Abstract
An important role is played by N6-methyladenosine (m6A) in RNA methylation modification. The modification information is crucially required for development in the field of medicine. Biochemical experiments for m6A identification have demonstrated high-quality results. However, this process is not a feasible solution due to its cost and time constraints. Recently, artificial intelligence has played a role to produce effective models which can be utilized for speedy and efficient identification of N6-methyladenosine sites. In consensus, a computational model named as m6A-NeuralTool is proposed in this study. The m6A-NeuralTool makes the final prediction for the identification of m6A sites by applying majority voting on three different sub-architectures. These sub-architectures uses a set of Convolution layers to extract the important features from the one-hot encoded input sequence. Further, one of the sub-architectures uses fully connected layers for the classification while the other two uses Support Vector Machine and Naive Bayes. The proposed m6A-NeuralTool is evaluated on four different species datasets using 10-fold cross-validation, where the proposed tool outperformed the existing state-of-art models for modification identification of m6A sites. When compared with existing models the proposed model increased the accuracy by 14.8%, 8.8%, 6.3%, and 4.9% for the datasets of Saccharomyces cerevisiae (S. cerevisiae), Arabidopsis thaliana (A. thaliana), Mus musculus (M. musculus) and Homo sapiens (H. sapiens) species respectively. The proposed model will allow the researchers from the field of bioinformatics and medicine to accurately identify the modification in m6A sites and use the information in the development of different products which would be beneficial for the community. The m6A-NeuralTool can be accessed freely at: http://nsclbio.jbnu.ac.kr/tools/m6A-NeuralTool/.
Highlights
The RNA post-transcriptional modification (PTM) is a common phenomenon of biological process
The aforementioned models have undoubtedly improved the performance of identifying m6A sites but still, a great potential for improvement is available
Dataset for H. sapien genome was proposed in 2017 by Chen et al The dataset consists of 2260 sequences out of which half are methyladenosine while the remaining half are non-methyladenosine [17]
Summary
The RNA post-transcriptional modification (PTM) is a common phenomenon of biological process. There are more than 160 RNA modifications among which Methyladenosine (m6A) takes place most frequently. The N6-Methyladenosine modification holds a significant role. The associate editor coordinating the review of this manuscript and approving it for publication was Nuno Garcia. In majority of the cell cycles like cell growth [1], cell progression [2], affecting translation performance [3] and others. Our familiarity with the behaviour of methyladenosine is unintelligible, to increase the knowledge on the functionality of m6A, it is important to identify its position in transcriptomes.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
