Efficient GPU-based algorithm for aligning huge sequence database

Sequence alignment has been widely utilized in biological computing science. To obtain the optimal alignment results many algorithms adopts dynamic programming method to achieve this goal. Smith-Waterman algorithm is the famous in the sequence alignment approach. However, such dynamic programming algorithms are computation-consuming. It is impossible to use these algorithms to compare query sequence with a sequence database such as GenBank and PDB. Recently, GPU computing has been applied in many sequence alignment algorithms to enhance the performance. In this paper, we proposed a GPU-based Smith-Waterman algorithm by combining the CPU and GPU computing capabilities to accelerate alignments on a sequence database. In the proposed algorithm, a filtration mechanism using frequency distance is used to decrease the number of compared sequences. We implemented the Smith-Waterman alignments by CUDA on the NVIDIA Tesla C2050. The experimental results show that the highest speedup ratio is about 80 to 90 times over CPU-based Smith-Waterman algorithm.