Biology subjects

Butto, T.

Publications and source records attributed to Butto, T..

7 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

Redefining human pre-rRNA processing at single nucleotide resolution using long read Nanopore sequencing

Ribosome biogenesis requires the synthesis and processing of precursor rRNAs (pre-rRNAs) into mature rRNAs. Traditional methods like northern blotting and metabolic labeling offer limited resolution. We present NanoRibolyzer, a nanopore-based, long-read sequencing approach that enables ab initio identification and quantification of rRNA precursors. Using both supervised and unsupervised mapping, it detects known and novel pre-rRNA species and defines cleavage sites at single-nucleotide resolution. A simple cell fractionation protocol provides spatial separation of nuclear and cytoplasmic pre-rRNAs. Targeted knockdowns quantify intermediate accumulations, revealing condition-specific processing fingerprints with biomarker potential. Pseudouridine mapping shows that the primary 47S transcript is extensively modified, while aberrant products are not. With its high resolution and unique mapping strategy, NanoRibolyzer offers new insights into rRNA processing and modification, enhancing our understanding of ribosome biogenesis.

cell biology↗

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

Yeast elongation factor homolog New1 protects a subset of mRNAs from degradation by no-go decay

New1 is a homologue of the essential yeast translation elongation factor eEF3. Lack of New1 has previously been shown to induce queueing of ribosomes upstream of the stop codon on mRNAs encoding specific C-terminal amino acids, primarily lysine and arginine. Here, we used UV crosslinking and analysis of cDNA, long-read nanopore sequencing and proteomics to address the open question of what consequences such queues have for the yeast cell. We show that these queues are ribosomal collisions, which are recognized by the collision sensor and E3 ubiquitin ligase Hel2, marking these collided ribosome complexes for mRNA degradation via canonical no-go decay. Decay is initiated by Cue2-mediated cleavage upstream of the stop codon. Ultimately, this leads to downregulation of encoded proteins, including highly abundant and important metabolic enzymes Pgk1 and Gpm1, as well as translation elongation factors eEF1-alpha and eEF1-beta. Collisions and resulting downstream effects are codon-, rather than amino acid dependent. E.g., for C-terminal lysine and arginine, only specific codons induce collisions upon lack of New1. Our study shows that New1 protects highly abundant and essential genes from degradation by no-go decay thatwould otherwise occur in the absence of translation inhibitors or other direct perturbations of translation.

molecular biology↗

bioRxiv2024

Real-time transcriptomic profiling in distinct experimental conditions

Nanopore technology offers real-time sequencing opportunities, providing rapid access to sequenced data and allowing researchers to manage the sequencing process efficiently, resulting in cost-effective strategies. Here, we present focused case studies demonstrating the versatility of real-time transcriptomics analysis in rapid quality control for long-read RNA-seq. We illustrate its utility through four experimental setups: 1) transcriptome profiling of distinct human cellular populations, 2) identification of experimentally enriched transcripts, 3) transcriptional analysis of cells under heat shock conditions and 4) identification of experimentally manipulated genes (knockout and overexpression) in several yeast strains. We show how to perform multiple layers of quality control as soon as sequencing has started, addressing both the quality of the experimental and sequencing traits. Real-time quality control measures assess sample/condition variability and determine the number of identified genes per sample/condition. Furthermore, real-time differential gene/transcript expression analysis can be conducted at various time points post-sequencing initiation (PSI), revealing dynamic changes in gene/transcript expression between two conditions. Using real-time analysis, which occurs in parallel to the sequencing run, we identified differentially expressed genes/transcripts as early as 1hr PSI. These changes were consistently observed throughout the entire sequencing process. We discuss the new possibilities offered by real-time data analysis, which have the potential to serve as a valuable tool for rapid and cost-effective quality checks in specific experimental settings and can be potentially integrated into clinical applications in the future.

bioinformatics↗

bioRxiv2022

NanopoReaTA: a user-friendly tool for nanopore-seq real-time transcriptional analysis

SummaryOxford Nanopore Technologies (ONT) sequencing platform offers an excellent opportunity to perform real-time analysis during sequencing. This feature allows for early insights into experimental data and accelerates a potential decision-making process for further analysis, which can be particularly relevant in the clinical context. Although some tools for the real-time analysis of DNA-sequencing data already exist, there is currently no application available for differential transcriptome data analysis designed for scientists or physicians with limited bioinformatics knowledge. Here we introduce NanopoReaTA, a user-friendly real-time analysis toolbox for RNA sequencing data from ONT. Sequencing results from a running or finished experiment are processed through an R Shiny-based graphical user interface (GUI) with an integrated Nextflow pipeline for whole transcriptome or gene-specific analyses. NanopoReaTA provides visual snapshots of a sequencing run in progress, thus enabling interactive sequencing and rapid decision-making that could also be applied to clinical cases. Availabilityhttps://github.com/AnWiercze/NanopoReaTA Contactbuttamer@uni-mainz.de and sugerber@uni-mainz.de

bioinformatics↗

bioRxiv2020

mTOR inhibition in primary neurons and the developing brain represses transcription of cholesterol biosynthesis genes and alters cholesterol levels

Dysregulated mammalian target of rapamycin (mTOR) activity is associated with various neurodevelopmental disorders ranging from idiopathic autism spectrum disorders to syndromes caused by single gene defects. This suggests that maintaining mTOR activity levels in a physiological range is essential for brain development and functioning. Upon activation, mTOR regulates a variety of cellular processes such as cell growth, autophagy and metabolism. On a molecular level, however, the consequences of mTOR activation in the brain are not well understood. Low levels of cholesterol are associated with a wide variety of neurodevelopmental disorders. We here describe numerous genes of the sterol/cholesterol biosynthesis pathway to be transcriptionally regulated by mTOR complex 1 (mTORC1) signaling in vitro in primary neurons and in vivo in the developing cerebral cortex of the mouse. We find that these genes are shared targets of the transcription factors SREBP, SP1 and NF-Y. Prenatal as well as postnatal mTORC1 inhibition downregulated expression of these genes which directly translated into reduced cholesterol levels pointing towards a substantial metabolic function of the mTORC1 signaling cascade. Altogether, our results indicate that mTORC1 is an essential transcriptional regulator of the expression of sterol/cholesterol biosynthesis genes in the developing brain. Altered expression of these genes may be an important factor contributing to the pathogenesis of neurodevelopmental disorders associated with dysregulated mTOR signaling.

neuroscience↗