Visualizing Genetic Patterns: A Comparative Analysis of DNA Sequences Through Image Processing

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Abstract

A comparative analysis of DNA sequence is investigated through Image Processing. The underlying algorithm transforms, in a novel way, genetic data into images. The information is encoded by using the pixel intensities representing the four constituent nucleotide bases viz. A, T, C and G. These sequences are then employed to generate visual representations, facilitating an intuitive understanding of complex genetic information. Our study integrates machine learning techniques to compare and cluster these DNA sequence-based images, offering a powerful tool for classification. By leveraging machine learning algorithms, we enable the automated recognition of genetic similarities/dissimilarities within genomes which, in turn, streamline the time-consuming process of traditional sequence comparison.

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Keywords

DNA sequence, Sequence alignment, Image analysis, Clustering, Machine Learning