Publication

Abstract : In the world of expanding constellation of biological species for various technological and biological reasons, it requires high efficiency and memory to record the genetic database of all the species so far found. If a species is found and it is established that it was not classified before, it is essential for the researchers to find out the family of species where it had been evolved from. Thus researchers use various bioinformatics algorithms which deal with varying operations on biological data such as DNA analysis, Protein analysis, Genome information retrieval and Nucleotide applications. It is mandatory that the process of working on a DNA of a new species should be accurate, fast and energy efficient. It should ensure the correctness of the solution. The concept of classifying the biological information follows the biological evolution procedure. The evolution of bioinformatics algorithms started from Needleman-Wunsch algorithm in 1970 and still it is continuing towards better configuration and performance. For an algorithm to work efficiently, a hardware implementation should be mapped into a platform which appreciates its inherent complexity and utilizes it completely. Biological algorithms have been often encountered with processes which require substantial parallel architecture. The best candidate would be Field Programmable Gate Arrays (FPGA) for the process of solving the biological mysteries satisfying the necessary requirements. In this paper, prominent algorithms for bioinformatics applications are studied and their FPGA implementation challenges and opportunities are explored.