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N6-methyladenosine (m6A) is the most abundant internal eukaryotic mRNA modification, and is involved in the regulation of various biological processes. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for its identification. Several software were published for m6A detection and there is a strong need for independent studies benchmarking their performance on data from different species, and against various reference datasets. Moreover, a computational workflow is needed to streamline the execution of tools whose installation and execution remains complicated. We developed NanOlympicsMod, a Nextflow pipeline exploiting containerized technology for comparing 14 tools for m6A detection on dRNA-seq data. NanOlympicsMod was tested on dRNA-seq data generated from in vitro (un)modified synthetic oligos. The m6A hits returned by each tool were compared to the m6A position known by design of the oligos. In addition, NanOlympicsMod was used on dRNA-seq datasets from wild-type and m6A-depleted yeast, mouse and human, and each tool's hits were compared to reference m6A sets generated by leading orthogonal methods. The performance of the tools markedly differed across datasets, and methods adopting different approaches showed different preferences in terms of precision and recall. Changing the stringency cut-offs allowed for tuning the precision-recall trade-off towards user preferences. Finally, we determined that precision and recall of tools are markedly influenced by sequencing depth, and that additional sequencing would likely reveal additional m6A sites. Thanks to the possibility of including novel tools, NanOlympicsMod will streamline the benchmarking of m6A detection tools on dRNA-seq data, improving future RNA modification characterization., (© The Author(s) 2024. Published by Oxford University Press.)

RNA modifications can alter the behavior of RNA molecules depending on where they are located on the strands. Traditionally, RNA modifications have been detected and characterized by biophysical assays, mass spectrometry, or specific next-generation sequencing techniques, but are limited to specific modifications or are low throughput. Nanopore is a platform capable of sequencing RNA strands directly, which permits transcriptome-wide detection of RNA modifications. RNA modifications alter the nanopore raw signal relative to the canonical form of the nucleotide, and several software tools detect these signal alterations. One such tool is Nanocompore, which compares the ionic current features between two different experimental conditions (i.e., with and without RNA modifications) to detect RNA modifications. Nanocompore is not limited to a single type of RNA modification, has a high specificity for detecting RNA modifications, and does not require model training. To use Nanocompore, the following steps are needed: (i) the data must be basecalled and aligned to the reference transcriptome, then the raw ionic current signals are aligned to the sequences and transformed into a Nanocompore-compatible format; (ii) finally, the statistical testing is conducted on the transformed data and produces a table of p-value predictions for the positions of the RNA modifications. These steps can be executed with several different methods, and thus we have also included two alternative protocols for running Nanocompore. Once the positions of RNA modifications are determined by Nanocompore, users can investigate their function in various metabolic pathways. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: RNA modification detection by Nanocompore Alternate Protocol 1: RNA modification detection by Nanocompore with f5c Alternate Protocol 2: RNA modification detection by Nanocompore using Nextflow., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

The SARS-CoV-2 virus has a complex transcriptome characterised by multiple, nested subgenomic RNAsused to express structural and accessory proteins. Long-read sequencing technologies such as nanopore direct RNA sequencing can recover full-length transcripts, greatly simplifying the assembly of structurally complex RNAs. However, these techniques do not detect the 5' cap, thus preventing reliable identification and quantification of full-length, coding transcript models. Here we used Nanopore ReCappable Sequencing (NRCeq), a new technique that can identify capped full-length RNAs, to assemble a complete annotation of SARS-CoV-2 sgRNAs and annotate the location of capping sites across the viral genome. We obtained robust estimates of sgRNA expression across cell lines and viral isolates and identified novel canonical and non-canonical sgRNAs, including one that uses a previously un-annotated leader-to-body junction site. The data generated in this work constitute a useful resource for the scientific community and provide important insights into the mechanisms that regulate the transcription of SARS-CoV-2 sgRNAs., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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 m 6 A 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., (© 2021. The Author(s).)