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Metadata

Name
Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs [mRNA bisulfite sequencing]
Repository
Gene Expression Omnibus
Identifier
geo.series:GSE81824
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 have previously established RNA bisulfite sequencing as a method for the analysis of RNA (cytosine-5) methylation patterns at single-base resolution. Furthermore, next-generation sequencing has provided opportunities to establish transcriptome-wide maps of this modification. We have now established a computational approach that integrates tailored filtering and data-driven statistical modelling to eliminate many of the artifacts that are known to be associated with bisulfite sequencing. Using RNAs from mouse embryonic stem cells we have 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. Furthermore, the catalytic activities of the Dnmt2 tRNA methyltransferase could be resolved at single-base resolution. Of note, mRNAs were found to be very sparsely methylated or not methylated at all, which provides an important reference for further studies. Our approach can be used to profile (cytosine-5) RNA methylation patterns in many experimental contexts be important for understanding the function of (cytosine-5) RNA methylation in RNA biology and in human disease.
Data or Study Types
Methylation profiling by high throughput sequencing
Source Organization
National Center for Biotechnology Information
Access Conditions
available
Year
2017
Access Hyperlink
http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GSE81824

Distributions

  • Encoding Format: TXT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE81824/matrix/
  • Encoding Format: MINiML ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE81824/miniml/
  • Encoding Format: SOFT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE81824/soft/
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.