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Accelerating the Smith-Waterman algorithm with interpair pruning and band optimization for the all-pairs comparison of base sequences.

Okada D, Ino F, Hagihara K - BMC Bioinformatics (2015)

Bottom Line: Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed.This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound.However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, 565-0871, Japan.

ABSTRACT

Background: The Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm (by realizing interpair block pruning and band optimization) compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).

Results: We present an interpair optimization method for the Smith-Waterman algorithm with the aim of accelerating the all-pairs comparison of base sequences. Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed. This lower bound is further used for band optimization. We integrated our interpair optimization method into SW#, a previous GPU-based implementation that employs variants of a banded Smith-Waterman algorithm and a banded Myers-Miller algorithm. Evaluation using the six genomes of Bacillus anthracis shows that our method pruned 88% of the matrix cells on a single GPU and 73% of the matrix cells on two GPUs. For the genomes of the human chromosome 21, the alignment performance reached 202 giga-cell updates per second (GCUPS) on two Tesla K40 GPUs.

Conclusions: Efficient interpair pruning and band optimization makes it possible to complete the all-pairs comparisons of the sequences of the same species 1.2 times faster than the intrapair pruning method. This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound. However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla. Consequently, our method is useful in accelerating the applications that require optimal local alignments scores for the same species. The source code is available for download from http://www-hagi.ist.osaka-u.ac.jp/research/code/.

No MeSH data available.


Breakdown analysis. Breakdown of execution time for genomes of (a) Bacillus anthracis, (b) the human chromosome 21 and (c) the human/gorilla/chimpanzee chromosome 19. The original version of phase 1 corresponds to the score-only Smith-Waterman algorithm with intrapair pruning. Phases 2 and 3 correspond to a banded (score-only) Smith-Waterman algorithm with block pruning and a banded Myers-Miller algorithm, respectively
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Fig12: Breakdown analysis. Breakdown of execution time for genomes of (a) Bacillus anthracis, (b) the human chromosome 21 and (c) the human/gorilla/chimpanzee chromosome 19. The original version of phase 1 corresponds to the score-only Smith-Waterman algorithm with intrapair pruning. Phases 2 and 3 correspond to a banded (score-only) Smith-Waterman algorithm with block pruning and a banded Myers-Miller algorithm, respectively

Mentions: Finally, we evaluated our interpair optimization method in terms of the applicability. To do this, we compared our method with banded alignment algorithms employed in the second and third phases of SW#. Figure 12 shows the breakdown of execution time for experimental datasets. Notice that the three phases usually examine different lengths of subsequences, because the first and second phases find the ending and starting alignment positions, respectively; the subsequences to be examined become shorter as the alignment phase proceeds.Fig. 12


Accelerating the Smith-Waterman algorithm with interpair pruning and band optimization for the all-pairs comparison of base sequences.

Okada D, Ino F, Hagihara K - BMC Bioinformatics (2015)

Breakdown analysis. Breakdown of execution time for genomes of (a) Bacillus anthracis, (b) the human chromosome 21 and (c) the human/gorilla/chimpanzee chromosome 19. The original version of phase 1 corresponds to the score-only Smith-Waterman algorithm with intrapair pruning. Phases 2 and 3 correspond to a banded (score-only) Smith-Waterman algorithm with block pruning and a banded Myers-Miller algorithm, respectively
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4595212&req=5

Fig12: Breakdown analysis. Breakdown of execution time for genomes of (a) Bacillus anthracis, (b) the human chromosome 21 and (c) the human/gorilla/chimpanzee chromosome 19. The original version of phase 1 corresponds to the score-only Smith-Waterman algorithm with intrapair pruning. Phases 2 and 3 correspond to a banded (score-only) Smith-Waterman algorithm with block pruning and a banded Myers-Miller algorithm, respectively
Mentions: Finally, we evaluated our interpair optimization method in terms of the applicability. To do this, we compared our method with banded alignment algorithms employed in the second and third phases of SW#. Figure 12 shows the breakdown of execution time for experimental datasets. Notice that the three phases usually examine different lengths of subsequences, because the first and second phases find the ending and starting alignment positions, respectively; the subsequences to be examined become shorter as the alignment phase proceeds.Fig. 12

Bottom Line: Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed.This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound.However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, 565-0871, Japan.

ABSTRACT

Background: The Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm (by realizing interpair block pruning and band optimization) compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).

Results: We present an interpair optimization method for the Smith-Waterman algorithm with the aim of accelerating the all-pairs comparison of base sequences. Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed. This lower bound is further used for band optimization. We integrated our interpair optimization method into SW#, a previous GPU-based implementation that employs variants of a banded Smith-Waterman algorithm and a banded Myers-Miller algorithm. Evaluation using the six genomes of Bacillus anthracis shows that our method pruned 88% of the matrix cells on a single GPU and 73% of the matrix cells on two GPUs. For the genomes of the human chromosome 21, the alignment performance reached 202 giga-cell updates per second (GCUPS) on two Tesla K40 GPUs.

Conclusions: Efficient interpair pruning and band optimization makes it possible to complete the all-pairs comparisons of the sequences of the same species 1.2 times faster than the intrapair pruning method. This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound. However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla. Consequently, our method is useful in accelerating the applications that require optimal local alignments scores for the same species. The source code is available for download from http://www-hagi.ist.osaka-u.ac.jp/research/code/.

No MeSH data available.