Performance Estimation of Pattern Matching Algorithms Employing DNA Sequence

The Internet is evolving, with huge amounts of data being generated and stored in terabytes and petabytes. These data contain enormous amounts of information if processed accordingly. In today’s era Information is the oil of the 21st century and analytics is the combustion engine. Analytics on data can be stored in the form of strings. Any string generated has a pattern, analyzing these patterns is the most important thing in this era of information. The information that is stored in a genome database that has been collected over the years from all over the world can be brought to some good use if there is proper analysis done on it. This information can be utilized by a thorough analysis on the sequencing of DNA (De-Oxy-Ribonucleic Acids). DNA is a molecule which stores all the information regarding the development, growth and reproduction of all known living organisms. This simple information can be used to our advantage. The work here compares some of the string pattern-based Algorithms for a real-life application of DNA Sequencing, this pattern matching will be done through the genome database. KMP, Naïve search, BM algorithm, Rabin Karp, Z-Algorithm are few algorithms compared. As the conclusion of work, it is being analyzed comparing the searching complexity of these algorithms that, Algorithms have distinct advantages depending upon the use. The choice of the algorithm could depend upon various other factors including length of the string and the pattern, the type of data, and kind of operations that must be performed. By understating the performance requirements, availability of data, the researcher or the user must make smart choices to choose an appropriate algorithm for their pattern matching tasks. The study here also concludes that KMP is better in terms of matching time for a DNA sequencing application.