Performance Analysis Of Needleman-Wunsch Algorithm (Global) And Smith-Waterman Algorithm (Local) In Reducing Search Space And Time For Dna Sequence Alignment

Abstract

Generally, sequence alignment is the process of comparing two sequences to identify similarities and differences between them, then, a typical approach to solve this problem is to find a good and plausible alignment between the two sequences. The data representation in this work is DNA sequence. This work intends to analyze large sequences as well as reducing the search space and time complexity without compromising the accuracy and efficiency. This is by evaluating the performance of Needleman-Wunsch algorithm (global) and Smith-Waterman algorithm (local) based on the Dynamic Programming algorithm. Dynamic Programming algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques, which is NP-hard to optimize. Implementation of parallel technique using OpenMP on Needleman-Wunsch algorithm and Smith-Waterman algorithm to identify the strengths and weaknesses for both algorithms. By using C Programming, Needle and Smith programs are developed based on the algorithms (respectively). The analysis concluded that the scoring and traceback techniques used in Needle and Smith are able to align an optimal alignment and improved the performance in searching similarity as well as reduced gaps and mismatch. OpenMP directives able to parallelize the codes and execute it faster, with four cores it can get an execution time of around 60% reduced.