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Evolutionary Evidence for Alternative Structure in RNA Sequence Co-variation

Overview of attention for article published in PLoS Computational Biology, July 2013
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Title
Evolutionary Evidence for Alternative Structure in RNA Sequence Co-variation
Published in
PLoS Computational Biology, July 2013
DOI 10.1371/journal.pcbi.1003152
Pubmed ID
Authors

Justin Ritz, Joshua S. Martin, Alain Laederach

Abstract

Sequence conservation and co-variation of base pairs are hallmarks of structured RNAs. For certain RNAs (e.g. riboswitches), a single sequence must adopt at least two alternative secondary structures to effectively regulate the message. If alternative secondary structures are important to the function of an RNA, we expect to observe evolutionary co-variation supporting multiple conformations. We set out to characterize the evolutionary co-variation supporting alternative conformations in riboswitches to determine the extent to which alternative secondary structures are conserved. We found strong co-variation support for the terminator, P1, and anti-terminator stems in the purine riboswitch by extending alignments to include terminator sequences. When we performed Boltzmann suboptimal sampling on purine riboswitch sequences with terminators we found that these sequences appear to have evolved to favor specific alternative conformations. We extended our analysis of co-variation to classic alignments of group I/II introns, tRNA, and other classes of riboswitches. In a majority of these RNAs, we found evolutionary evidence for alternative conformations that are compatible with the Boltzmann suboptimal ensemble. Our analyses suggest that alternative conformations are selected for and thus likely play functional roles in even the most structured of RNAs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
France 1 2%
Italy 1 2%
Canada 1 2%
Unknown 60 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 28%
Student > Ph. D. Student 18 28%
Student > Master 6 9%
Professor 4 6%
Student > Doctoral Student 3 5%
Other 8 13%
Unknown 7 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 38%
Agricultural and Biological Sciences 19 30%
Computer Science 5 8%
Chemistry 5 8%
Immunology and Microbiology 1 2%
Other 3 5%
Unknown 7 11%