deltaTE: Detection of translationally regulated genes by integrative analysis of Ribo-seq and RNA-seq data
deltaTE: Detection of translationally regulated genes by integrative analysis of Ribo-seq and RNA-seq data
Ribosome profiling quantifies the genome-wide ribosome occupancy of transcripts. With the integration of matched RNA sequencing data, the translation efficiency (TE) of genes can be calculated to reveal translational regulation. This layer of gene-expression regulation is otherwise difficult to assess on a global scale and generally not well understood in the context of human disease. Current statistical methods to calculate differences in TE have low accuracy, cannot accommodate complex experimental designs or confounding factors, and do not categorize genes into buffered, intensified, or exclusively translationally regulated genes. This article outlines a method [referred to as deltaTE (ΔTE), standing for change in TE] to identify translationally regulated genes, which addresses the shortcomings of previous methods. In an extensive benchmarking analysis, ΔTE outperforms all methods tested. Furthermore, applying ΔTE on data from human primary cells allows detection of substantially more translationally regulated genes, providing a clearer understanding of translational regulation in pathogenic processes. In this article, we describe protocols for data preparation, normalization, analysis, and visualization, starting from raw sequencing files. © 2019 The Authors. Basic Protocol: One-step detection and classification of differential translation efficiency genes using DTEG.R Alternate Protocol: Step-wise detection and classification of differential translation efficiency genes using R Support Protocol: Workflow from raw data to read counts.
Databases, Genetic, Genome, High-Throughput Nucleotide Sequencing/methods, Humans, Protein Biosynthesis/genetics, RNA/metabolism, RNA-Seq/methods, Ribosomes/genetics, Sequence Analysis, RNA/methods, Software, Transcriptome
e108
Chothani, Sonia
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Adami, Eleonora
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Ouyang, John F
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Viswanathan, Sivakumar
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Hubner, Norbert
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Cook, Stuart A
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Schafer, Sebastian
6946786d-17c8-4c35-a6ac-871cc7a82cf2
Rackham, Owen J L
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
December 2019
Chothani, Sonia
24850611-01f3-46ae-af99-8c2693e6ca8f
Adami, Eleonora
c68076ed-059b-4f12-bb41-22f55de4cbae
Ouyang, John F
ce6f93a5-b40f-4add-8d7b-3ae795c1a4cb
Viswanathan, Sivakumar
3a7c01b3-ebb9-4329-96a6-f1b111a8a765
Hubner, Norbert
e01e1d75-f475-4d26-a3de-ba4150133c34
Cook, Stuart A
2c9730f9-0c9b-4d81-a32b-2fa1180b8153
Schafer, Sebastian
6946786d-17c8-4c35-a6ac-871cc7a82cf2
Rackham, Owen J L
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
Chothani, Sonia, Adami, Eleonora, Ouyang, John F, Viswanathan, Sivakumar, Hubner, Norbert, Cook, Stuart A, Schafer, Sebastian and Rackham, Owen J L
(2019)
deltaTE: Detection of translationally regulated genes by integrative analysis of Ribo-seq and RNA-seq data.
Current protocols in molecular biology, 129 (1), .
(doi:10.1002/cpmb.108).
Abstract
Ribosome profiling quantifies the genome-wide ribosome occupancy of transcripts. With the integration of matched RNA sequencing data, the translation efficiency (TE) of genes can be calculated to reveal translational regulation. This layer of gene-expression regulation is otherwise difficult to assess on a global scale and generally not well understood in the context of human disease. Current statistical methods to calculate differences in TE have low accuracy, cannot accommodate complex experimental designs or confounding factors, and do not categorize genes into buffered, intensified, or exclusively translationally regulated genes. This article outlines a method [referred to as deltaTE (ΔTE), standing for change in TE] to identify translationally regulated genes, which addresses the shortcomings of previous methods. In an extensive benchmarking analysis, ΔTE outperforms all methods tested. Furthermore, applying ΔTE on data from human primary cells allows detection of substantially more translationally regulated genes, providing a clearer understanding of translational regulation in pathogenic processes. In this article, we describe protocols for data preparation, normalization, analysis, and visualization, starting from raw sequencing files. © 2019 The Authors. Basic Protocol: One-step detection and classification of differential translation efficiency genes using DTEG.R Alternate Protocol: Step-wise detection and classification of differential translation efficiency genes using R Support Protocol: Workflow from raw data to read counts.
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More information
e-pub ahead of print date: 17 October 2019
Published date: December 2019
Additional Information:
© 2019 The Authors.
Keywords:
Databases, Genetic, Genome, High-Throughput Nucleotide Sequencing/methods, Humans, Protein Biosynthesis/genetics, RNA/metabolism, RNA-Seq/methods, Ribosomes/genetics, Sequence Analysis, RNA/methods, Software, Transcriptome
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Local EPrints ID: 447927
URI: http://eprints.soton.ac.uk/id/eprint/447927
ISSN: 1934-3647
PURE UUID: dfc17a7b-32d9-4c30-b501-36a372cd39a5
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Date deposited: 26 Mar 2021 17:30
Last modified: 17 Mar 2024 04:03
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Contributors
Author:
Sonia Chothani
Author:
Eleonora Adami
Author:
John F Ouyang
Author:
Sivakumar Viswanathan
Author:
Norbert Hubner
Author:
Stuart A Cook
Author:
Sebastian Schafer
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