Abstract:
DNA N6-methyladenine (6mA) is an epigenetic modification, which is involved in many
biological regulation processes like DNA replication, DNA repair, transcription, and gene
expression regulation. The widespread presence of this 6mA modification
in eukaryotes has been unclear until recently. Therefore, for eukaryotes, the study of
DNA 6mA is insufficient. Accurate identification of 6mA sites genome-wide provides a
deeper understanding of the epigenetic modification process and the biological processes
it involves. Existing experimental techniques are time-consuming and computational
machine learning methods have room for performance improvement. DNA N6-
methyladenine prediction in cross-species shows low performance. Hence, there is a need
for a highly accurate, time-efficient method to predict the distribution of 6mA sites
in eukaryotes. Deep learning models have shown higher accuracy in many experiments in
bioinformatics. In this regard, we develop a customized VGG16 based model using
convolution neural networks. We introduce a novel 3-dimensional encoding mechanism
extending the one-hot encoding method for the given DNA sequences of length 41bp to
support the VGG16 model input. Specifically, the 10-fold cross-validation on the
benchmark datasets for the proposed model achieves higher accuracies for crossspecies, Rice, and M. musculus genomes. The cross-species data set was prepared by
integrating the benchmark datasets of Rice, and M. musculus. This model outperforms the
existing computational tools SNNRice6mA, ilM-CNN with a current validation accuracy
of 97% for the prediction of 6mA sites. The model trained with cross-species data
predicts 6mA sites of other species Arabidopsis Thaliana, Rosa Chinensis, Drosophila,
and Yeast with a prediction accuracy over 70%. Thus, this model can be used for the
genome-wide prediction of 6mA sites in eukaryotes.
Keywords: DNA Sequence encoding method, Deep learning, Epigenetics, Bioinformatics,
DNA N6-Methyladenine
