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Natural Selection on Functional Modules, a Genome-Wide Analysis

Overview of attention for article published in PLoS Computational Biology, March 2011
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Title
Natural Selection on Functional Modules, a Genome-Wide Analysis
Published in
PLoS Computational Biology, March 2011
DOI 10.1371/journal.pcbi.1001093
Pubmed ID
Authors

François Serra, Leonardo Arbiza, Joaquín Dopazo, Hernán Dopazo

Abstract

Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.

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Geographical breakdown

Country Count As %
United States 4 4%
Spain 3 3%
Brazil 2 2%
Argentina 2 2%
France 1 1%
Germany 1 1%
Portugal 1 1%
United Kingdom 1 1%
Russia 1 1%
Other 0 0%
Unknown 73 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 39%
Researcher 21 24%
Professor > Associate Professor 8 9%
Professor 7 8%
Student > Master 5 6%
Other 8 9%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 66%
Biochemistry, Genetics and Molecular Biology 13 15%
Engineering 3 3%
Medicine and Dentistry 2 2%
Immunology and Microbiology 2 2%
Other 3 3%
Unknown 7 8%