m5CPred-SVM

m5CPred-SVM predicts 5-methylcytosine (m5C) sites in RNA sequences to enable accurate identification of post-transcriptional m5C modifications for studies of RNA metabolism and cell fate decisions.

Key Features:

• Species Coverage: Predicts m5C sites for Homo sapiens, Mus musculus, and Arabidopsis thaliana.
• Feature Extraction: Uses six distinct sequence-based feature types derived from RNA segments and selects informative features via a sequential forward feature selection strategy.
• Machine Learning Algorithm: Implements a support vector machine (SVM) classifier, reported to outperform other evaluated learning algorithms.
• Benchmarking and Validation: Trained on benchmark datasets collected from three recently published methods and shown through extensive comparisons to achieve substantially higher prediction accuracy than existing computational methods.

Scientific Applications:

• m5C site identification: Provides predicted m5C site locations to support identification of post-transcriptional modification sites in RNA.
• Functional studies of RNA: Enables analyses of RNA metabolism and investigations into cell fate decisions by supplying predicted m5C site information.

Methodology:

Extracts six sequence-based feature types from RNA segments, applies sequential forward feature selection, and classifies sites using a support vector machine trained and validated on benchmark datasets collected from three recently published methods with extensive comparative evaluation.