Proteinortho: Detection of (Co-)orthologs in large-scale analysis
By Marcus Lechner, Sven Findeiss, Lydia Steiner, Manja Marz, Peter F. Stadler, Sonja J. Prohaska. BMC Bioinformatics, 2011
1,086 citations recorded by the source corpus.
BackgroundOrthology 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.ResultsThe 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.ConclusionsProteinortho 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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/17a2c1b4016ac226fa6e74d93247c2b67dec05de
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In Molecular Biology
Citations in the corpus
- Genome evolution across 1,011 Saccharomyces cerevisiae isolates (2018, positive): “The orthology identification was carried out using Proteinortho (v.5.15) 68 and synteny information was considered (the PoFF feature of Proteinortho).”
- OrthoVenn2: a web server for whole-genome comparison and annotation of orthologous clusters across multiple species (2019, methodology): “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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. ic.oup.com /nar/advance-”