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Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution

View ORCID ProfileHan Fang, Yi-Fei Huang, Aditya Radhakrishnan, Adam Siepel, View ORCID ProfileGholson J. Lyon, View ORCID ProfileMichael C. Schatz
doi: https://doi.org/10.1101/156588
Han Fang
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, NY, USA, 11724
2Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY 11794
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Yi-Fei Huang
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, NY, USA, 11724
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Aditya Radhakrishnan
3Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA, 21205
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Adam Siepel
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, NY, USA, 11724
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Gholson J. Lyon
4Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, NY, USA, 11724
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Michael C. Schatz
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, NY, USA, 11724
5Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA 21211
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  • For correspondence: mschatz@cs.jhu.edu
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Abstract

Ribosome profiling (Riboseq) is a powerful technique for measuring protein translation, however, sampling errors and biological biases are prevalent and poorly understand. Addressing these issues, we present Scikit-ribo (https://github.com/hanfang/scikit-ribo), the first open-source software for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Riboseq and RNAseq data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next we show commonly used RPKM-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81). From this, we determine the Kozak-like sequence directly from Riboseq and discover novel roles of the DEAD-box protein Dhh1p, deepening our understanding of translation control.

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Posted June 27, 2017.
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Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution
Han Fang, Yi-Fei Huang, Aditya Radhakrishnan, Adam Siepel, Gholson J. Lyon, Michael C. Schatz
bioRxiv 156588; doi: https://doi.org/10.1101/156588
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Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution
Han Fang, Yi-Fei Huang, Aditya Radhakrishnan, Adam Siepel, Gholson J. Lyon, Michael C. Schatz
bioRxiv 156588; doi: https://doi.org/10.1101/156588

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