DRUMMER—rapid detection of RNA modifications through comparative nanopore sequencing

Abstract

Motivation The chemical modification of ribonucleotides regulates the structure, stability, and interactions of RNAs. Profiling of these modifications using short-read (Illumina) sequencing techniques provides high sensitivity but low-to-medium resolution i.e., modifications cannot be assigned to specific transcript isoforms in regions of sequence overlap. An alternative strategy uses current fluctuations in nanopore-based long read direct RNA sequencing (DRS) to infer the location and identity of nucleotides that differ between two experimental conditions. While highly sensitive, these signal-level analyses require high quality transcriptome annotations and thus are best suited to the study of model organisms. By contrast, the detection of RNA modifications in microbial organisms which typically have no or low-quality annotations requires an alternative strategy. Here, we demonstrate that signal fluctuations directly influence error rates during base calling and thus provides an alternative approach for identifying modified nucleotides. Results DRUMMER (Detection of Ribonucleic acid Modifications Manifested in Error Rates (i) utilizes a range of statistical tests and background noise correction to identify modified nucleotides with high confidence, (ii) operates with similar sensitivity to signal-level analysis approaches, and (iii) correlates very well with orthogonal approaches. Using well-characterized DRS datasets supported by independent meRIP-Seq and miCLIP-Seq datasets we demonstrate that DRUMMER operates with high sensitivity and specificity. Availability and implementation DRUMMER is written in Python 3 and is available as open source in the GitHub repository: https://github.com/DepledgeLab/DRUMMER Supplementary information Supplementary data are available at Bioinformatics online.

Exploring the epitranscriptome by native RNA sequencing

“…Later studies showed that alterations in current intensity are also reflected in the base-calling data in the form of mismatches, insertions, and deletions (Liu et al 2019). These findings triggered several subsequent studies to detect m 6 A and Ψ modifications using base-calling "error signatures'' from nanopore sequencing data (Figure 1B, right panel) (Liu et al 2019;Parker et al 2020;Wongsurawat et al 2018;Viehweger et al 2019;Begik et al 2021;Abebe et al 2022). Furthermore, several studies took advantage of the current signal metrics to detect modified sites by comparison with a paired sample with fewer or no modifications (Figure 1B, middle panel) (Leger et al 2021;Begik et al 2021), using internal unmodified sequences from the same sequencing run (Ramasamy et al 2022) or implementing machine learning approaches to determine the proportion of modified molecules from single samples (Pratanwanich et al 2021;Begik et al 2021).…”

“…These findings triggered several subsequent studies to detect m 6 A and Ψ modifications using base-calling “error signatures” from nanopore sequencing data ( Fig. 1 B, right panel; Liu et al 2019 ; Viehweger et al 2019 ; Parker et al 2020 ; Begik et al 2021 ; Jenjaroenpun et al 2021 ; Abebe et al 2022 ). Furthermore, several studies took advantage of the current signal metrics to detect modified sites by comparison with a paired sample with fewer or no modifications ( Fig.…”

Nanopore-Based Detection of Viral RNA Modifications

“…Given the technical challenges and excessive cost associated with generating such a data set, it seems unlikely that this issue will be rectified soon. When considering which methodology/tool to use, it is worth noting that while powerful and generally easier to implement, error rate methodologies are limited to the level of RNA isoforms rather than individual RNAs and their power is greatest where read depths are very high, as sensitivity generally scales with depth of sequencing ( 107 ). Signal-level methodologies are, at present, also limited to the level of RNA isoforms but require significantly lower read depths to make accurate predictions and retain the promise of allowing analysis of individual reads in the future.…”

“…Choosing the right tool for a given analysis is a complex process. Read depth is a critical factor for several tools (DRUMMER, Eligos) ( 88 , 107 ). Others favor specificity over sensitivity (EpiNano, m6Anet, MINES).…”

RNA Modification in Inflammatory Bowel Diseases

“…These methods were further developed to be an internal comparison strategy termed “IndoC”, where features, such as “electric current signal intensity” and “raw signal trace meta-information” of potentially modified sites, are identified according to comparisons with other sequences within the sample [[ 36 ]. Moreover, another recent study indicated that the technology termed the detection of ribonucleic acid modifications manifested in error rates (DRUMMER) provides a rapid detection of RNA modifications through comparative nanopore sequencing [ 37 ]. Taken together, these recently developed analytical methods will be easily transferrable for general use in life science ( Table 1 ).…”