pRNAm-PC

pRNAm-PC predicts N6-methyladenosine (m6A) sites in RNA sequences to identify adenine methylation at GAC motifs for studying RNA modification mechanisms in viruses and eukaryotes.

Key Features:

• Pseudo dinucleotide composition (PseDNC) encoding: Represents RNA sequences using PseDNC to capture dinucleotide composition and sequence-order information.
• Physicochemical property integration: Incorporates physical-chemical matrices into feature construction.
• Auto-covariance and cross-covariance transformations: Applies auto-covariance and cross-covariance calculations to transform physicochemical matrices into predictive features.
• Motif-specific prediction: Targets m6A occurrence at adenine residues within the GAC motif.
• Performance evaluation: Validated by jackknife testing and reported to achieve improved accuracy and stability compared with existing predictors.

Scientific Applications:

• m6A site mapping: High-throughput computational identification of m6A sites in RNA sequences.
• RNA modification research: Facilitates investigation of m6A roles and mechanisms in viruses and eukaryotes.
• Transcriptomics and epigenetics analyses: Supports analyses of methylation patterns in transcriptome and epigenetic studies.
• Comparative RNA analysis: Enables assessment of m6A distribution across diverse RNA species.

Methodology:

Uses pseudo dinucleotide composition (PseDNC) with physicochemical matrices transformed by auto-covariance and cross-covariance, and performance was assessed using jackknife testing.