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Frequency of intron loss correlates with processed pseudogene abundance: a novel strategy to test the reverse transcriptase model of intron loss

Overview of attention for article published in BMC Biology, March 2013
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Title
Frequency of intron loss correlates with processed pseudogene abundance: a novel strategy to test the reverse transcriptase model of intron loss
Published in
BMC Biology, March 2013
DOI 10.1186/1741-7007-11-23
Pubmed ID
Authors

Tao Zhu, Deng-Ke Niu

Abstract

Although intron loss in evolution has been described, the mechanism involved is still unclear. Three models have been proposed, the reverse transcriptase (RT) model, genomic deletion model and double-strand-break repair model. The RT model, also termed mRNA-mediated intron loss, suggests that cDNA molecules reverse transcribed from spliced mRNA recombine with genomic DNA causing intron loss. Many studies have attempted to test this model based on its predictions, such as simultaneous loss of adjacent introns, 3'-side bias of intron loss, and germline expression of intron-lost genes. Evidence either supporting or opposing the model has been reported. The mechanism of intron loss proposed in the RT model shares the process of reverse transcription with the formation of processed pseudogenes. If the RT model is correct, genes that have produced more processed pseudogenes are more likely to undergo intron loss.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Netherlands 1 3%
Germany 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 22%
Researcher 6 17%
Professor 5 14%
Student > Bachelor 4 11%
Student > Master 3 8%
Other 7 19%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 61%
Biochemistry, Genetics and Molecular Biology 7 19%
Nursing and Health Professions 1 3%
Computer Science 1 3%
Energy 1 3%
Other 0 0%
Unknown 4 11%