Accurate Recovery of Ribosome Positions Reveals Slow Translation of Wobble-Pairing Codons in Yeast

  • Hao Wang
  • Joel McManus
  • Carl Kingsford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9649)


Ribosome profiling quantitatively captures ribosome locations during translation. The resulting profiles of ribosome locations are widely used to study translational speed. However, an accurate estimation of the ribosome location depends on identifying the A-site from ribosome profiling reads, a problem that was previously unsolved. Here, we propose a novel method to estimate the ribosome A-site positions from high-coverage ribosome profiling reads. Our model allows more reads to be used, accurately explains the 3-nt periodicity of ribosome profiling reads from various lengths, and recovers consistent ribosome positions across different lengths. Our recovered ribosome positions are correctly highly skewed towards a single frame within a codon. They retain sub-codon resolution and enable detection of off-frame translational events, such as frameshifts. Our method improves the correlation with other estimates of codon decoding time. Further, the refined profiles show that yeast wobble-pairing codons are translated slower than their synonymous Watson-Crick-pairing codons. These results provide evidence that protein synthetic rate can be tuned by codon usage bias.


Ribosome profiling A-site recovery Translation rate 



We thank Geet Duggal, Darya Filipova, Heewook Lee, Brad Solomon, Jing Xiang, Hongyi Xing, David Pellow, Pieter Spealman and Chengxi Ye for useful discussions. This research is funded in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4554 to Carl Kingsford, by the US National Science Foundation (CCF-1256087, CCF-1319998), and by the US National Institutes of Health (R21HG006913, R01HG007104). C.K. received support as an Alfred P. Sloan Research Fellow.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computational Biology Department, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Biological SciencesCarnegie Mellon UniversityPittsburghUSA

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