iR6mA-RNN: Identifying N6-Methyladenosine Sites in Eukaryotic Transcriptomes using Recurrent Neural Networks and Sequence-embedded Features

Abstract

As a common biological event observed in all living creatures, RNA modification is an essential post-transcriptional factor that regulates the activity, localization, and stability of RNAs. Multiple diseases are associated with RNA modification. N6-methyladenosine (6mA) modification of RNA is one of the most frequent events that affect the translational processes and structural stability of modified transcripts and control transcriptional processes in cell state maintenance and transition. To detect 6mA sites in eukaryotic transcriptomes, a number of computational models were developed as online applications to assist experimental scientists in reducing human effort and budget. However, most of those online web servers are now either outdated or inaccessible. In this study, we propose iR6mA-RNN, an effective computational framework using recurrent neural networks and sequence-embedded features, to predict possible 6mA sites in eukaryotic transcriptomes. When tested on an independent test set, the proposed model achieved an area under the receiver operating characteristic curve of 0.7972 and an area under the precision-recall curve of 0.7785. Our model also outperformed the other two existing methods. Results from another sensitivity analysis confirmed the stability of the model as well.