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Ribosome Traffic on mRNAs Maps to Gene Ontology: Genome-wide Quantification of Translation Initiation Rates and Polysome Size Regulation

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Ribosome Traffic on mRNAs Maps to Gene Ontology: Genome-wide Quantification of Translation Initiation Rates and Polysome Size Regulation
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002866
Pubmed ID
Authors

Luca Ciandrini, Ian Stansfield, M. Carmen Romano

Abstract

To understand the complex relationship governing transcript abundance and the level of the encoded protein, we integrate genome-wide experimental data of ribosomal density on mRNAs with a novel stochastic model describing ribosome traffic dynamics during translation elongation. This analysis reveals that codon arrangement, rather than simply codon bias, has a key role in determining translational efficiency. It also reveals that translation output is governed both by initiation efficiency and elongation dynamics. By integrating genome-wide experimental data sets with simulation of ribosome traffic on all Saccharomyces cerevisiae ORFs, mRNA-specific translation initiation rates are for the first time estimated across the entire transcriptome. Our analysis identifies different classes of mRNAs characterised by their initiation rates, their ribosome traffic dynamics, and by their response to ribosome availability. Strikingly, this classification based on translational dynamics maps onto key gene ontological classifications, revealing evolutionary optimisation of translation responses to be strongly influenced by gene function.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 4%
Germany 3 2%
United Kingdom 3 2%
Portugal 2 1%
Switzerland 1 <1%
Iran, Islamic Republic of 1 <1%
France 1 <1%
Estonia 1 <1%
Argentina 1 <1%
Other 0 0%
Unknown 153 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 28%
Student > Ph. D. Student 46 27%
Student > Master 16 9%
Student > Bachelor 10 6%
Student > Doctoral Student 9 5%
Other 26 15%
Unknown 18 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 73 42%
Biochemistry, Genetics and Molecular Biology 41 24%
Physics and Astronomy 11 6%
Computer Science 9 5%
Engineering 5 3%
Other 13 8%
Unknown 21 12%