Evaluation of performance of the same analysis in different programming languages

Abstract

Aim. This study evaluates the performance of the Needleman-Wunsch algorithm across Python, C++, and C#, focusing on execution time and memory usage. The algorithm, based on dynamic programming with a time complexity of O(mn), ensures optimal global sequence alignment, making it essential in bioinformatics. An existing repository was used for implementation, and despite its age, the core logic remains valid for performance comparison. While newer algorithms like Smith-Waterman and BLAST offer different advantages, Needleman-Wunsch is still the gold standard for global alignments due to its simplicity and accuracy. Conclusions. This study highlights the trade-offs between ease of use, speed, and resource consumption. C++ is ideal for performance-critical applications, while Python offers accessibility for rapid development despite higher resource needs.