Biology subjects

Miedema, J.

Publications and source records attributed to Miedema, J..

3 recordsLinked to original sources

bioRxiv2026

DModE: An end-to-end framework for Differential Modification and Expression Analysis of Nanopore direct RNA sequencing data

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).

bioinformatics↗

bioRxiv2025

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing

RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of epitranscriptomes and RNA modification.

bioinformatics↗

bioRxiv2024

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

bioinformatics↗