Over the recent decades, bioinformatics has acquired a major concern due to the rapid growth in biological data that includes protein structures and genome sequences. Many considerable efforts have been conducted by computer scientists, mathematicians and biologists to coup with complex biological problems such as sequence alignment problem, using several techniques to formulate/model the targeted biological problems as computational problems and design algorithms to solve them in an accurate and efficient manner. Needleman-Wunsch algorithm as well as other alignment algorithms have been the subject of many studies to improve their performance due to their importance and the large scale of the data they have to handle (e.g., aligning strings of hundreds of thousands of characters). Approaches included a mixture of different parallel implementations using specialized hardware such as Graphical Processing Units (GPUs) and a vectorized approach of reading and processing the input data. In this work, a parallel implementation of NW algorithm is presented using GPU due to its efficiency and high speed, to solve the slowness problem associated with this algorithm when processing large data sets, as well as to enhance the performance of the algorithm especially when processing vectors of adjacent cells parallel to the matrix miner diagonal. The experiments show that the proposed implementation improves the performance of the algorithm by 99%.
['Maged Fakirah', 'Mohammed A. Shehab', 'Yaser Jararweh', 'Mahmoud Al-Ayyoub']
Accelerating Needleman-Wunsch global alignment algorithm with GPUs