Evaluation of Nanopore direct RNA sequencing updates for modification detection
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PROVIDER: PRJEB89590 | ENA |
REPOSITORIES: ENA
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Project description:RNA internal modifications play critical role in development of multicellular organisms and their response to environmental cues. Using nanopore direct RNA sequencing (DRS), we constructed a large in vitro epitranscriptome (IVET) resource from plant cDNA library labeled with m6A, m1A and m5C respectively. Furthermore, after transfer learning, the pre-trained model was used to detect additional RNA internal modification such as m1A, hm5C, m7G and Ψ modification. Finally, we illustrated a global view of epitranscriptome with m6A, m1A, m5C, m7G and Ψ modification in rice seedlings under normal and high salinity environment. In summary, we provided a strategy for creating IVET resource from cDNA library and developed a computational method that use IVET-based transfer learning termed TandemMod for profiling epitranscriptome landscape with co-occupancy of multiple types of RNA modification in plants responsive to environmental signal.
2024-03-28 | GSE227087 | GEO
Project description:The covalent modification of RNA molecules is a pervasive feature of all classes of RNAs and has fundamental roles in the regulation of several cellular processes. Mapping the location of RNA modifications transcriptome-wide is key to unveiling their role and dynamic behaviour, but technical limitations have often hampered these efforts. Nanopore direct RNA sequencing is a third-generation sequencing technology that allows the sequencing of native RNA molecules, thus providing a direct way to detect modifications at single-molecule resolution. Despite recent advances, the analysis of nanopore sequencing data for RNA modification detection is still a complex task that presents many challenges. Many works have addressed this task using different approaches, resulting in a large number of tools with different features and performances. Here we review the diverse approaches proposed so far and outline the principles underlying currently available algorithms.
| S-EPMC8677041 | biostudies-literature
Project description:Detection of SINEUP RNA modification by Oxford Nanopore direct-RNA sequencing
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Project description:To detect the modifed bases in SINEUP RNA, we compared chemically modified in vitro transcribed (IVT) SINEUP-GFP RNA and in-cell transcribed (ICT) SINEUP RNA from SINEUP-GFP and sense EGFP co-transfected HEK293T/17 cells. Comparative study of Nanopore direct RNA sequencing data from non-modified and modified IVT samples against the data from ICT SINEUP RNA sample revealed modified k-mers positions in SINEUP RNA in the cell.
2023-02-04 | GSE224018 | GEO
Project description:Nanopore sequencing of K562 for DNA cytosine modification detection
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Project description:RNA modification classification using neural network
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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:Direct RNA sequencing from Oxford Nanopore Technologies (ONT) has become a valuable method for studying RNA modifications such as N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C). Recent advancements in the RNA004 chemistry substantially reduce sequencing errors compared to previous chemistries (e.g., RNA002), thereby promising enhanced accuracy for epitranscriptomic analysis. In this study, we benchmark the performance of two state-of-the-art RNA modification detection models capable of handling RNA004 data - ONT's Dorado and m6Anet - using two wild-type (WT) cell lines, HEK293T and HeLa, with respective ground truths from GLORI and eTAM-seq, and their paired in vitro transcribed (IVT) RNA as negative controls. We found that under default settings and considering sites with ≥10% modification ratio and ≥10X coverage, Dorado has higher recall (~0.92) than m6Anet (~0.51) for m6A detection. Among the overlapping methylated sites between ground truth and computational predictions, there are high correlations of site-specific m6A modification stoichiometry, with correlation coefficient of ~0.89 for Dorado-truth comparison and ~0.72 for m6Anet-truth comparison. However, combined assessment of WT and IVT datasets show that while the per-site false positive rate (FPR) can be lower (~8% for Dorado and ~33% for m6Anet), both computational tools can have high per-site false discovery rate (FDR) of m6A (~40% for Dorado and ~80% for m6Anet) due to the low prevalence of m6A in transcriptome, with a similar trend observed for pseudouridine (~95% FDR for Dorado). Additional motif analysis reveals that both Dorado and m6Anet exhibit high heterogeneity of false positive calls across sequence contexts, suggesting that sequence contexts help determine accuracy of specific modification calls. There is also a substantial overlap of false positive calls between the two IVT samples, suggesting a post-filtering strategy to improve modification calling by compiling a set of low-confidence sites with a probabilistic model from several IVT samples across diverse cells/tissues. Our analysis highlights key strengths and limitations of the current generation of m6A detection algorithms and offers insights into optimizing thresholds and interpretability. The IVT datasets generated by the RNA004 chemistry provides a publicly available benchmark resource for further development and refinement of computational methods.
| S-EPMC11838592 | biostudies-literature
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