m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence

Abstract

Background: The process of nucleotides modification or methyl groups addition to nucleotides is known as post-transcriptional modification (PTM). 1-methyladenosine (m1A) is a type of PTM formed by adding a methyl group to the nitrogen at the 1st position of the adenosine base. Many human disorders are associated with m1A, which is widely found in ribosomal RNA and transfer RNA. Objective: The conventional methods such as mass spectrometry and site-directed mutagenesis proved to be laborious and burdensome. Systematic identification of modified sites from RNA sequences is gaining much attention nowadays. Consequently, an extreme gradient boost predictor, m1A-Pred, is developed in this study for the prediction of modified m1A sites. Method: The current study involves the extraction of position and composition-based properties within nucleotide sequences. The extraction of features helps in the development of the features vector. Statistical moments were endorsed for dimensionality reduction in the obtained features. Results: Through a series of experiments using different computational models and evaluation methods, it was revealed that the proposed predictor, m1A-pred, proved to be the most robust and accurate model for the identification of modified sites. Availability and Implementation: To enhance the research on m1A sites, a friendly server was also developed which was the final phase of this research.