i6mA-CNN: a convolution based computational approach towards identification of DNA N6-methyladenine sites in rice genome
This provides an automated computational method to identify 6mA sites in plant genomes, saving time and money compared to experimental approaches, though it is incremental as it builds on existing feature integration techniques.
The study developed i6mA-CNN, a convolutional neural network tool to identify DNA N6-methyladenine sites in the rice genome, achieving an area under the ROC curve of 0.98 and an overall accuracy of 0.94 on a benchmark dataset.
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.