MERP-DNA: A Computational and Genetic Bioinformatics Framework for Tracing Sequence Information from DNA to RNA and Protein

Modern genomic analysis requires integration of genomic variation records, standardized reference transcripts, coding sequences, and molecular sequence resources. Building on the previously developed MERP-DNA framework, this study extends and evaluates its application to large-scale genomic variant analysis by integrating ClinVar, the Matched Annotation from NCBI and EMBL-EBI (MANE) reference set, RefSeq sequence resources, and coding-sequence information. RNA and protein resources were used to establish and independently validate transcript, coding-sequence, and protein reference relationships, while MERP-DNA was applied to validated DNA sequences and genomic variants. The workflow was implemented as a modular MATLAB-based architecture involving approximately twenty scripts and multiple intermediate datasets for data parsing, quality control, genomic-to-transcript mapping, coding-sequence identification, reference validation, MERP calculation, statistical analysis, and visualization. From 15,051 ClinVar records associated with 20 selected MANE genes, 11,098 were mapped to coding regions. Reference validation identified 1,818 matching records, and 1,808 variants remained valid for MERP analysis. All 1,808 variants were successfully processed. The mean MERP distance was 37,600.88, with a median of 31,632.5. First trajectory divergence matched the mutation position in all 1,808 cases. Raw MERP distance showed a very strong positive monotonic relationship with downstream suffix length (Spearman’s ρ = 0.999777), while linear regression achieved R² = 0.999590. After suffix normalization, no significant associations were detected with mutation position, substitution class, genomic region, or fraction of different states. These findings demonstrate the feasibility of a reproducible MANE-integrated computational framework for numerical DNA sequence representation and trajectory-based genomic variant analysis.