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A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs.

Jacek Blazewicz¹, Wojciech Frohmberg, Michal Kierzynka

¹Poznań University of Technology, Poznań, Poland.

BMC Bioinformatics

|May 24, 2011

Summary

This summary is machine-generated.

This study introduces a GPU-accelerated algorithm for pairwise sequence alignment, enabling the computation of full alignments, not just scores. This efficient solution addresses the growing challenge of large biological sequence datasets.

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Area of Science:

Bioinformatics
Computational Biology
High-Performance Computing

Background:

Pairwise sequence alignment is crucial in biological research.
Increasing sequence data presents computational challenges for alignment.
Existing GPU solutions often omit alignment construction, focusing only on scores.

Purpose of the Study:

To implement global and semiglobal Needleman-Wunsch and Smith-Waterman algorithms with backtracking on GPUs.
To enable the computation of full pairwise sequence alignments, not just scores.

Main Methods:

Developed GPU-accelerated algorithms for Needleman-Wunsch and Smith-Waterman with backtracking.
Utilized Graphics Processing Unit (GPU) architecture for parallel computation.
Implemented load balancing for multi-GPU support.

Main Results:

Achieved high performance of up to 6.3 GCUPS on a single GPU for affine gap penalties.
Demonstrated significant efficiency compared to CPU and other GPU-based methods.
Showcased excellent scalability with multi-GPU support.

Conclusions:

GPU architecture can effectively support the backtracking procedure for sequence alignment.
The developed algorithm computes pairwise alignments efficiently, opening new possibilities in molecular biology.
Performance scales nearly linearly with the addition of more GPUs.