bioRxivBioinformaticsPreprint
DModE: An end-to-end framework for Differential Modification and Expression Analysis of Nanopore direct RNA sequencing data
Johannes MiedemaiD, Stefan Pastore, Luca Drescher, Alexander Haehnel, Anna WierczeikoiD, Nicolò AlagnaiD, Lioba Lehmann, Mark HelmiD, Tamer ButtoiD, Susanne GerberiD
Abstract
SummaryNanopore direct RNA sequencing (DRS) enables simultaneous quantification of transcript abundance and RNA modifications from native RNA molecules, providing a unique opportunity to study transcriptional and epitranscriptomic regulation within a single experiment. However, comprehensive analysis of DRS data remains challenging, as existing workflows typically focus on individual processing steps and often require manual integration of multiple software packages for expression analysis, modification detection, statistical testing, and visualization. Furthermore, integrated differential expression and differential RNA modification analysis at both gene and isoform resolution remains poorly supported by current workflows. Here, we present DModE (Differential Modification and Expression Analysis), an end-to-end framework for integrated analysis of Nanopore DRS data. DModE combines an Epi2ME-compatible Nextflow preprocessing workflow with a dedicated Python package for downstream statistical analysis, visualization, and reporting. The framework supports differential gene and isoform expression analysis, differential RNA modification analysis at genome and transcript level, metagene profiling, exploratory epitranscriptomic analyses, and integrated assessment of relationships between expression and modification dynamics. Results are automatically summarized in interactive HTML reports, facilitating reproducible and accessible data interpretation. By integrating transcriptomic and epitranscriptomic analyses within a single framework, DModE substantially simplifies comprehensive DRS data analysis and lowers the barrier for studying RNA modification biology using Nanopore sequencing. Availability and implementationThe DModE Preprocessing pipeline is available on GitHub (https://github.com/johannesmiedema/DModE-preprocessing). The Python package can be installed via pip and is also available on GitHub (https://github.com/johannesmiedema/dmode).
- assay: long-read sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
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