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Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs
Cytosine-5 RNA methylation plays an important role in several biologically and pathologically relevant processes. However, owing to methodological limitations, the transcriptome-wide distribution of this mark has remained largely unknown. We previously established RNA bisulfite sequencing as a metho...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580717/ https://www.ncbi.nlm.nih.gov/pubmed/28684555 http://dx.doi.org/10.1101/gr.210666.116 |
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author | Legrand, Carine Tuorto, Francesca Hartmann, Mark Liebers, Reinhard Jacob, Dominik Helm, Mark Lyko, Frank |
author_facet | Legrand, Carine Tuorto, Francesca Hartmann, Mark Liebers, Reinhard Jacob, Dominik Helm, Mark Lyko, Frank |
author_sort | Legrand, Carine |
collection | PubMed |
description | Cytosine-5 RNA methylation plays an important role in several biologically and pathologically relevant processes. However, owing to methodological limitations, the transcriptome-wide distribution of this mark has remained largely unknown. We previously established RNA bisulfite sequencing as a method for the analysis of RNA cytosine-5 methylation patterns at single-base resolution. More recently, next-generation sequencing has provided opportunities to establish transcriptome-wide maps of this modification. Here, we present a computational approach that integrates tailored filtering and data-driven statistical modeling to eliminate many of the artifacts that are known to be associated with bisulfite sequencing. By using RNAs from mouse embryonic stem cells, we performed a comprehensive methylation analysis of mouse tRNAs, rRNAs, and mRNAs. Our approach identified all known methylation marks in tRNA and two previously unknown but evolutionary conserved marks in 28S rRNA. In addition, mRNAs were found to be very sparsely methylated or not methylated at all. Finally, the tRNA-specific activity of the DNMT2 methyltransferase could be resolved at single-base resolution, which provided important further validation. Our approach can be used to profile cytosine-5 RNA methylation patterns in many experimental contexts and will be important for understanding the function of cytosine-5 RNA methylation in RNA biology and in human disease. |
format | Online Article Text |
id | pubmed-5580717 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-55807172017-09-14 Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs Legrand, Carine Tuorto, Francesca Hartmann, Mark Liebers, Reinhard Jacob, Dominik Helm, Mark Lyko, Frank Genome Res Method Cytosine-5 RNA methylation plays an important role in several biologically and pathologically relevant processes. However, owing to methodological limitations, the transcriptome-wide distribution of this mark has remained largely unknown. We previously established RNA bisulfite sequencing as a method for the analysis of RNA cytosine-5 methylation patterns at single-base resolution. More recently, next-generation sequencing has provided opportunities to establish transcriptome-wide maps of this modification. Here, we present a computational approach that integrates tailored filtering and data-driven statistical modeling to eliminate many of the artifacts that are known to be associated with bisulfite sequencing. By using RNAs from mouse embryonic stem cells, we performed a comprehensive methylation analysis of mouse tRNAs, rRNAs, and mRNAs. Our approach identified all known methylation marks in tRNA and two previously unknown but evolutionary conserved marks in 28S rRNA. In addition, mRNAs were found to be very sparsely methylated or not methylated at all. Finally, the tRNA-specific activity of the DNMT2 methyltransferase could be resolved at single-base resolution, which provided important further validation. Our approach can be used to profile cytosine-5 RNA methylation patterns in many experimental contexts and will be important for understanding the function of cytosine-5 RNA methylation in RNA biology and in human disease. Cold Spring Harbor Laboratory Press 2017-09 /pmc/articles/PMC5580717/ /pubmed/28684555 http://dx.doi.org/10.1101/gr.210666.116 Text en © 2017 Legrand et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Method Legrand, Carine Tuorto, Francesca Hartmann, Mark Liebers, Reinhard Jacob, Dominik Helm, Mark Lyko, Frank Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs |
title | Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs |
title_full | Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs |
title_fullStr | Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs |
title_full_unstemmed | Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs |
title_short | Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs |
title_sort | statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse rnas |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580717/ https://www.ncbi.nlm.nih.gov/pubmed/28684555 http://dx.doi.org/10.1101/gr.210666.116 |
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