Scikit-ribo Enables 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

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

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. New open-source statistical learning software package enables accurate analysis of translational efficiency from Ribo-seq and RNA-seq data. Using it corrects the biases for thousands of genes in S. cerevisiae, which enables improved estimates of relative protein abundances and the discovery of the Kozak-like regulatory sequence in yeast from Ribo-seq data.

Original languageEnglish (US)
Pages (from-to)180-191.e4
JournalCell Systems
Volume6
Issue number2
DOIs
StatePublished - Feb 28 2018

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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