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

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

PROVIDER: PRJNA1031667 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR26501205_1.fastq.gz	Fastqsanger.gz
	SRR26501206_1.fastq.gz	Fastqsanger.gz
	SRR26501207_1.fastq.gz	Fastqsanger.gz
	SRR26501208_1.fastq.gz	Fastqsanger.gz
	SRR26501209_1.fastq.gz	Fastqsanger.gz

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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

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

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Project description:Nanopore direct RNA seq-based basecalling of m6A

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RNA modifications detection by comparative Nanopore direct RNA sequencing.

Project description:RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. In recent years, a growing number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework that identifies modifications from these data. Our strategy compares an RNA sample of interest against a non-modified control sample, not requiring a training set and allowing the use of replicates. We show that Nanocompore can detect different RNA modifications with position accuracy in vitro, and we apply it to profile m6A in vivo in yeast and human RNAs, as well as in targeted non-coding RNAs. We confirm our results with orthogonal methods and provide novel insights on the co-occurrence of multiple modified residues on individual RNA molecules.

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

Project description:The field of epitranscriptomics has experienced a major revolution in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here we show that using the platform offered by Oxford Nanopore Technologies, we can detect N6-methyladenosine (m6A) RNA modifications with high accuracy. Our results open new avenues to investigate the universe of RNA modifications with single nucleotide, single molecule resolution.

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Project description:The field of epitranscriptomics has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here we show that using Oxford Nanopore Technologies, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Our results open new avenues to investigate the universe of RNA modifications with single nucleotide resolution, in individual RNA molecules.

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RODAN: a fully convolutional architecture for basecalling nanopore RNA sequencing data.

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DRUMMER-Rapid detection of RNA modifications through comparative nanopore sequencing.

Project description:The chemical modification of ribonucleotides regulates the structure, stability, and interactions of RNAs. Profiling of these modifications using short-read (Illumina) sequencing techniques provides high sensitivity but low-to-medium resolution i.e., modifications cannot be assigned to specific transcript isoforms in regions of sequence overlap. An alternative strategy uses current fluctuations in nanopore-based long read direct RNA sequencing (DRS) to infer the location and identity of nucleotides that differ between two experimental conditions. While highly sensitive, these signal-level analyses require high quality transcriptome annotations and thus are best suited to the study of model organisms. By contrast, the detection of RNA modifications in microbial organisms which typically have no or low-quality annotations requires an alternative strategy. Here, we demonstrate that signal fluctuations directly influence error rates during base calling and thus provides an alternative approach for identifying modified nucleotides. DRUMMER (Detection of Ribonucleic acid Modifications Manifested in Error Rates (i) utilizes a range of statistical tests and background noise correction to identify modified nucleotides with high confidence, (ii) operates with similar sensitivity to signal-level analysis approaches, and (iii) correlates very well with orthogonal approaches. Using well-characterized DRS datasets supported by independent meRIP-Seq and miCLIP-Seq datasets we demonstrate that DRUMMER operates with high sensitivity and specificity. DRUMMER is written in Python 3 and is available as open source in the GitHub repository: https://github.com/DepledgeLab/DRUMMER. Supplementary data are available at Bioinformatics online.

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