Journal
CELL SYSTEMS
Volume 6, Issue 2, Pages 180-+Publisher
CELL PRESS
DOI: 10.1016/j.cels.2017.12.007
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Funding
- US NIH [R01-HG006677]
- US National Science Foundation [DBI-1350041]
- Cold Spring Harbor Laboratory (CSHL) Cancer Center Support Grant [5P30CA045508]
- NIH (NIGMS) [GM102192]
- NATIONAL CANCER INSTITUTE [P30CA045508] Funding Source: NIH RePORTER
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [R01HG006677] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R35GM127070, R01GM102192] Funding Source: NIH RePORTER
- Direct For Biological Sciences [1627442] Funding Source: National Science Foundation
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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 (https://github.com/schatzlab/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.
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