ArticleBioinformatics (Oxford, England)2022
DRUMMER-rapid detection of RNA modifications through comparative nanopore sequencing.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 67 citations in OpenAlex.
- Advanced deep learning strategies in nanopore RNA sequencing.RNA biology · 2026Review
- Interrogating the Escherichia coli Epitranscriptome Via CRISPR Interference and Nanopore Native RNA Sequencing.MicrobiologyOpen · 2026Article
- Compendium of RNA modifications for bacterial stress adaptation.Microbiology and molecular biology reviews : MMBR · 2026Review
- ModiCal: A Targeted Calibration Workflow for Site-Specific mACS chemical biology · 2026Article
- Systematic benchmarking of dorado basecalling models for RNA modification detection with highly multiplexed nanopore sequencing.Nucleic acids research · 2026Article
- Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes.iMeta · 2026Review
- FIRST-seq: a nanopore-based cDNA sequencing platform for RNA modification and structure profiling.Genome biology · 2026Article
- Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking.Advanced biotechnology · 2026Article
- Review
- RMPore: a comprehensive database of single-molecule RNA modifications detected by Nanopore direct RNA sequencing.Nucleic acids research · 2026Article
- tRNA isodecoder analysis using Nanopore ionic current signals and deep learning.bioRxiv : the preprint server for biology · 2025Article
- Nanopore based RNA methylation profiling of a circulating lung cancer biomarker.Communications medicine · 2025Article
- Comprehensive discovery of mNature communications · 2025Article
- Exploring RNA modifications in infectious non-coding circular RNAs.RNA biology · 2025Article
- Investigating RNA dynamics from single molecule transcriptomes.Trends in genetics : TIG · 2025Review
- Transcriptomics in the era of long-read sequencing.Nature reviews. Genetics · 2025Review
- Enhanced detection of RNA modifications in Escherichia coli utilizing direct RNA sequencing.Cell reports methods · 2025Article
- ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.Nucleic acids research · 2025Article
- The RMaP challenge of predicting RNA modifications by nanopore sequencing.Communications chemistry · 2025Article
- Direct RNA sequencing of the Escherichia coli epitranscriptome uncovers alterations under heat stress.Nucleic acids research · 2025Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
motivationThe 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.
resultsDRUMMER (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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.