Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing
N6-Methyladenosine (m6A) is the most abundant modification of eukaryotic coding transcripts and is involved in the regulation of a variety of biological processes in physiological and disease conditions. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for the identification of m6A and other marks at near base-resolution within individual RNA molecules. Several tools were published for m6A detection on these data that exploit different approaches, working on transcriptome or genome space and relying on single samples or requiring multiple conditions. There is a strong need for independent studies benchmarking the tools 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. To address these points, we developed NanOlympicsMod, a Nextflow pipeline for executing and comparing tools for m6A detection on dRNA-seq data, and we applied it to several dRNA-seq datasets.