Implementation and Efficiency Analysis of Hamming Distance Algorithm and k-Mer Method for Identifying Sequence Variation in the cox1 Gene of Schistosoma mansoni

Sequence variation analysis is a fundamental task in bioinformatics that demands efficient algorithms. This study implements and analyzes the efficiency of the Hamming Distance algorithm and k-Mer method for identifying variation in the cytochrome c oxidase subunit 1 (cox1) gene sequences of Schistosoma mansoni. Twenty cox1 sequences were obtained from NCBI GenBank and normalized to 1024 bp through trimming. Hamming Distance was used to measure the number of differing base positions between sequences, while k-Mer (k=4 and k=6) was used to extract substring frequency profiles as representations of sequence composition. Efficiency testing measured execution time across various sequence lengths (100–10,000 bp). Results showed Hamming distances ranging from 1 to 16 with an average of 8.626 ± 3.513, reflecting an average similarity of 99.16%. Linear regression analysis produced the equation y = 0.00004x + 0.0000 with R² = 1.0, empirically proving the O(n) complexity of Hamming Distance. The total pipeline for 20 sequences completed in 16.43 ms. This study provides a reproducible implementation and an algorithmic efficiency baseline for DNA sequence variation analysis.