Literature DB >> 26102273

Transcriptome-wide measurement of ribosomal occupancy by ribosome profiling.

Florian Aeschimann1, Jieyi Xiong2, Andreas Arnold1, Christoph Dieterich3, Helge Großhans4.   

Abstract

Gene expression profiling provides a tool to analyze the internal states of cells or organisms, and their responses to perturbations. While global measurements of mRNA levels have thus been widely used for many years, it is only through the recent development of the ribosome profiling technique that an analogous examination of global mRNA translation programs has become possible. Ribosome profiling reveals which RNAs are being translated to what extent and where the translated open reading frames are located. In addition, different modes of translation regulation can be distinguished and characterized. Here, we present an optimized, step-by-step protocol for ribosome profiling. Although established in Caenorhabditis elegans, our protocol and optimization approaches should be equally usable for other model organisms or cell culture with little adaptation. Next to providing a protocol, we compare two different methods for isolation of single ribosomes and two different library preparations, and describe strategies to optimize the RNase digest and to reduce ribosomal RNA contamination in the libraries. Moreover, we discuss bioinformatic strategies to evaluate the quality of the data and explain how the data can be analyzed for different applications. In sum, this article seeks to facilitate the understanding, execution, and optimization of ribosome profiling experiments.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Caenorhabditis elegans; Polysome profiling; Post-transcriptional regulation; Ribosome profiling; Translation; Translational control

Mesh:

Substances:

Year:  2015        PMID: 26102273     DOI: 10.1016/j.ymeth.2015.06.013

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  14 in total

Review 1.  Regulation of bacterial gene expression by ribosome stalling and rescuing.

Authors:  Yongxin Jin; Shouguang Jin; Weihui Wu
Journal:  Curr Genet       Date:  2015-11-26       Impact factor: 3.886

2.  Estimation of ribosome profiling performance and reproducibility at various levels of resolution.

Authors:  Alon Diament; Tamir Tuller
Journal:  Biol Direct       Date:  2016-05-10       Impact factor: 4.540

3.  Unsupervised Bayesian Prediction of RNA Translation from Ribosome Profiling Data.

Authors:  Etienne Boileau; Christoph Dieterich
Journal:  Methods Mol Biol       Date:  2021

4.  Accurate detection of short and long active ORFs using Ribo-seq data.

Authors:  Saket Choudhary; Wenzheng Li; Andrew D Smith
Journal:  Bioinformatics       Date:  2020-04-01       Impact factor: 6.937

5.  Bayesian prediction of RNA translation from ribosome profiling.

Authors:  Brandon Malone; Ilian Atanassov; Florian Aeschimann; Xinping Li; Helge Großhans; Christoph Dieterich
Journal:  Nucleic Acids Res       Date:  2017-04-07       Impact factor: 16.971

6.  Translation and codon usage regulate Argonaute slicer activity to trigger small RNA biogenesis.

Authors:  Meetali Singh; Eric Cornes; Blaise Li; Piergiuseppe Quarato; Loan Bourdon; Florent Dingli; Damarys Loew; Simone Proccacia; Germano Cecere
Journal:  Nat Commun       Date:  2021-06-09       Impact factor: 14.919

7.  N6-adenosine methylation of ribosomal RNA affects lipid oxidation and stress resistance.

Authors:  Noa Liberman; Zach K O'Brown; Andrew Scott Earl; Konstantinos Boulias; Maxim V Gerashchenko; Simon Yuan Wang; Colette Fritsche; Paul-Enguerrand Fady; Anna Dong; Vadim N Gladyshev; Eric Lieberman Greer
Journal:  Sci Adv       Date:  2020-04-22       Impact factor: 14.136

8.  Non-Canonical Caspase Activity Antagonizes p38 MAPK Stress-Priming Function to Support Development.

Authors:  Benjamin P Weaver; Yi M Weaver; Shizue Omi; Wang Yuan; Jonathan J Ewbank; Min Han
Journal:  Dev Cell       Date:  2020-04-16       Impact factor: 12.270

9.  Dual randomization of oligonucleotides to reduce the bias in ribosome-profiling libraries.

Authors:  Aarón Lecanda; Benedikt S Nilges; Puneet Sharma; Danny D Nedialkova; Juliane Schwarz; Juan M Vaquerizas; Sebastian A Leidel
Journal:  Methods       Date:  2016-07-19       Impact factor: 3.608

10.  Discovery of numerous novel small genes in the intergenic regions of the Escherichia coli O157:H7 Sakai genome.

Authors:  Sarah M Hücker; Zachary Ardern; Tatyana Goldberg; Andrea Schafferhans; Michael Bernhofer; Gisle Vestergaard; Chase W Nelson; Michael Schloter; Burkhard Rost; Siegfried Scherer; Klaus Neuhaus
Journal:  PLoS One       Date:  2017-09-13       Impact factor: 3.240

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