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Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space
When computing alignments of DNA sequences to a large genome, a key element in achieving high processing throughput is to prioritize locations in the genome where high-scoring mappings might be expected. We formulated this task as a series of list-processing operations that can be efficiently perfor...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358639/ https://www.ncbi.nlm.nih.gov/pubmed/25780763 http://dx.doi.org/10.7717/peerj.808 |
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author | Wilton, Richard Budavari, Tamas Langmead, Ben Wheelan, Sarah J. Salzberg, Steven L. Szalay, Alexander S. |
author_facet | Wilton, Richard Budavari, Tamas Langmead, Ben Wheelan, Sarah J. Salzberg, Steven L. Szalay, Alexander S. |
author_sort | Wilton, Richard |
collection | PubMed |
description | When computing alignments of DNA sequences to a large genome, a key element in achieving high processing throughput is to prioritize locations in the genome where high-scoring mappings might be expected. We formulated this task as a series of list-processing operations that can be efficiently performed on graphics processing unit (GPU) hardware.We followed this approach in implementing a read aligner called Arioc that uses GPU-based parallel sort and reduction techniques to identify high-priority locations where potential alignments may be found. We then carried out a read-by-read comparison of Arioc’s reported alignments with the alignments found by several leading read aligners. With simulated reads, Arioc has comparable or better accuracy than the other read aligners we tested. With human sequencing reads, Arioc demonstrates significantly greater throughput than the other aligners we evaluated across a wide range of sensitivity settings. The Arioc software is available at https://github.com/RWilton/Arioc. It is released under a BSD open-source license. |
format | Online Article Text |
id | pubmed-4358639 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43586392015-03-16 Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space Wilton, Richard Budavari, Tamas Langmead, Ben Wheelan, Sarah J. Salzberg, Steven L. Szalay, Alexander S. PeerJ Bioinformatics When computing alignments of DNA sequences to a large genome, a key element in achieving high processing throughput is to prioritize locations in the genome where high-scoring mappings might be expected. We formulated this task as a series of list-processing operations that can be efficiently performed on graphics processing unit (GPU) hardware.We followed this approach in implementing a read aligner called Arioc that uses GPU-based parallel sort and reduction techniques to identify high-priority locations where potential alignments may be found. We then carried out a read-by-read comparison of Arioc’s reported alignments with the alignments found by several leading read aligners. With simulated reads, Arioc has comparable or better accuracy than the other read aligners we tested. With human sequencing reads, Arioc demonstrates significantly greater throughput than the other aligners we evaluated across a wide range of sensitivity settings. The Arioc software is available at https://github.com/RWilton/Arioc. It is released under a BSD open-source license. PeerJ Inc. 2015-03-03 /pmc/articles/PMC4358639/ /pubmed/25780763 http://dx.doi.org/10.7717/peerj.808 Text en © 2015 Wilton et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Wilton, Richard Budavari, Tamas Langmead, Ben Wheelan, Sarah J. Salzberg, Steven L. Szalay, Alexander S. Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space |
title | Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space |
title_full | Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space |
title_fullStr | Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space |
title_full_unstemmed | Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space |
title_short | Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space |
title_sort | arioc: high-throughput read alignment with gpu-accelerated exploration of the seed-and-extend search space |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358639/ https://www.ncbi.nlm.nih.gov/pubmed/25780763 http://dx.doi.org/10.7717/peerj.808 |
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