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Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis
Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041805/ https://www.ncbi.nlm.nih.gov/pubmed/33846332 http://dx.doi.org/10.1038/s41467-021-22489-2 |
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author | Liu, Xin Sun, Tao Shcherbina, Anna Li, Qin Jarmoskaite, Inga Kappel, Kalli Ramaswami, Gokul Das, Rhiju Kundaje, Anshul Li, Jin Billy |
author_facet | Liu, Xin Sun, Tao Shcherbina, Anna Li, Qin Jarmoskaite, Inga Kappel, Kalli Ramaswami, Gokul Das, Rhiju Kundaje, Anshul Li, Jin Billy |
author_sort | Liu, Xin |
collection | PubMed |
description | Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code. |
format | Online Article Text |
id | pubmed-8041805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80418052021-04-30 Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis Liu, Xin Sun, Tao Shcherbina, Anna Li, Qin Jarmoskaite, Inga Kappel, Kalli Ramaswami, Gokul Das, Rhiju Kundaje, Anshul Li, Jin Billy Nat Commun Article Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code. Nature Publishing Group UK 2021-04-12 /pmc/articles/PMC8041805/ /pubmed/33846332 http://dx.doi.org/10.1038/s41467-021-22489-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Xin Sun, Tao Shcherbina, Anna Li, Qin Jarmoskaite, Inga Kappel, Kalli Ramaswami, Gokul Das, Rhiju Kundaje, Anshul Li, Jin Billy Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis |
title | Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis |
title_full | Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis |
title_fullStr | Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis |
title_full_unstemmed | Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis |
title_short | Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis |
title_sort | learning cis-regulatory principles of adar-based rna editing from crispr-mediated mutagenesis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041805/ https://www.ncbi.nlm.nih.gov/pubmed/33846332 http://dx.doi.org/10.1038/s41467-021-22489-2 |
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