus) , Geobacter pickeringii (G. pickeringi) , Fragaria vesca (F. vesca) , and Rosa chinensis (R. chinensis) .…”

“…Z. Zhao et al (2020) [ 17 ] proposed a model of DNA N4-methylcytosine sites via boost-learning various types of sequence features. The DNA sequence was first encoded with one-hot binary (OHB), sequential nucleotide frequency (SNF), K-nucleotide frequency (KNF), K-spectrum nucleotide pair frequency (KSNPF), and PseDNC.…”

Bifunctional Role of Fe(II)/2OG-Dependent TET Family 5-Methylcytosine Dioxygenases and ALKBH2,3 in Modified Cytosine Demethylation

“…Despite significant studies on 5mC and 3mC that have been carried out, work on 4mC is still in the rudimentary phase [24]. 4mC is primarily present in prokaryotes however, current high-sensitivity methods can detect it in eukaryotes also, such as single-molecule real-time sequencing, [25].…”

iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

“…It has been extensively detected in viruses, bacteria, protists, fungi, algae, etc. As the other important epigenetic modification, 4mC protects host DNA from the degradation of restriction enzymes and corrects prokaryotic DNA replication errors, and controls the DNA replication and cell cycle of prokaryotes [ 7 ]. Therefore, DNA methylation identification is fundamentally essential for revealing the functional mechanisms.…”