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Compressive genomics for protein databases
Motivation: The exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast; we can exploit this fact to greatly accelerate homology search. Acc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851851/ https://www.ncbi.nlm.nih.gov/pubmed/23812995 http://dx.doi.org/10.1093/bioinformatics/btt214 |
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author | Daniels, Noah M. Gallant, Andrew Peng, Jian Cowen, Lenore J. Baym, Michael Berger, Bonnie |
author_facet | Daniels, Noah M. Gallant, Andrew Peng, Jian Cowen, Lenore J. Baym, Michael Berger, Bonnie |
author_sort | Daniels, Noah M. |
collection | PubMed |
description | Motivation: The exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast; we can exploit this fact to greatly accelerate homology search. Acceleration of programs in the popular PSI/DELTA-BLAST family of tools will not only speed-up homology search directly but also the huge collection of other current programs that primarily interact with large protein databases via precisely these tools. Results: We introduce a suite of homology search tools, powered by compressively accelerated protein BLAST (CaBLASTP), which are significantly faster than and comparably accurate with all known state-of-the-art tools, including HHblits, DELTA-BLAST and PSI-BLAST. Further, our tools are implemented in a manner that allows direct substitution into existing analysis pipelines. The key idea is that we introduce a local similarity-based compression scheme that allows us to operate directly on the compressed data. Importantly, CaBLASTP’s runtime scales almost linearly in the amount of unique data, as opposed to current BLASTP variants, which scale linearly in the size of the full protein database being searched. Our compressive algorithms will speed-up many tasks, such as protein structure prediction and orthology mapping, which rely heavily on homology search. Availability: CaBLASTP is available under the GNU Public License at http://cablastp.csail.mit.edu/ Contact: bab@mit.edu |
format | Online Article Text |
id | pubmed-3851851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-38518512013-12-05 Compressive genomics for protein databases Daniels, Noah M. Gallant, Andrew Peng, Jian Cowen, Lenore J. Baym, Michael Berger, Bonnie Bioinformatics Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany Motivation: The exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast; we can exploit this fact to greatly accelerate homology search. Acceleration of programs in the popular PSI/DELTA-BLAST family of tools will not only speed-up homology search directly but also the huge collection of other current programs that primarily interact with large protein databases via precisely these tools. Results: We introduce a suite of homology search tools, powered by compressively accelerated protein BLAST (CaBLASTP), which are significantly faster than and comparably accurate with all known state-of-the-art tools, including HHblits, DELTA-BLAST and PSI-BLAST. Further, our tools are implemented in a manner that allows direct substitution into existing analysis pipelines. The key idea is that we introduce a local similarity-based compression scheme that allows us to operate directly on the compressed data. Importantly, CaBLASTP’s runtime scales almost linearly in the amount of unique data, as opposed to current BLASTP variants, which scale linearly in the size of the full protein database being searched. Our compressive algorithms will speed-up many tasks, such as protein structure prediction and orthology mapping, which rely heavily on homology search. Availability: CaBLASTP is available under the GNU Public License at http://cablastp.csail.mit.edu/ Contact: bab@mit.edu Oxford University Press 2013-07-01 2013-06-19 /pmc/articles/PMC3851851/ /pubmed/23812995 http://dx.doi.org/10.1093/bioinformatics/btt214 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany Daniels, Noah M. Gallant, Andrew Peng, Jian Cowen, Lenore J. Baym, Michael Berger, Bonnie Compressive genomics for protein databases |
title | Compressive genomics for protein databases |
title_full | Compressive genomics for protein databases |
title_fullStr | Compressive genomics for protein databases |
title_full_unstemmed | Compressive genomics for protein databases |
title_short | Compressive genomics for protein databases |
title_sort | compressive genomics for protein databases |
topic | Ismb/Eccb 2013 Proceedings Papers Committee July 21 to July 23, 2013, Berlin, Germany |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851851/ https://www.ncbi.nlm.nih.gov/pubmed/23812995 http://dx.doi.org/10.1093/bioinformatics/btt214 |
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