Biology subjects

Liehrmann, A.

Publications and source records attributed to Liehrmann, A..

6 recordsLinked to original sources

bioRxiv2026

phyloDS: phylogeny-aware differential splicing across species, beyond reference annotations

Alternative splicing diverges rapidly between species, yet its comparative analysis lags far behind that of gene expression: junction orthology is defined on a single reference genome, species are treated as independent samples, and detected shifts are rarely grounded in sequence-level evidence. We present phyloDS, an RNA-seq--driven framework that resolves all three limitations. phyloDS defines orthologous splice junctions on multiple sequence alignments of one-to-one orthologous genes, tests their differential usage with a regularized Ornstein--Uhlenbeck model, and cross-validates detected shifts against splice-site predictions made from genomic sequence alone. Applied to primate brain and rodent heart transcriptomes, phyloDS recovered up to 2.9-fold more shared orthologous splice junctions than a reference-based strategy and kept type~I error near nominal where competing methods inflate it more than two-fold. It identified 234 and 502 differentially used orthologous splice junctions in hominoids and mole-rats (86 and 141 unannotated), revealing a mitochondrial and contractile splicing signature in mole-rats.

bioinformatics↗

bioRxiv2026

Interspecies Differential Gene Expression Analysis with Regularized Phylogenetic Linear Models

Comparative transcriptomic datasets are increasingly used to investigate the molecular basis of phenotypic diversification across species. However, finding genes that are differentially expressed (DE) between lineages remains challenging, for two main reasons. First, the random evolutionary drift can blur the signal left by lineage-specific shifts in mean expression, and induces phylogenetic correlations that, if ignored, can widely inflate the False Discovery Rate (FDR), i.e., the amount of spuriously detected genes. Second, DE analysis from RNA-Seq data involves multiple testing on many genes for a small number of individual measurements with high noise, and requires dedicated statistical tools. Traditional DE tools, such as limma, and classical Phylogenetic Comparative Methods (PCMs), such as the Expression Variance and Evolution (EVE) model, are both designed to tackle one of these two challenges alone, but both fail in the context of inter-species RNA-Seq data. In this work, we present phyloDE, a new tool for inter-species DE, that aims at taking the best from both approaches. On simulations based on a recently published four-species rodent dataset, we show that, contrary to other methods, phyloDE correctly controls the FDR in all settings, while keeping a reasonable power. When reanalyzing the empirical dataset, phyloDE discovers more DE genes that exhibit consistent changes in their cis-regulatory landscape compared to EVE in all the experimental settings. The method is implemented in R, with an interface inheriting from limma.

evolutionary biology↗

bioRxiv2025

ProteoCast: a web server to predict, validate, and interpret missense variant effects

Understanding how mutations affect protein function remains critical yet challenging, particularly for variants in clinical databases lacking experimental characterisation and for intrinsically disordered regions. Current computational approaches often operate as black boxes, providing predictions without sufficient transparency or quality assessment of the underlying data. Here we present ProteoCast, a user-friendly web server that predicts variant effects through evolutionary constraint analysis and structural context integration. ProteoCast provides a three-tier variant classification (impactful, mild, neutral) to help prioritise mutations for clinical interpretation and experimental validation. It incorporates multiple sequence alignment quality controls to ensure prediction reliability and flag positions with insufficient evolutionary information. Beyond single-variant classification, ProteoCast employs a novel segmentation approach based on mutational sensitivity to identify functional linear peptides in disordered regions. Interactive visualisations guide users through results interpretation, from variant-level predictions to protein-wide functional landscapes. Evaluation on 63,000 ClinVar variants demonstrates 77% sensitivity and 87% specificity for pathogenicity prediction, with performance maintained across species (85% accuracy on Drosophila lethal mutations). ProteoCast successfully identifies twice as many functional motifs in intrinsically disordered regions compared to conservation-based phylogenetic methods. Predictions can be tuned to specific conformations, such as bound forms in protein complexes, for improved accuracy and interpretability. With its transparent, unsupervised methodology and computational efficiency (minutes per protein), ProteoCast democratises access to variant effect prediction and functional site discovery for the broader research community. The web server is freely available at: https://proteocast.ijm.fr/. HighlightsO_LIProteoCast web server democratises variant effect prediction through a fast, transparent platform combining evolutionary analysis and structural context C_LIO_LIThree-tier variant classification enables prioritisation for clinical interpretation and genome editing experiments C_LIO_LINovel mutational sensitivity segmentation approach identifies functional peptides in disordered regions C_LIO_LIInteractive visualisations and input alignment quality controls guide interpretation C_LI

bioinformatics↗

bioRxiv2025

The GT1 domain of RNase J ensures RNA quality control through dsRNA binding in Arabidopsis plastids

RNase J is a ribonuclease found in bacteria, archaea, and plant chloroplasts, and plays diverse roles in RNA maturation and stability. Chloroplast RNase J is encoded by the nuclear RNJ locus and is essential for embryo maturation. Arabidopsis or tobacco plants depleted for RNase J accumulate massive amounts of double-stranded RNA, which interferes with translation and causes chlorosis. Land plant RNase J uniquely contains a C-terminal GT1 domain, a DNA- binding motif found in transcription factors. Here, we have used complementation of an Arabidopsis rnj mutant with versions of RNase J with a mutated or deleted GT1 domain to investigate its role in RNase J function. We show that in vitro, the recombinant GT1 domain binds both double-stranded RNA and DNA, but not single-stranded nucleic acids, with no sequence specificity. Furthermore, while RNase J lacking GT1 binding complements the rnj mutant, these plants accumulate high levels of dsRNA as detected by immunolocalization and RNA-Seq. GT1 mutations also change RNase J solubility in vivo, suggesting that the GT1 domain is involved in localization within the plastid. Taken together, our results suggest that the GT1 domain plays a key role in dsRNA removal through localizing the enzyme and/or selectively binding the dsRNA substrate. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/682605v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@e5642borg.highwire.dtl.DTLVardef@136456org.highwire.dtl.DTLVardef@1205466org.highwire.dtl.DTLVardef@184f72b_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2025

Proteome-wide prediction of the Functional Impact of Missense Variants with ProteoCast

Dissecting the functional impact of genetic mutations is essential to advancing our understanding of genotype-phenotype relationships and identifying therapeutic targets. Despite progress in sequencing and genome editing technologies, proteome-wide mutation effect prediction remains challenging. Here we show that evolutionary information alone enables accurate prediction of mutation effects across entire proteomes. ProteoCast is a scalable and interpretable computational method that leverages protein sequence conservation to classify genetic variants and identify functionally important protein sites. We apply ProteoCast to the complete Drosophila melanogaster proteome (22,000 isoforms, 300 million mutations) and validate it against nearly 400,000 natural and experimental variants. It correctly classifies 85% of known lethal mutations as functionally impactful versus 13-18% of population variants. ProteoCast-guided genome editing experiments confirm these predictions. Moreover, ProteoCast successfully identifies functionally important protein modification sites and binding motifs. ProteoCast provides a publicly available resource and deployable pipeline for studying gene function and mutations in any organism.

evolutionary biology↗

bioRxiv2023

DiffSegR: An RNA-Seq data driven method for differential expression analysis using changepoint detection

To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3/5)-degradation of rRNA, mRNA, and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html.

bioinformatics↗