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Resolving the Ortholog Conjecture: Orthologs Tend to Be Weakly, but Significantly, More Similar in Function than Paralogs

Overview of attention for article published in PLoS Computational Biology, May 2012
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Title
Resolving the Ortholog Conjecture: Orthologs Tend to Be Weakly, but Significantly, More Similar in Function than Paralogs
Published in
PLoS Computational Biology, May 2012
DOI 10.1371/journal.pcbi.1002514
Pubmed ID
Authors

Adrian M. Altenhoff, Romain A. Studer, Marc Robinson-Rechavi, Christophe Dessimoz

Abstract

The function of most proteins is not determined experimentally, but is extrapolated from homologs. According to the "ortholog conjecture", or standard model of phylogenomics, protein function changes rapidly after duplication, leading to paralogs with different functions, while orthologs retain the ancestral function. We report here that a comparison of experimentally supported functional annotations among homologs from 13 genomes mostly supports this model. We show that to analyze GO annotation effectively, several confounding factors need to be controlled: authorship bias, variation of GO term frequency among species, variation of background similarity among species pairs, and propagated annotation bias. After controlling for these biases, we observe that orthologs have generally more similar functional annotations than paralogs. This is especially strong for sub-cellular localization. We observe only a weak decrease in functional similarity with increasing sequence divergence. These findings hold over a large diversity of species; notably orthologs from model organisms such as E. coli, yeast or mouse have conserved function with human proteins.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 14 4%
Germany 4 1%
Brazil 4 1%
Sweden 4 1%
Netherlands 2 <1%
Poland 2 <1%
Switzerland 2 <1%
United Kingdom 2 <1%
Canada 2 <1%
Other 5 2%
Unknown 282 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 95 29%
Researcher 57 18%
Student > Bachelor 39 12%
Student > Master 38 12%
Professor 23 7%
Other 39 12%
Unknown 32 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 169 52%
Biochemistry, Genetics and Molecular Biology 76 24%
Computer Science 20 6%
Chemistry 5 2%
Medicine and Dentistry 5 2%
Other 13 4%
Unknown 35 11%