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Related Experiment Video

Updated: Oct 3, 2025

A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues

A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues

Published on: December 5, 2016

m6A-Finder: Detecting m6A methylation sites from RNA transcriptomes using physical and statistical properties based

Asad Khan¹, Hafeez Ur Rehman¹, Usman Habib²

¹Department of Computer Science, National University of Computer & Emerging Sciences, Hayatabad, 24720 Peshawar, Pakistan.

Computational Biology and Chemistry

|February 15, 2022

Summary

N6-methyladenosine (m6A) site identification is crucial for understanding diseases. A new predictor, m6A-Finder, uses combined sequence features and feature selection to improve m6A site detection accuracy in Saccharomyces cerevisiae.

Keywords:

Feature selection M6A modification sites M6A-Finder MRMR RNA SVM

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

N6-methyladenosine (m6A) is a prevalent RNA modification regulating vital cellular processes.
Aberrant m6A sites are implicated in various diseases, including cancer and cardiovascular conditions.
Accurate m6A site identification is challenging due to complex sequence patterns.

Purpose of the Study:

To develop a novel and accurate predictor for identifying m6A sites.
To address the limitations of existing predictors in precise m6A site detection.
To investigate the utility of combined sequence order features for m6A site prediction.

Main Methods:

A new predictor, m6A-Finder, was developed.
Features were extracted based on global (physical properties) and local (statistical) sequence order.
The Minimum Redundancy Maximum Relevance (mRMR) algorithm was employed for feature selection to mitigate overfitting.
The method was evaluated on the Saccharomyces cerevisiae dataset.

Main Results:

m6A-Finder achieved high performance metrics.
Accuracy: 82.02%
Sensitivity: 82.10%
Specificity: 81.94%
Matthew's Correlation Coefficient: +0.64.

Conclusions:

The proposed m6A-Finder predictor effectively identifies m6A sites by integrating diverse sequence features.
Feature selection using mRMR enhances prediction accuracy and reduces overfitting.
The findings contribute to a better understanding of m6A site regulation and its role in diseases.