Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs

• Published in: Genome research Vol. 27; no. 9; pp. 1589 - 1596
• Main Authors: Legrand, Carine, Tuorto, Francesca, Hartmann, Mark, Liebers, Reinhard, Jacob, Dominik, Helm, Mark, Lyko, Frank
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.09.2017
• Subjects: Animals; DNA Methylation - genetics; High-Throughput Nucleotide Sequencing - methods; Humans; Method; Methyltransferases - genetics; Mice; RNA Processing, Post-Transcriptional - genetics; RNA, Messenger - genetics; RNA, Ribosomal, 28S - genetics; RNA, Transfer - genetics; Transcriptome - genetics
• ISSN: 1088-9051; 1549-5469
• DOI: 10.1101/gr.210666.116

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.