Codon usage signatures and codon pair usage in genes implicated in Stroke

Ischaemic strokes account for the majority of strokes, which are a leading cause of death and permanent disability globally. In order to identify molecular patterns connected to disease-specific gene expression, this study investigates codon use bias in stroke-associated genes. With the aid of st...

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Autore principale: Abonty, Nafija Anjum
Natura: Tesi
Lingua:inglese
Pubblicazione: AUW 2025
Accesso online:https://repository.auw.edu.bd/handle/123456789/535
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author Abonty, Nafija Anjum
author_facet Abonty, Nafija Anjum
author_sort Abonty, Nafija Anjum
collection institutional Repository
description Ischaemic strokes account for the majority of strokes, which are a leading cause of death and permanent disability globally. In order to identify molecular patterns connected to disease-specific gene expression, this study investigates codon use bias in stroke-associated genes. With the aid of statistical testing and machine learning models, we examined nucleotide composition, codon usage indices, and Relative Synonymous Codon Usage (RSCU) using genomic data from BrainBase and HRT Atlas.The findings showed a strong bias in favour of GC-rich codons, especially at the third codon position, indicating adaptive selection for improved translational efficiency and mRNA stability under stress. Using only codon characteristics, machine learning classifiers—Random Forest in particular—were able to differentiate between genes linked to stroke and those involved in housekeeping. These results point to codon use bias as a possible molecular indicator of stroke, providing encouraging paths for the identification of biomarkers and better genomic categorisation.
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spelling 123456789-5352026-02-18T06:09:51Z Codon usage signatures and codon pair usage in genes implicated in Stroke Abonty, Nafija Anjum Ischaemic strokes account for the majority of strokes, which are a leading cause of death and permanent disability globally. In order to identify molecular patterns connected to disease-specific gene expression, this study investigates codon use bias in stroke-associated genes. With the aid of statistical testing and machine learning models, we examined nucleotide composition, codon usage indices, and Relative Synonymous Codon Usage (RSCU) using genomic data from BrainBase and HRT Atlas.The findings showed a strong bias in favour of GC-rich codons, especially at the third codon position, indicating adaptive selection for improved translational efficiency and mRNA stability under stress. Using only codon characteristics, machine learning classifiers—Random Forest in particular—were able to differentiate between genes linked to stroke and those involved in housekeeping. These results point to codon use bias as a possible molecular indicator of stroke, providing encouraging paths for the identification of biomarkers and better genomic categorisation. 2025-07-13T06:48:28Z 2025-07-13T06:48:28Z 2025-04 Thesis https://repository.auw.edu.bd/handle/123456789/535 en application/pdf AUW
spellingShingle Abonty, Nafija Anjum
Codon usage signatures and codon pair usage in genes implicated in Stroke
title Codon usage signatures and codon pair usage in genes implicated in Stroke
title_full Codon usage signatures and codon pair usage in genes implicated in Stroke
title_fullStr Codon usage signatures and codon pair usage in genes implicated in Stroke
title_full_unstemmed Codon usage signatures and codon pair usage in genes implicated in Stroke
title_short Codon usage signatures and codon pair usage in genes implicated in Stroke
title_sort codon usage signatures and codon pair usage in genes implicated in stroke
url https://repository.auw.edu.bd/handle/123456789/535
work_keys_str_mv AT abontynafijaanjum codonusagesignaturesandcodonpairusageingenesimplicatedinstroke