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.

Identification of 6-methyladenosine sites using novel feature encoding methods and ensemble models

“…The dataset is divided into “k” disjoint folds or partitions, where each fold is used as a testing set once while the remaining “k−1” folds are used for training the model. This process is repeated multiple times to ensure a more stringent and robust test 45 . In this specific study, “k” was set to 10, meaning the dataset was split into 10 folds.…”

m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models

“…The AAPIV (accumulated information of individual nucleotide bases) is a method used to provide information on the frequency of each individual nucleotide base in a sequence [ 25 ]. This method is responsible for collecting and accumulating data related to the occurrence of each nucleotide base, including single and paired nucleotide bases [ 26 , 27 ].…”

Dynamic regulation and key roles of ribonucleic acid methylation

“… Yao et al (2022) built the framework m1ARegpred (m1A regulators substrate prediction), m1ARegpred was achieved based on ML and the combination of sequence-derived and genome-derived features. Suleman and Khan (2022) developed an extreme gradient boost predictor named as m1A-Pred for the prediction of modified m1A sites.…”