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CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi-GPUs.

Che-Lun Hung1, Yu-Shiang Lin2, Chun-Yuan Lin3

  • 1Department of Computer Science and Communication Engineering, Providence University, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung City 43301, Taiwan.

Computational Biology and Chemistry
|June 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces CUDA ClustalW v1.0, a GPU-accelerated version of ClustalW for multiple sequence alignment. It significantly speeds up analysis for large biological datasets, achieving over 33x faster execution times.

Keywords:
CUDAClustalWGPUParallel computingProgressive multiple sequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment is crucial for analyzing DNA and protein sequences in biological applications like homology modeling and phylogenetic reconstruction.
  • The computational complexity of multiple sequence alignment (a NP-hard problem) and the increasing volume of data from next-generation sequencing pose significant challenges for traditional methods.
  • Progressive alignment, while effective, becomes time-consuming for large datasets.

Purpose of the Study:

  • To develop a faster multiple sequence alignment tool for large biological datasets.
  • To leverage parallel computing, specifically Graphics Processing Units (GPUs), to overcome the computational bottlenecks of existing methods.
  • To present CUDA ClustalW v1.0 as an efficient alternative to traditional ClustalW for large-scale sequence analysis.

Main Methods:

  • Implementation of a GPU-accelerated version of ClustalW v2.0.11, named CUDA ClustalW v1.0.
  • Utilizing parallel computing architecture (GPU) to perform iterative pairwise alignments.
  • Comparative performance analysis between CUDA ClustalW v1.0 and the original ClustalW v2.0.11.

Main Results:

  • CUDA ClustalW v1.0 demonstrates substantial performance improvements.
  • Achieved over 33x speedup in overall execution time compared to ClustalW v2.0.11.
  • The GPU-based approach effectively handles large-scale multiple sequence alignment tasks.

Conclusions:

  • CUDA ClustalW v1.0 offers a significant acceleration for multiple sequence alignment.
  • The GPU implementation is a viable solution for analyzing the growing volume of biological sequence data.
  • This advancement facilitates more efficient homology modeling, phylogenetic reconstruction, and other critical bioinformatics analyses.