Biology subjects

Cleynen, A.

Publications and source records attributed to Cleynen, A..

8 recordsLinked to original sources

bioRxiv2026

T2G: fast and accurate transcriptome to genome BAM liftover for short and long read RNA sequencing

Aligning RNA sequencing reads to the transcriptome rather than the genome recovers short and junction-spanning reads that genome alignment loses or misplaces, but it leaves the alignments in transcript coordinates, which genome browsers, variant callers and most annotation tools cannot use. Existing liftover tools translate between genome assemblies, and the few that reach transcript coordinates are tied to a single quantification model or assay type. Here we describe T2G, which converts complete BAM files from transcriptome to genome coordinates while keeping CIGAR strings, alignment tags and per-base RNA modification calls intact. Across nanopore direct RNA sequencing, ribosome footprinting and paired-end Illumina RNA sequencing, aligning to the transcriptome and lifting with T2G gave higher on-target specificity and more accurate splice junctions than direct genome alignment, removed the systematic assignment of reads to processed pseudogenes, and preserved isoform identity, revealing isoform-specific modification levels that genome alignment pools together. In a reanalysis of a published RNA-binding protein experiment, T2G reduced the number of reads attributed to pseudogenes 8.5-fold compared with unspliced genome alignment. T2G is available at https://github.com/Arnaroo/T2GD as a Python reference implementation and as t2gd, a dependency-free binary for Linux, macOS and Windows that also provides a gene-level quantifier and BAM utilities toolkit.

bioinformatics↗

bioRxiv2026

Multi-omic dissection of RNA control reveals convergent cis-regulatory programs during glucose starvation in yeast

Cells reprogram gene expression at multiple, often independently studied, regulatory layers during nutrient stress. Here we use direct RNA sequencing of polysome-fractionated transcripts that simultaneously measures transcript abundance, mRNA degradation, poly(A) tail length, and three RNA modifications (m5C, m6A, pseudouridine) to build an integrated, multi-omic view of the Saccharomyces cerevisiae response to acute glucose starvation. Lasso-penalised mixture-of-regressions clustering of stochastic translation efficiency ({delta}STE) against this combined feature set partitions 5,033 transcripts into five translational programs with distinct functional identities, from a strongly upregulated translation-machinery cluster to a strongly repressed glycosylation cluster. CDS sequence composition discriminates these extremes, while 5' and 3'UTR composition contributes only marginal, largely non-significant effects after length correction. Extending this analysis, we show that the repressed glycosylation program is distinguished by a convergent stack of independent cis-regulatory signatures: weak Kozak initiation context, high upstream open reading frame burden, excess RNA secondary structure specifically over the start codon, and depletion for the targets of major stabilising and export-associated RNA-binding proteins. In other programmes, translation efficiency tunes with initiation-context strength indicating that downstream regulatory layers coregulate translation. These results provide an integrated, feature-resolved map of post-transcriptional regulation during acute nutrient stress.

genomics↗

bioRxiv2026

A refined Saccharomyces cerevisiae reference transcriptome from Direct RNA Sequencing, with a reusable pipeline for UTR annotation updates

Untranslated regions (UTRs) flanking the coding sequence govern mRNA translation, localisation, stability, and decay, making accurate UTR boundaries essential for quantitative RNA sequencing and the study of post-transcriptional control in Saccharomyces cerevisiae and beyond. Reference transcriptomes built from short-read sequencing have been invaluable to the yeast community, yet in a genome as gene-dense as that of S. cerevisiae, short reads frequently cannot be assigned to a single transcript of origin, leaving roughly one quarter of transcripts without a confidently defined UTR. Here we use Oxford Nanopore Direct RNA Sequencing (DRS), in which each full-length polyadenylated molecule is read end to end, to resolve this ambiguity and deliver two complementary resources. First, an updated, ready-to-use S. cerevisiae S288C reference: change-point segmentation of per-gene DRS coverage defined boundaries for 5,416 of the 6,695 annotated genes, and a merge-max rule retaining the longer UTR from each source ensures no gene loses existing annotation. The result adds previously absent UTRs to 927 (5') and 896 (3') genes and extends 29.4% of 5' and 26.1% of 3' boundaries among comparable genes. Second, the complete, documented pipeline so that any laboratory can rebuild or update a transcriptome from its own DRS data. Validation on two independent datasets shows improved mapping rates, reduced soft-clipping, and metagene profiles consistent with genuine transcript signal.

