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Proteinortho: Detection of (Co-)orthologs in large-scale analysis
BACKGROUND: Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114741/ https://www.ncbi.nlm.nih.gov/pubmed/21526987 http://dx.doi.org/10.1186/1471-2105-12-124 |
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author | Lechner, Marcus Findeiß, Sven Steiner, Lydia Marz, Manja Stadler, Peter F Prohaska, Sonja J |
author_facet | Lechner, Marcus Findeiß, Sven Steiner, Lydia Marz, Manja Stadler, Peter F Prohaska, Sonja J |
author_sort | Lechner, Marcus |
collection | PubMed |
description | BACKGROUND: Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. RESULTS: The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. CONCLUSIONS: Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware. |
format | Online Article Text |
id | pubmed-3114741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31147412011-06-15 Proteinortho: Detection of (Co-)orthologs in large-scale analysis Lechner, Marcus Findeiß, Sven Steiner, Lydia Marz, Manja Stadler, Peter F Prohaska, Sonja J BMC Bioinformatics Methodology Article BACKGROUND: Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. RESULTS: The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. CONCLUSIONS: Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware. BioMed Central 2011-04-28 /pmc/articles/PMC3114741/ /pubmed/21526987 http://dx.doi.org/10.1186/1471-2105-12-124 Text en Copyright ©2011 Lechner et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Lechner, Marcus Findeiß, Sven Steiner, Lydia Marz, Manja Stadler, Peter F Prohaska, Sonja J Proteinortho: Detection of (Co-)orthologs in large-scale analysis |
title | Proteinortho: Detection of (Co-)orthologs in large-scale analysis |
title_full | Proteinortho: Detection of (Co-)orthologs in large-scale analysis |
title_fullStr | Proteinortho: Detection of (Co-)orthologs in large-scale analysis |
title_full_unstemmed | Proteinortho: Detection of (Co-)orthologs in large-scale analysis |
title_short | Proteinortho: Detection of (Co-)orthologs in large-scale analysis |
title_sort | proteinortho: detection of (co-)orthologs in large-scale analysis |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114741/ https://www.ncbi.nlm.nih.gov/pubmed/21526987 http://dx.doi.org/10.1186/1471-2105-12-124 |
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