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Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases

Obtaining unique oligos from an EST database is a problem of great importance in bioinformatics, particularly in the discovery of new genes and the mapping of the human genome. Many algorithms have been developed to find unique oligos, many of which are much less time consuming than the traditional...

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Autores principales: Mata-Montero, Manrique, Shalaby, Nabil, Sheppard, Bradley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3638690/
https://www.ncbi.nlm.nih.gov/pubmed/23653640
http://dx.doi.org/10.1155/2013/793130
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author Mata-Montero, Manrique
Shalaby, Nabil
Sheppard, Bradley
author_facet Mata-Montero, Manrique
Shalaby, Nabil
Sheppard, Bradley
author_sort Mata-Montero, Manrique
collection PubMed
description Obtaining unique oligos from an EST database is a problem of great importance in bioinformatics, particularly in the discovery of new genes and the mapping of the human genome. Many algorithms have been developed to find unique oligos, many of which are much less time consuming than the traditional brute force approach. An algorithm was presented by Zheng et al. (2004) which finds the solution of the unique oligos search problem efficiently. We implement this algorithm as well as several new algorithms based on some theorems included in this paper. We demonstrate how, with these new algorithms, we can obtain unique oligos much faster than with previous ones. We parallelize these new algorithms to further improve the time of finding unique oligos. All algorithms are run on ESTs obtained from a Barley EST database.
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spelling pubmed-36386902013-05-07 Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases Mata-Montero, Manrique Shalaby, Nabil Sheppard, Bradley Adv Bioinformatics Research Article Obtaining unique oligos from an EST database is a problem of great importance in bioinformatics, particularly in the discovery of new genes and the mapping of the human genome. Many algorithms have been developed to find unique oligos, many of which are much less time consuming than the traditional brute force approach. An algorithm was presented by Zheng et al. (2004) which finds the solution of the unique oligos search problem efficiently. We implement this algorithm as well as several new algorithms based on some theorems included in this paper. We demonstrate how, with these new algorithms, we can obtain unique oligos much faster than with previous ones. We parallelize these new algorithms to further improve the time of finding unique oligos. All algorithms are run on ESTs obtained from a Barley EST database. Hindawi Publishing Corporation 2013 2013-04-08 /pmc/articles/PMC3638690/ /pubmed/23653640 http://dx.doi.org/10.1155/2013/793130 Text en Copyright © 2013 Manrique Mata-Montero et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mata-Montero, Manrique
Shalaby, Nabil
Sheppard, Bradley
Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases
title Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases
title_full Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases
title_fullStr Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases
title_full_unstemmed Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases
title_short Efficient Serial and Parallel Algorithms for Selection of Unique Oligos in EST Databases
title_sort efficient serial and parallel algorithms for selection of unique oligos in est databases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3638690/
https://www.ncbi.nlm.nih.gov/pubmed/23653640
http://dx.doi.org/10.1155/2013/793130
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