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

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

PROVIDER: PRJNA322635 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR3583360_1.fastq.gz	Fastqsanger.gz
	SRR3583360_2.fastq.gz	Fastqsanger.gz
	SRR3583370_1.fastq.gz	Fastqsanger.gz
	SRR3583370_2.fastq.gz	Fastqsanger.gz
	SRR3583361_1.fastq.gz	Fastqsanger.gz

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

Project 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.

| S-EPMC5580717 | biostudies-literature

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

Project 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.

2017-05-19 | GSE81824 | GEO

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

2017-05-19 | GSE81823 | GEO

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

Project description:This SuperSeries is composed of the SubSeries listed below.

2017-05-19 | GSE81825 | GEO

Detection of statistically robust interactions from diverse RNA-DNA ligation data

Project description:Detection of statistically robust interactions from diverse RNA-DNA ligation data

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Detection of statistically robust interactions from diverse RNA-DNA ligation data [RADICL-seq]

Project description:Detection of statistically robust interactions from diverse RNA-DNA ligation data [RADICL-seq]

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Detection of statistically robust interactions from diverse RNA-DNA ligation data [ATAC-seq]

Project description:Detection of statistically robust interactions from diverse RNA-DNA ligation data [ATAC-seq]

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Detection of statistically robust interactions from diverse RNA-DNA ligation data [nucRNA-seq]

Project description:Detection of statistically robust interactions from diverse RNA-DNA ligation data [nucRNA-seq]

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Detection of statistically robust interactions from diverse RNA-DNA ligation data

Project description:Chromatin-localized RNAs play diverse roles in gene regulation and nuclear architecture. Mapping genome-wide RNA-DNA interactions is possible using a variety of molecular methods, including using bridging oligonucleotides to ligate RNA and DNA in proximity. While molecular methods have progressed, a robust computational method for calling biologically meaningful RNA-DNA interactions from these data is lacking. Herein, we present RADIAnT, a reads-to-interactions pipeline for analyzing RNA-DNA ligation data. RADIAnT calls interactions against a dataset-specific, unified background which considers RNA binding site-TSS distance and genomic region bias. By scaling the background with RNA abundance, RADIAnT is sensitive enough to detect specific interactions of lowly expressed transcripts, while remaining specific enough to discount false positive interactions of highly abundant RNAs. RADIAnT outperforms previously proposed methods in the accurate recall of genome-wide Malat1-DNA interactions, and in a use-case, was utilized to identify dynamic chromatin-associated RNAs in the physiologically- and pathlologically-relevant process of endothelial-to-mesenchymal transition, whose gene regulatory behaviour was validated experimentally.

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Detection of statistically robust interactions from diverse RNA-DNA ligation data [ATAC-seq]

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