| Summary: | Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease marked by
motor neuron degeneration and complex genetic underpinnings. Despite growing research into
its molecular pathology, the role of codon usage bias (CUB) in ALS-related genes remains
largely unexplored. In this study, we analyzed 607 ALS-associated genes and compared them
with 138 human housekeeping genes to examine differences in codon usage. Using tools such as
CAIcal and CodonW, we assessed nucleotide composition, GC content, RSCU values, ENC,
neutrality plots, and parity plots. Statistical testing (Mann–Whitney U test) revealed 15 codons
with significant usage differences between ALS and non-disease genes. These codons were
further used as features in machine learning models (KNN, SVM, RF), where Random Forest
achieved the highest classification accuracy (85.9%). Our results indicate that ALS genes are
characterized by GC-rich codon preferences influenced by natural selection and functional
constraints, likely related to translational efficiency and gene expression optimization. This
codon-level distinction has implications for ALS biomarker discovery and the rational design of
therapeutic gene constructs.
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