genomics↗

bioRxiv2026

VX: an AI-enabled desktop genome viewer and transcriptome browser with a programmable analysis framework

BackgsroundGenome and transcriptome browsers are central to the interpretation of high-throughput sequencing data, but todays tools assume a human operator at a graphical interface and offer only limited programmability. As large-language-model assistants become routine in bioinformatics [Anthropic, 2024], this creates a bottleneck: agents cannot observe the visual state of the browser or drive it through the same interface as the human user, and analyses remain fragmented across a separate ecosystem of external tools. Transcript-coordinate data, produced by ribosome profiling [Ingolia et al., 2012] and direct RNA sequencing [Garalde et al., 2018], is also awkwardly supported in chromosome-oriented viewers. ResultsWe present VX, a desktop genome and transcriptome viewer written in D, using GTK 3 and OpenGL, that handles genome-scale and transcriptome-scale data in a unified interface. VX exposes its full functionality through an embedded HTTP API on the loopback interface and a Model Context Protocol server of currently thirty-nine tools, so that scripts and LLM agents can load data, navigate, manage tracks, run analyses, and capture figures through the same contract used by the GUI. An integrated analysis framework provides more than fifty analyses and includes signal processing and peak calling, quantification, variant analysis, alignment statistics, interaction and cross-track comparisons, all with an explicit four-level scope hierarchy running from viewport to whole dataset; results are written to disk and, where appropriate, added as new tracks. Additional features include a magnifier popup for base-resolution inspection (Alt+hover), chromosome-alias resolution across UCSC, Ensembl, and NCBI conventions, viewport video recording via an ffmpeg pipe, and INI-based configuration. ConclusionsVX complements existing desktop and web browsers by providing a native agent-control layer, an integrated analysis framework, and first-class transcriptspace handling. The binary is freely available for non-commercial use; the HTTP API and MCP protocol are fully specified in this article, so third-party clients can be written independently of the core implementation.

bioinformatics↗

bioRxiv2025

SWARM: A Single-Molecule Workflow for High-Precision Profiling of RNA Modifications

Nanopore direct RNA sequencing promises to decode the epitranscriptome by detecting multiple modifications on individual RNA molecules, but its potential for biological discovery is hampered by high false-positive rates. We present SWARM, an AI-based framework designed to overcome this fundamental limitation. Its key innovation is a crosstalk-aware training strategy that incorporates non-target modifications and orthogonally validated cellular signals, enabling high-precision detection of m6A, pseudouridine ({Psi}), and m5C at single-nucleotide and single-molecule resolution. Using rigorous in vitro and cellular RNA benchmarks, SWARM outperforms existing tools and maintains strong agreement with orthogonal methods. Applying SWARM across mammalian tissues reveals thousands of novel modification sites with confirmed motifs and localisation patterns. Our high-resolution multi-tissue modification map revealed no evidence of widespread m6A-{Psi} interplay in predominant writer contexts, challenging models of a coordinated epitranscriptomic code. We further discovered a previously unrecognised splicing-shaped mode of {Psi} deposition, whereby TRUB1-mediated pseudouridylation preferentially occurs after exon-exon ligation, consistent with local RNA structure stabilisation. SWARM provides a robust, universally applicable tool for epitranscriptome discovery.

bioinformatics↗

bioRxiv2025

FracFixR: A compositional statistical framework for absolute proportion estimation between fractions in RNA sequencing data

