Comparative Analysis of Pattern Matching Algorithms Using DNA Sequences

DNA sequences are the most crucial in a cell as they code for the generation of proteins that affect the metabolism of an organism. Some viral pathogens can affect the host cells by integrating their DNA into the host cell’s DNA. After integration, the replication of the host cell occurs in favor of the virus. During integration, DNA sequences that encode viral proteins are integrated with the DNA of host cells. Finding the position of viral DNA becomes highly tedious as DNA sequences are extremely long. This job can be made easier with the use of pattern-matching algorithms. In this paper, a comparative analysis of different pattern matching algorithms has been presented. The algorithms taken up are Brute-Force, Knuth Morris Pratt, Rabin Karp, Boyer Moore, Shift-Or, and Z-Algorithm. DNA sequences have been used as a medium to compare these algorithms. The algorithms have been analyzed using different parameters such as run time complexity, pre-processing time complexity, and search time complexity. The best algorithm can be used to detect diseases caused by DNA integrating pathogens at early stages to reduce complications at later times.