CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi-GPUs. (October 2015) - Hung, Che-Lun; Lin, Yu-Shiang; Lin, Chun-Yuan; Chung, Yeh-Ching; Chung, Yi-Fang
Graphical abstract: Highlights: CUDA ClustalW v1.0 achieved more than 33× speedups by comparing to ClustalW v2.0.11. Abstract: For biological applications, sequence alignment is an important strategy to analyze DNA and protein sequences. Multiple sequence alignment is an essential methodology to study biological data, such as homology modeling, phylogenetic reconstruction and etc. However, multiple sequence alignment is a NP-hard problem. In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments. Due to rapid growth of the next generation sequencing technologies, a large number of sequences can be produced in a short period of time. When the problem instance is large, progressive alignment will be time consuming. Parallel computing is a suitable solution for such applications, and GPU is one of the important architectures for contemporary parallel computing researches. Therefore, we proposed a GPU version of ClustalW v2.0.11, called CUDA ClustalW v1.0, in this work. From the experiment results, it can be seen that the CUDA ClustalW v1.0 can achieve more than 33× speedups for overall execution time by comparing to ClustalW v2.0.11.