<![CDATA[Advances in computational technology and bioinformatics have enabled efficient and accurate DNA (Deoxyribo Nucleic Acid) data analysis. This study explores computational algorithm utilization for simple DNA data analysis in bioinformatics. We implemented basic algorithms (sequence sequence, pattern matching, clustering) using Python and Biopython library to analyze 500 DNA sequence samples from various model organisms. Results show computational algorithms accelerate analysis by 70% compared to manual methods, achieving 95% accuracy in identifying sequence patterns and structural similarities. Performance analysis reveals dynamic programming-based sequence sequence has O(mn) time complexity, while hierarchical clustering requires O(n²) computational time. This study highlights optimization needs for large-scale Dataset s and parameter adjustments for specific cases. Computational algorithms prove effective in supporting simple DNA data analysis, paving the way for developing complex bioinformatics tools.]]>
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