The recent discovery of numerous
DNA N6-methyladenine
(6mA) sites has transformed our perception about the roles of 6mA
in living organisms. However, our ability to understand them is hampered
by our inability to identify 6mA sites rapidly and cost-efficiently
by existing experimental methods. Developing a novel method to quickly
and accurately identify 6mA sites is critical for speeding up the
progress of its function detection and understanding. In this study,
we propose a novel computational method, called I-DNAN6mA, to identify
6mA sites and complement experimental methods well, by leveraging
the base-pairing rules and a well-designed three-stage deep learning
model with pairwise inputs. The performance of our proposed method
is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental
results demonstrate that I-DNAN6mA achieves area under the receiver
operating characteristic curve values of 0.967, 0.963, 0.947, 0.976,
and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew’s
correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and
0.924 on five benchmark data sets, respectively, and outperforms several
existing state-of-the-art methods. To our knowledge, I-DNAN6mA is
the first approach to identify 6mA sites using a novel image-like
representation of DNA sequences and a deep learning model with pairwise
inputs. I-DNAN6mA is expected to be useful for locating functional
regions of DNA.