Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.

<h4>Motivation</h4>To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not suf...

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Main Authors: Sven Warris, Feyruz Yalcin, Katherine J L Jackson, Jan Peter Nap
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0122524
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spelling doaj-641d3d92c75843afad38d74a3bbfea3b2021-03-04T08:23:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01104e012252410.1371/journal.pone.0122524Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.Sven WarrisFeyruz YalcinKatherine J L JacksonJan Peter Nap<h4>Motivation</h4>To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.<h4>Results</h4>With the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation.https://doi.org/10.1371/journal.pone.0122524
collection DOAJ
language English
format Article
sources DOAJ
author Sven Warris
Feyruz Yalcin
Katherine J L Jackson
Jan Peter Nap
spellingShingle Sven Warris
Feyruz Yalcin
Katherine J L Jackson
Jan Peter Nap
Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.
PLoS ONE
author_facet Sven Warris
Feyruz Yalcin
Katherine J L Jackson
Jan Peter Nap
author_sort Sven Warris
title Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.
title_short Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.
title_full Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.
title_fullStr Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.
title_full_unstemmed Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.
title_sort flexible, fast and accurate sequence alignment profiling on gpgpu with paswas.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description <h4>Motivation</h4>To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.<h4>Results</h4>With the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation.
url https://doi.org/10.1371/journal.pone.0122524
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