Journal
BIOINFORMATICS
Volume 38, Issue 11, Pages 3113-3115Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac274
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Funding
- National Institute of Health [R01AI073898, R01-GM056927, AI130618, AI147163, AI145266, AI121321, AI118891]
- National Cancer Institute [CA115299]
- Individual National Research Service Award [AI138432]
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The study introduces a new method called DRUMMER for detecting RNA modifications. This method combines statistical tests and noise correction, resulting in high accuracy and sensitivity. It also shows good correlation with other methods.
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.
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