Abstract
DNA N6-methyladenine (6mA) has subsequently been identified as an important epigenetic modification which plays an important role in various cellular processes. The precise discrimination of N6-methyladenine (6mA) in genomes is required to recognize its biological functions. Although, we have several experimental techniques for the identification of 6mA-sites, in silico prediction has evolved as an alternative approach due to high-cost and labor-intense in experimental techniques. Taking into account, the implementation of an efficient and accurate model for identification of N6-methyladenine is of high priority. Several machine learning and deep learning models have already been developed to classify genome-wide 6mA sites. However, their success in predicting 6mA sites still has room for improvement. Based on this, we proposed a novel deep learning based model for the prediction of DNA N6-methyladenine sites in rice genomes. We built our model based on a special architecture called SpinalNet using DNA 6mA sites in rice genome and obtained an accuracies of 94.31% and 94.77% with an MCCs of 0.88 and 0.89 on two different datasets. The model generalizes well to other genomes as well, validated through cross-species testing. The results validate that the proposed model produces better scores than existing models regarding all evaluation parameters. A user-friendly webserver is made available at http://nsclbio.jbnu.ac.kr/tools/SpineNet6mA/.
Highlights
DNA N6-methyladenine (6mA) is an important epigenetic modification of diverse species genomes, found in bacteria, eukaryotes, and archaea [1], [2]
Of DNA 6mA may provide a bottomless understanding of the process of epigenetic modification
In silico prediction of DNA 6mA sites in a genome have evolved as an alternative approach due to the constraint of labor-intensive and costly experiments
Summary
DNA N6-methyladenine (6mA) is an important epigenetic modification of diverse species genomes, found in bacteria, eukaryotes, and archaea [1], [2]. Tahir et al, proposed another model named iDNA6mA for the same purpose of identification of 6mA in the rice genome They again trained and tested their model on the same dataset used by Chen et al, and outperformed i6mA-Pred while predicting 6mA sites [10]. We have proposed a deep architecture using SpinalNet for the first time for sequence data to get high accuracy while predicting the 6mA sites in rice genome. The input to the SpinalNet-block is the 960-d vector which is the concatenation of the learned features from the two CNN branches of the model
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