EpiNano: detection of m6A RNA modifications using Oxford nanopore direct RNA sequencing

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 m6A RNA modifications from dRNA-seq datasets. The latest version of EpiNano contains models for predicting m6A 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 m6A RNA modifications in dRNA-seq data.We thank all members of the Novoa lab for their valuable insights and discussion. We thank Rebeca Medina for obtaining the TapeStation image used for Fig. 1. OB is supported by an international PhD fellowship (UIPA) from the University of New South Wales. This work was supported by the Australian Research Council (DP180103571 to EMN) and the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) (PGC2018-098152-A-100 to EMN). We acknowledge the support of the MEIC to the EMBL partnership, Centro de Excelencia Severo Ochoa, and CERCA Program/Generalitat de Catalunya