  DNA N6-methylation (6mA) in Adenine nucleotide is a post replication
modification and is responsible for many biological functions. Experimental
methods for genome wide 6mA site detection is an expensive and manual labour
intensive process. Automated and accurate computational methods can help to
identify 6mA sites in long genomes saving significant time and money. Our study
develops a convolutional neural network based tool i6mA-CNN capable of
identifying 6mA sites in the rice genome. Our model coordinates among multiple
types of features such as PseAAC inspired customized feature vector, multiple
one hot representations and dinucleotide physicochemical properties. It
achieves area under the receiver operating characteristic curve of 0.98 with an
overall accuracy of 0.94 using 5 fold cross validation on benchmark dataset.
Finally, we evaluate our model on two other plant genome 6mA site
identification datasets besides rice. Results suggest that our proposed tool is
able to generalize its ability of 6mA site identification on plant genomes
irrespective of plant species. Web tool for this research can be found at:
https://cutt.ly/Co6KuWG. Supplementary data (benchmark dataset, independent
test dataset, comparison purpose dataset, trained model, physicochemical
property values, attention mechanism details for motif finding) are available
at https://cutt.ly/PpDdeDH.
