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Chemical RNA modifications, collectively referred to as the "epitranscriptome," are essential players in fine-tuning gene expression. Our ability to analyze RNA modifications has improved rapidly in recent years, largely due to the advent of high-throughput sequencing methodologies, which typically consist of coupling modification-specific reagents, such as antibodies or enzymes, to next-generation sequencing. Recently, it also became possible to map RNA modifications directly by sequencing native RNAs using nanopore technologies, which has been applied for the detection of a number of RNA modifications, such as N6-methyladenosine (m 6 A), pseudouridine (Ψ), and inosine (I). However, the signal modulations caused by most RNA modifications are yet to be determined. A global effort is needed to determine the signatures of the full range of RNA modifications to avoid the technical biases that have so far limited our understanding of the epitranscriptome., (© 2022 Begik et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)

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.

Nanopore RNA sequencing shows promise as a method for discriminating and identifying different RNA modifications in native RNA. Expanding on the ability of nanopore sequencing to detect N 6 -methyladenosine, we show that other modifications, in particular pseudouridine (Ψ) and 2'-O-methylation (Nm), also result in characteristic base-calling 'error' signatures in the nanopore data. Focusing on Ψ modification sites, we detected known and uncovered previously unreported Ψ sites in mRNAs, non-coding RNAs and rRNAs, including a Pus4-dependent Ψ modification in yeast mitochondrial rRNA. To explore the dynamics of pseudouridylation, we treated yeast cells with oxidative, cold and heat stresses and detected heat-sensitive Ψ-modified sites in small nuclear RNAs, small nucleolar RNAs and mRNAs. Finally, we developed a software, nanoRMS, that estimates per-site modification stoichiometries by identifying single-molecule reads with altered current intensity and trace profiles. This work demonstrates that Nm and Ψ RNA modifications can be detected in cellular RNAs and that their modification stoichiometry can be quantified by nanopore sequencing of native RNA., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

RNA modifications play pivotal roles in the RNA life cycle and RNA fate, and are now appreciated as a major posttranscriptional regulatory layer in the cell. In the last few years, direct RNA nanopore sequencing (dRNA-seq) has emerged as a promising technology that can provide single-molecule resolution maps of RNA modifications in their native RNA context. While native RNA can be successfully sequenced using this technology, the detection of RNA modifications is still challenging. Here, we provide an upgraded version of EpiNano (version 1.2), an algorithm to predict m 6 A RNA modifications from dRNA-seq datasets. The latest version of EpiNano contains models for predicting m 6 A RNA modifications in dRNA-seq data that has been base-called with Guppy. Moreover, it can now train models with features extracted from both base-called dRNA-seq FASTQ data and raw FAST5 nanopore outputs. Finally, we describe how EpiNano can be used in stand-alone mode to extract base-calling "error" features and current intensity information from dRNA-seq datasets. In this chapter, we provide step-by-step instructions on how to produce in vitro transcribed constructs to train EpiNano, as well as detailed information on how to use EpiNano to train, test, and predict m 6 A RNA modifications in dRNA-seq data.

The epitranscriptomics field 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 direct RNA sequencing, N 6 -methyladenosine (m 6 A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m 6 A-modified and unmodified synthetic sequences, can predict m 6 A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m 6 A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these 'errors' are typically not observed in yeast ime4-knockout strains, which lack m 6 A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

RNA modifications have been historically considered as fine-tuning chemo-structural features of infrastructural RNAs, such as rRNAs, tRNAs, and snoRNAs. This view has changed dramatically in recent years, to a large extent as a result of systematic efforts to map and quantify various RNA modifications in a transcriptome-wide manner, revealing that RNA modifications are reversible, dynamically regulated, far more widespread than originally thought, and involved in major biological processes, including cell differentiation, sex determination, and stress responses. Here we summarize the state of knowledge and provide a catalog of RNA modifications and their links to neurological disorders, cancers, and other diseases. With the advent of direct RNA-sequencing technologies, we expect that this catalog will help prioritize those RNA modifications for transcriptome-wide maps., (© 2017 Jonkhout et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)

RNA modifications can alter RNA structure-function relationships and various cellular processes. However, the genomic distribution and biological roles of most RNA modifications remain uncharacterized. Here, we propose using phage display antibody technology and direct sequencing through nanopores to facilitate systematic interrogation of the distribution, location and dynamics of RNA modifications.