PACES

PACES predicts N4-acetylcytidine (ac4C) modification sites in human mRNA sequences to identify potential acetylated cytidine positions that influence mRNA stability, processing, and translation.

Key Features:

• ac4C site prediction: Predicts N4-acetylcytidine (ac4C) modification sites within human mRNA sequences.
• Classifier architecture: Integrates two random forest classifiers for site prediction.
• Sequence feature representation: Uses position-specific dinucleotide sequence profiles and K-nucleotide frequencies as input features.
• Input data: Analyzes genomic sequences as input to identify candidate modified sites.
• Training model: Generates predictions based on a robust trained classification model.

Scientific Applications:

• Motif identification: Identification and exploration of potential ac4C-modified motifs within mRNA.
• Transcriptome characterization: Characterizing the distribution of ac4C across the human transcriptome and its potential effects on mRNA stability, processing, and translation.
• Post-transcriptional regulation studies: Aiding studies of post-transcriptional gene expression mechanisms and prioritizing candidate ac4C sites for experimental follow-up.

Methodology:

Prediction is performed by integrating two random forest classifiers trained on features derived from position-specific dinucleotide sequence profiles and K-nucleotide frequencies applied to input genomic/mRNA sequences.