Base-9 Encoding in Genomic Structure: A Framework for Codon Pattern Recognition, Compression, and Functional Mapping

This paper introduces a novel mathematical model for genomic data representation based on base-9 encoding of DNA codons. By converting standard triplet codons from base-4 to base-9 and analyzing their modular residue bands, the system reveals consistent clustering of synonymous codons, significant compression gains, and alignment with known protein folding domains. The work includes detailed mapping tables, compression benchmarks, structural overlays, statistical clustering analysis, and implementation-ready pseudocode. Designed for application in genomics, bioinformatics, AI model input, and long-term genomic storage, this framework offers a lightweight, symbolic representation of biological data that may augment or simplify existing tools. Authored independently and released under CC BY-NC-ND 4.0 with DOI and ORCID attribution.