An overview of using direct RNA sequencing to detect RNA modifications. (A) After performing RNA library preparation (ligation of the RNA to a helicase-containing DNA adapter, plus optional reverse transcription step), RNA molecules can translocate through the nanopore embedded on a membrane with the help of a helicase protein, at an approximate speed of 70 bases per second (under the library preparation kits: SQK-RNA001 and SQK-RNA002). RNA translocation disrupts the current created by the ion flow that is passing through the nanopore. The current intensity information is acquired and processed using MinKnow software, which generates Fast5 files containing the current intensity information of each identified read (one Fast5 file will contain up to 4000 reads). Fast5 files can then be base-called using base-calling algorithms (e.g., Guppy, Bonito), which are fed with a base-calling model, generating FastQ files as output. FastQ files can be mapped to a reference sequence by using an alignment tool (e.g., minimap2), which creates a BAM file. Modification information can be stored in BAM files using specific tags. (B) Schematic overview of three major strategies that can be used to detect RNA modifications in direct RNA nanopore sequencing data. A first strategy consists in identifying RNA modifications in the form of nonrandom base-calling “errors,” which can be seen in the form of increased base-calling “errors” (mismatch, insertion and deletion) at the modified site (position 0) and/or surrounding positions (left panel). In this strategy, the use of knockout and/or knockout strains allows distinguishing “errors” caused by the presence of RNA modifications from those that are intrinsic to the sequencing and or base-calling itself (i.e., background “error”). A second strategy involves using raw current intensity (signal intensity, dwell time and/or trace features) to identify positions with altered current intensity values, when comparing two strains (e.g., wild type and knockout) or when comparing reads within a given sample (middle panel). A third strategy consists in using a modification-aware base-calling model (instead of the canonical model that predicts four letters) when performing the base-calling step (right panel). This approach requires generating training sets that can be used to train the modification-aware base-calling model.
