الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
Biological pairwise sequence alignment could be a means for arranging two biological sequence characters to identify regions of similarity. This operation attracts considerable interest owing to its significant influence on the critical aspects of life. Sequence alignment over large databases cannot yield results within a reasonable time, power, and cost. This necessitates acceleration platforms such as CPUs, GPUs, and FPGAs. A trade-off exists between device utilization area, running speed, power dissipation, cost, development time, and reusability in the selection of an acceleration platform. In general, FPGAs supply additional flexibility, higher performance, higher speed, and lower cost. However, they exhibit the disadvantage of incorporating a programming language that is of a lower level than those of CPUs and GPUs. An optimized software and hardware digital implementation of two widely used DNA sequence alignment algorithms based on (LUT) is illustrated in this study. These algorithms are the best means for identifying similar regions between sequences. The proposed implementation relies on the complete parallelization of these foundational algorithms under certain limitations to overcome most of the problems of dynamic programming and hardware implementation. The proposed method takes O(N/4) calculation steps, where N is the length of each sequence with a minimum value of four (i.e., N=4, 8, 12,…). A performance comparison between the state of the art and our proposed algorithm is conducted for software and hardware implementation. Combinational circuits are used for FPGA-based hardware implementation of DNA sequence alignment algorithms. Performance and device resource usage are evaluated for different hardware designs. A customized convolution neural network model is used to implement global alignment and achieve 98.3% accuracy. Hardware implementation can further obtain more tests and be evaluated for long sequences.