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.
Project description:To investigate the potential mechanisms by which m6A contributes to ALS disease degeneration, we utilized a direct RNA sequencing platform, allowing for the identification of the m6A modification sites at single-nucleotide resolution. With the croreference of the ALS risk genes and transcriptomic RNA-seq data in ALS, we could further indntify the m6A-dependent potential genes and pathways in ALS.
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
Project description:We applied direct RNA long read sequencing for characterization of transcripts from constructs inserted into HEK293T mammalian cells with different promoters. Direct RNA sequencing was performed on an Oxford Nanopore GridION device using the Direct Sequencing Kit (SQK-RNA004, date accessed 15 May 2024), MinION RNA flow cell (FLO-MIN00RA), and data pre-processing was performed with MinKNOW (v24.06.10).
Project description:N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most abundant modifications in mammalian mRNA, but the coordination of their biological functions remains poorly understood. We develop a machine learning-based nanopore direct RNA sequencing method (NanoSPA) that simultaneously analyzes m6A and Ψ in the human transcriptome. Applying NanoSPA to polysome profiling, we reveal opposing transcriptomic co-occurrence of m6A and Ψ and synergistic, hierarchical effects of m6A and Ψ on the polysome.
Project description:m6A is a ubiquitous RNA modification in eukaryotes. Transcriptome-wide m6A patterns in Arabidopsis have been assayed recently. However, m6A differential patterns among organs have not been well characterized. The goal of the study is to comprehensively analyze m6A patterns of numerous types of RNAs, the relationship between transcript level and m6A methylation extent, and m6A differential patterns among organs in Arabidopsis. In total, 18 libraries were sequneced. For the 3 organs: leaf, flower and root, each organ has mRNA-Seq, m6A-Seq and Input sequenced. And each sequence has 2 replicats.