Sequence Entropy And Markov Modelling Of Mutated CFTR Genes: Insights From Multiple Sequence Alignment
Keywords:
Cystic Fibrosis, CFTR Gene, Gene Mutation, Multiple Sequence Alignment, Shannon Entropy, Markov Chain Model, Genotype-Phenotype CorrelationAbstract
Mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene are central to the development of cystic fibrosis, a severe genetic disorder affecting epithelial function. This study analyses 35 mutated CFTR gene sequences to explore underlying sequence variation and structural patterns. Multiple sequence alignment was performed to organize the sequences, followed by Shannon entropy analysis to identify regions of high variability and conservation. Hierarchical clustering provided insights into relationships among the mutated sequences, while sequence logo plots visually highlighted nucleotide distribution at each alignment position. To model the sequence behaviour statistically, a first-order Markov chain was constructed, capturing transition probabilities between nucleotides across the aligned sequences. Together, these methods offer a comprehensive view of the mutational landscape within the CFTR gene. The findings enhance our understanding of sequence-level mutation dynamics and provide a foundation for further computational modelling and genotype-phenotype correlation studies in cystic fibrosis research.
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