Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution

Fang, Han, Huang, Yi-Fei, Radhakrishnan, Aditya, Siepel, Adam, Lyon, Gholson J., Schatz, Michael C. (February 2018) Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution. Cell Systems, 6 (2). pp. 180-191. ISSN 2405-4712

DOI: 10.1016/j.cels.2017.12.007


Summary Ribosome profiling (Ribo-seq) is a powerful technique for measuring protein translation; however, sampling errors and biological biases are prevalent and poorly understood. Addressing these issues, we present Scikit-ribo (, an open-source analysis package for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Ribo-seq and RNA sequencing data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next, we show that the commonly used reads per kilobase of transcript per million mapped reads-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 2,000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81), and allows us to determine the Kozak-like sequence directly from Ribo-seq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis and quantify the artifacts that can occur from cycloheximide treatment.

Item Type: Paper
Uncontrolled Keywords: Ribo-seq translation bioinformatics statistical method machine learning
Subjects: bioinformatics
bioinformatics > computational biology > algorithms > machine learning
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > translation
CSHL Authors:
Communities: CSHL Cancer Center Program > Cancer Genetics
CSHL labs > Lyon lab
CSHL labs > Schatz lab
CSHL labs > Siepel lab
CSHL Cancer Center Program > Cancer Genetics and Genomics Program
Depositing User: Matt Covey
Date: 28 February 2018
Date Deposited: 26 Jan 2018 17:13
Last Modified: 03 Nov 2020 14:23
PMCID: PMC5832574
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