MotivationRNA fractionation followed by sequencing is widely used to study RNA localization, translation, and subcellular compartmentalization. Interpreting fractionated RNA-seq data poses a fundamental compositional challenge: library preparation and sequencing depth obscure the original proportions of RNA fractions, which can bias comparisons - particularly when biological changes shift RNA distribution across fractions. This bias compromises comparisons of fraction-specific RNA profiles and limits the utility of standard differential expression methods. Existing approaches using transcript frequency ratios or standard normalization fail to account for the compositional nature of fractionated samples and cannot estimate the unrecoverable "lost" fraction. ResultsWe developed FracFixR, a statistical framework that reconstructs original fraction proportions by modeling the compositional relationship between whole and fractionated RNA samples. Using non-negative linear regression on carefully selected transcripts, FracFixR estimates global fraction weights, corrects individual transcript frequencies, and quantifies unrecoverable material. The framework includes methods for differential proportion testing between conditions using binomial GLM, logit, or beta-binomial models. We rigorously validated FracFixR using synthetic data with known ground truth and real polysome profiling data from multiple cell lines, demonstrating accurate reconstruction of fraction weights (Pearson correlation > 0.85) and enabling detection of differentially translated transcripts between cancer subtypes. Availability and implementationFracFixR is implemented as an R package freely available on GitHub at https://github.com/Arnaroo/FracFixR.

bioinformatics↗

bioRxiv2024

High-Accuracy RNA Integrity Definition for Unbiased Transcriptome Comparisons with INDEGRA

RNA sample integrity variability introduces biases and obscures natural RNA degradation, posing a significant challenge in transcriptomics. To address this, we developed the Direct Transcriptome Integrity (DTI) measure, a universal and robust RNA integrity metric based on nanopore sequencing. By accurately modeling RNA fragmentation, DTI provides a reliable assessment of sample quality. Integrated into the INDEGRA package (freely available at https://github.com/Arnaroo/INDEGRA), we provide tools to correct false discoveries and enable precise differential expression and RNA degradation analyses, even for challenging sample types. INDEGRA software can be used to accurately measure RNA DTI stability metric, isolate biological component of RNA degradation from technical biases, compare biological RNA stability transcriptome-wide and suppress false degradation-induced differential gene expression hits to allow broad comparisons across samples of different quality DTI offers a straightforward and accurate method for assessing RNA degradation, characterizing both overall sample integrity and transcript-specific degradation rates using direct RNA sequencing (DRS) data. Calculated through INDEGRA, DTI reveals inter- and intra-transcript variability in degradation, while INDEGRA separates RNA degradation from mapping inaccuracies, and connects degradation profiles to RNA fragmentation rates. By leveraging INDEGRA, researchers can minimize false differential transcript abundance findings caused by variations in overall sample integrity, while preserving genuine transcript-specific differences in stability and degradation. INDEGRA supports integration with widely used differential transcript abundance tools like DESeq2, limma-voom, and edgeR, enabling seamless analysis pipelines. INDEGRA enhances the accuracy and reliability of RNA quantification in high-throughput data and simplifies comparisons across diverse transcriptomic datasets, including those derived from different tissues, species, or experimental protocols.

bioinformatics↗

bioRxiv2022

Unsupervized identification of prognostic copy-numberalterations using segmentation and lasso regularization

Identifying copy-number alteration with prognostic impact is typically done in a supervised approach, were candidate regions are user-selected (chomosome arms, oncogenes, etc). Yet CNA events may range from whole chromosome alterations to small focal amplifications or deletions, with no available approach to combine the potential prognostic impact of different aberration ranges. We propose and compare different statistical models to integrate the effects of multi-scale CNA events by exploiting the longitudinal structure of the genome, and assume that the survival distribution follows a Cox-proportional hazard model. These methods are adaptable to any cohorts screened for CNA by genome-wide assays such as CGH-array or whole-genome sequencing technologies, and with sufficient follow-up time. We show that combining a segmentation in the survival odds strategy with a lasso-regularization selection approach provides the best results in terms of recovering the true significant CNA regions as well as predicting survival outcomes. In particular, as shown on a 551 Multiple Myeloma patient cohort, this method allows to refine previously identified regions to exhibit potential novel driver genes.

bioinformatics↗