RNA modification detection using direct RNA sequencing and nanoDoc2

The core of nanoDoc2 includes a machine-learning algorithm in which a 6-mer segmented raw current signal is compared by Deep-One-Class classification using a Wavenet-based neural network. As an output, an RNA modification is detected by a statistical score in each candidate position. Herein, we describe the detailed instructions on how to use nanoDoc2 for signal segmentation, train/test the neural network and finally predict RNA modifications present in nanopore direct RNA sequence data.