Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models
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PROVIDER: PRJEB67632 | ENA |
REPOSITORIES: ENA
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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
Project description:Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models
| PRJNA1031667 | ENA
Project description:Nanopore direct RNA seq-based basecalling of m6A
| PRJEB61874 | ENA
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.
| S-EPMC8664944 | biostudies-literature
Project description:BackgroundDespite recent progress in basecalling of Oxford nanopore DNA sequencing data, its wide adoption is still being hampered by its relatively low accuracy compared to short read technologies. Furthermore, very little of the recent research was focused on basecalling of RNA data, which has different characteristics than its DNA counterpart.ResultsWe fill this gap by benchmarking a fully convolutional deep learning basecalling architecture with improved performance compared to Oxford nanopore's RNA basecallers.AvailabilityThe source code for our basecaller is available at: https://github.com/biodlab/RODAN .
| S-EPMC9020074 | biostudies-literature
Project description:Nanopore direct RNA seq-based basecalling of m6A (RNA004 direct RNA sequenced curlcakes)
| PRJEB82528 | ENA
Project description:We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usually of high biological interest. With sequence backbones resolved, we further run anomaly detection (AD) on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide, and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD .
| S-EPMC11339354 | biostudies-literature
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.
| S-EPMC9154255 | biostudies-literature
Project description:MotivationNanopore sequencing is emerging as a key pillar in the genomic technology landscape but computational constraints limiting its scalability remain to be overcome. The translation of raw current signal data into DNA or RNA sequence reads, known as 'basecalling', is a major friction in any nanopore sequencing workflow. Here, we exploit the advantages of the recently developed signal data format 'SLOW5' to streamline and accelerate nanopore basecalling on high-performance computing (HPC) and cloud environments.ResultsSLOW5 permits highly efficient sequential data access, eliminating a potential analysis bottleneck. To take advantage of this, we introduce Buttery-eel, an open-source wrapper for Oxford Nanopore's Guppy basecaller that enables SLOW5 data access, resulting in performance improvements that are essential for scalable, affordable basecalling.Availability and implementationButtery-eel is available at https://github.com/Psy-Fer/buttery-eel.
| S-EPMC10261880 | biostudies-literature
Project description:Modifications are present on many classes of RNA, including tRNA, rRNA, and mRNA. These modifications modulate diverse biological processes such as genetic recoding and mRNA export and folding. In addition, modifications can be introduced to RNA molecules using chemical probing strategies that reveal RNA structure and dynamics. Many methods exist to detect RNA modifications by short-read sequencing; however, limitations on read length inherent to short-read-based methods dissociate modifications from their native context, preventing single-molecule modification analysis. Here, we demonstrate direct RNA nanopore sequencing to detect endogenous and exogenous RNA modifications on long RNAs at the single-molecule level. We detect endogenous 2'-O-methyl and base modifications across E. coli and S. cerevisiae ribosomal RNAs as shifts in current signal and dwell times distally through interactions with the helicase motor protein. We further use the 2'-hydroxyl reactive SHAPE reagent acetylimidazole to probe RNA structure at the single-molecule level with readout by direct nanopore sequencing.
| S-EPMC8896822 | biostudies-literature