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Abebe, Jonathan S; Price, Alexander M; Hayer, Katharina E; Mohr, Ian; Weitzman, Matthew D; Wilson, Angus C; Depledge, Daniel P
Bioinformatics, 05/2022, Letnik: 38, Številka: 11Journal Article
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.
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