Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT

ABSTRACT: MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT

PROVIDER: PRJNA834813 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR19067347_1.fastq.gz	Fastqsanger.gz
	SRR19067347_2.fastq.gz	Fastqsanger.gz
	SRR19067348_1.fastq.gz	Fastqsanger.gz
	SRR19067348_2.fastq.gz	Fastqsanger.gz
	SRR19067349_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 40

Similar Datasets

MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT

Project description:The study aimed to characterize the mutational signatures produced by MarathonRT upon reverse transcription of modified cellular RNAs and use the signatures of validated modified rRNAs as training data for machine learning algorithms. Performance of the resulting trained algorithms was evaluated on a series of test RNAs in order to establish a general workflow for detection of RNA modifications

2023-08-07 | GSE202160 | GEO

Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models

Project description:Chemical RNA modifications, collectively referred to as the ‘epitranscriptome’, have been intensively studied during the last years, largely facilitated by the use of next-generation sequencing technologies. Recent efforts have turned towards the nanopore direct RNA sequencing (DRS) platform, as it allows simultaneous detection of diverse RNA modification types in full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling ‘errors’ in DRS datasets, m6A modifications produce very modest ‘errors’, limiting the applicability of this approach to sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully-unmodified in vitro synthetic sequences, increase the ‘error’ signal of m6A modifications, leading to enhanced detection of RNA modifications even at lower stoichiometries. We then show that the use of these models enhances the detection of RNA modifications on previously published in vivo human samples, using third-party softwares for the detection of RNA modifications. Moreover, our work provides a novel RNA basecalling model that shows a median accuracy of 97%, compared to previously available RNA basecalling models that show 91% accuracy. Notably, this increase in accuracy does not only lead to improved detection of RNA modifications, but also enhanced mappability of RNA reads, which becomes more evident in the case of short RNA reads (50% increase). Altogether, our work stresses the importance of using fully unmodified RNA sequences for training RNA basecalling models, and how the use of different basecalling models can significantly affect the detection of RNA modifications and read mappability.

2024-08-26 | GSE246151 | GEO

Accurate detection of m6A RNA modifications in native RNA sequences [Yeast]

Project description:Accurate detection of m6A RNA modifications in native RNA sequences [Yeast]

| PRJNA521324 | ENA

Accurate detection of m6A RNA modifications in native RNA sequences [Curlcake constructs]

Project description:Accurate detection of m6A RNA modifications in native RNA sequences [Curlcake constructs]

| PRJNA511582 | ENA

Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models

Project description:Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models

| PRJNA1031667 | ENA

clampFISH 2.0 enables rapid, scalable amplified RNA detection in situ

Project description:clampFISH 2.0 enables rapid, scalable amplified RNA detection in situ

| PRJNA870496 | ENA

Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models

Project description:Detection of RNA modifications from dRNA-seq data using enhanced basecalling models

| PRJEB67632 | ENA

Detection of internal N7-methylguanosine (m7G) RNA modifications by mutational profiling sequencing

Project description:Detection of internal N7-methylguanosine (m7G) RNA modifications by mutational profiling sequencing

| PRJNA499084 | ENA

Detection of internal N7-methylguanosine (m7G) RNA modifications by mutational profiling sequencing

Project description:Methylation of guanosine on position N7 (m7G) on internal RNA positions have been found in all domains of life and have been implicated in human disease, but m7G modifications has so far only been mapped in a limited number of RNA molecules. Here, we present m7G Mutational Profiling sequencing (m7G-MaP-seq), which allows high throughput detection of m7G modifications. In our method, m7G modified positions are converted to abasic sites by mild reduction, recorded as cDNA mutations through reverse transcription, sequenced and subsequently detected by identification of positions with increased mutation rates in the reduced sample compared to the control. We show that m7G-MaP-seq efficiently detects m7G modifications in rRNA, including a previously uncharacterised rRNA modification in Arabidopsis thaliana. Furthermore, we identify m7G tRNA modifications in budding yeast, human and arabidopsis tRNA and show that m7G modification occurs before tRNA splicing. We do not find any evidence for internal m7G modifications being present in other small RNA, such as miRNA, snoRNA and sRNA. Likewise, high coverage m7G-MaP-seq analysis of mRNA from E. coli or yeast cells failed to identify any internal m7G modifications.

2018-10-30 | GSE121927 | GEO

Differential RNA modifications from dRNA-seq

Project description:Detection of differential RNA modifications from direct RNA sequencing of human cell lines

| PRJEB40872 | ENA