Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis

This study investigates codon usage patterns and biases in 258 Multiple Sclerosis (MS)-associated genes compared to 137 housekeeping (HK) genes, using computational and statistical approaches. Codon usage indices such as RSCU, CAI, ENC, GC3%, and the P2 index were analyzed alongside rare amino aci...

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Autor principal: Abida, Maisha
Format: Thesis
Idioma:anglès
Publicat: AUW 2025
Accés en línia:https://repository.auw.edu.bd/handle/123456789/534
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author Abida, Maisha
author_facet Abida, Maisha
author_sort Abida, Maisha
collection institutional Repository
description This study investigates codon usage patterns and biases in 258 Multiple Sclerosis (MS)-associated genes compared to 137 housekeeping (HK) genes, using computational and statistical approaches. Codon usage indices such as RSCU, CAI, ENC, GC3%, and the P2 index were analyzed alongside rare amino acid usage, mutation-driven nucleotide skew, neutrality and parity plots, and codon pair bias. Machine learning models (SVM, RF, KNN) were trained on different codon feature sets to classify MS vs. HK genes. Results showed that MS genes exhibit a GC-rich codon bias, strong translational selection (P2 > 0.5), and mutation pressure predominantly at third codon positions. Seventeen codons were identified as significantly different via Mann-Whitney U test. SVM achieved the highest classification accuracy (81%) with full codons, while feature selection improved performance for other models. The findings underscore the influence of both compositional and adaptive forces on MS gene codon usage, with potential implications for gene therapy and synthetic design.
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spelling 123456789-5342026-02-18T06:09:53Z Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis Abida, Maisha This study investigates codon usage patterns and biases in 258 Multiple Sclerosis (MS)-associated genes compared to 137 housekeeping (HK) genes, using computational and statistical approaches. Codon usage indices such as RSCU, CAI, ENC, GC3%, and the P2 index were analyzed alongside rare amino acid usage, mutation-driven nucleotide skew, neutrality and parity plots, and codon pair bias. Machine learning models (SVM, RF, KNN) were trained on different codon feature sets to classify MS vs. HK genes. Results showed that MS genes exhibit a GC-rich codon bias, strong translational selection (P2 > 0.5), and mutation pressure predominantly at third codon positions. Seventeen codons were identified as significantly different via Mann-Whitney U test. SVM achieved the highest classification accuracy (81%) with full codons, while feature selection improved performance for other models. The findings underscore the influence of both compositional and adaptive forces on MS gene codon usage, with potential implications for gene therapy and synthetic design. 2025-07-13T06:46:11Z 2025-07-13T06:46:11Z 2025-04 Thesis https://repository.auw.edu.bd/handle/123456789/534 en application/pdf AUW
spellingShingle Abida, Maisha
Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis
title Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis
title_full Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis
title_fullStr Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis
title_full_unstemmed Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis
title_short Codon Usage Signatures and Codon Pair Usage in Genes Associated with Multiple Sclerosis
title_sort codon usage signatures and codon pair usage in genes associated with multiple sclerosis
url https://repository.auw.edu.bd/handle/123456789/534
work_keys_str_mv AT abidamaisha codonusagesignaturesandcodonpairusageingenesassociatedwithmultiplesclerosis