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Questioning the Ubiquity of Neofunctionalization

Overview of attention for article published in PLoS Computational Biology, January 2009
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81 Mendeley
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2 Connotea
Title
Questioning the Ubiquity of Neofunctionalization
Published in
PLoS Computational Biology, January 2009
DOI 10.1371/journal.pcbi.1000252
Pubmed ID
Authors

Todd A. Gibson, Debra S. Goldberg

Abstract

Gene duplication provides much of the raw material from which functional diversity evolves. Two evolutionary mechanisms have been proposed that generate functional diversity: neofunctionalization, the de novo acquisition of function by one duplicate, and subfunctionalization, the partitioning of ancestral functions between gene duplicates. With protein interactions as a surrogate for protein functions, evidence of prodigious neofunctionalization and subfunctionalization has been identified in analyses of empirical protein interactions and evolutionary models of protein interactions. However, we have identified three phenomena that have contributed to neofunctionalization being erroneously identified as a significant factor in protein interaction network evolution. First, self-interacting proteins are underreported in interaction data due to biological artifacts and design limitations in the two most common high-throughput protein interaction assays. Second, evolutionary inferences have been drawn from paralog analysis without consideration for concurrent and subsequent duplication events. Third, the theoretical model of prodigious neofunctionalization is unable to reproduce empirical network clustering and relies on untenable parameter requirements. In light of these findings, we believe that protein interaction evolution is more persuasively characterized by subfunctionalization and self-interactions.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 3 4%
Australia 1 1%
Canada 1 1%
Denmark 1 1%
Sweden 1 1%
Korea, Republic of 1 1%
Estonia 1 1%
Spain 1 1%
Other 1 1%
Unknown 66 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 35%
Student > Ph. D. Student 24 30%
Professor > Associate Professor 7 9%
Student > Master 7 9%
Professor 4 5%
Other 8 10%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 68%
Biochemistry, Genetics and Molecular Biology 9 11%
Computer Science 7 9%
Physics and Astronomy 2 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 3 4%
Unknown 4 5%