Biology subjects

Vingron, M.

Publications and source records attributed to Vingron, M..

At least 19 recordsLinked to original sources

bioRxiv2026

A draft atlas of the human plasma proteome at isoform resolution

Key plasma protein, including albumin, complement system proteins, and coagulation factors circulate in biochemically and functionally distinct isoforms, or proteoforms. Despite the importance of these proteoforms being widely accepted, they are challenging to resolve systematically, and as a consequence, we still lack a more complete picture of the total proteoform diversity of the human plasma proteome. Here, we generated 3,114 intact protein fractions by extensively separating plasma according to isoelectric point and molecular weight. We then analysed these fractions by liquid chromatography data-independent acquisition mass spectrometry (DIA-MS) upon tryptic digestions, and combined the resultant data into a multidimensional, proteoform resolved atlas of the human plasma proteome. We report that 5,077 unequivocally detected plasma proteins circulate at least 179,049 distinct proteoforms, of which we detect 44,083, with a median of 11 proteoforms per protein, at high confidence. Substantially increasing the number of experimentally confirmed proteoforms, our atlas implies that the canonical protein forms account for less than half of the total protein mass in plasma. We detect the greatest proteoform diversity among the most abundant plasma proteins and those functioning in nutrient transport, complement and coagulation. Finally, our data clarifies discrepancies between the Olink and SomaScan proteomic platforms, as according to our atlas, these techniques provide correlated values for relatively abundant proteins with low to medium proteoform diversity, but struggle in the quantification of low-abundant proteins and on those which exist in many proteoforms. In sum, our data reveals extensive proteoform diversity of the human plasma proteome, and implies that proteoform diversity represents an enormous untapped reservoir for biomedical research and biomarker discovery.

biochemistry↗

bioRxiv2026

TandemTwister: Scalable genotyping and advanced visualization of tandem repeats

Tandem repeats are genomic regions consisting of consecutively repeated units with variable copy numbers and possible mutations. They are used in DNA fingerprinting and have been implicated in complex traits and genetic disorders, including neurodegenerative and developmental diseases. The vast and expanding number of tandem repeat loci in the human genome underscores the need for fast and scalable tools for accurate genotyping and visualization. An accurate tool for characterizing these variants is essential for understanding their functional impacts and associations with phenotypes. We developed TandemTwister, a novel algorithm implemented in C++, as a highly scalable and parallelized tool for tandem repeat copy number genotyping. Additionally, we created an interactive visualization tool to facilitate quick manual inspection, displaying exact motif occurrences, counts, and population information across haplotypes. TandemTwister demonstrates high accuracy and runtime efficiency for tandem repeat genotyping across all long-read sequencing technologies and assembled genomes. We evaluated the performance of TandemTwister in Ashkenazim trio on different sequencing technologies on a set of 1.2 million annotated tandem repeat regions. TandemTwister was the fastest and most accurate genotyping tool available for tandem repeats in comparison to the state of the art tools. For PacBio Hifi data as an example, TandemTwister was run in 17 minutes on 32 CPU cores resulting in 99.4% recall, 98.0% Mendelian consistency, and 94% sequence accuracy. We also showed a successful super-population clustering and examined inheritance patterns of tandem repeats and haplotype blocks in three trio sets. TandemTwister demonstrated its ability to detect pathogenic repeat expansions. We applied it in a cohort of 31 individuals with neurodegenerative and developmental disorders, successfully distinguishing healthy from pathogenic copy numbers.

genomics↗

bioRxiv2025

Context-aware sequence-to-activity model of human gene regulation

Sequence-to-function models have been very successful in predicting gene expression, chromatin accessibility, and epigenetic marks from DNA sequences alone. However, current state-of-the-art models have a fundamental limitation: they cannot extrapolate beyond the cell types and conditions included in their training dataset. Here, we introduce a new approach that is designed to overcome this limitation: Corgi, a new context-aware sequence-to-function model that accurately predicts genome-wide gene expression and epigenetic signals, even in previously unseen cell types. We designed an architecture that strives to emulate the cell: Corgi integrates DNA sequence and trans-regulator expression to predict the coverage of multiple assays including chromatin accessibility, histone modifications, and gene expression. We define trans-regulators as transcription factors, histone modifiers, transcriptional coactivators, and RNA binding proteins, which directly modulate chromatin states, gene expression, and mRNA decay. Trained on a diverse set of bulk and single cell human datasets, Corgi has robust predictive performance, approaching experimental-level accuracy in gene expression predictions in previously unseen cell types, while also setting a new state-of-the-art level for joint cross-sequence and cross-cell type epigenetic track prediction. Corgi can be used in practice to impute context-specific assays such as DNA accessibility and histone ChIP-seq, using only RNA-seq data.

genomics↗

bioRxiv2025

Accurate detection of pathogenic structural variants guided by multi-platform comparison

Structural variants (SVs) are a common cause of human diseases and greatly contribute to inter-individual variability. Their detection represents a significant challenge due to their diversity, size, and enrichment in repetitive regions. With the use of high quality long-read technologies, the majority of these challenges can now be mitigated. However, many downstream applications ranging from clinical diagnostics and genome-wide association studies to large-scale aggregation of population variants continue to rely on short-read-sequencing data due to its high through-put and cost-effectiveness. Thus, the challenges of short-read sequencing SV detection remain a constant and relevant obstacle. We created dicast, a machine-learning method to improve the identification of true-positive SVs from short-read sequencing using genomic context and alignment features. Dicast is driven by a novel and comprehensive benchmark call-set created through the combination of several sequencing technologies and rigorous manual curation. This benchmark set served as the basis for a systematic evaluation of five sequencing platforms and fifteen SV detection methods across different SV classes, sizes, and genomic contexts, enabling us to quantify the strengths and weaknesses of each and inform the training of our model. Leveraging these insights, dicast outperforms state-of-the-art short-read-based callers and consensus approaches, identifying considerably more true-positive variants while maintaining a high precision. We also demonstrate the methods applicability in diagnostic scenarios using putative pathogenic candidates in a limb malformation cohort, as well as known pathogenic variants in atrial fibrillation and neuromuscular disease cohorts. Dicast identifies all known pathogenic variants and 20% more manually confirmed candidate deletions than a consensus approach, and therefore can be used to reliably reduce time-consuming manual inspection in diagnostics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/655285v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1c1702aorg.highwire.dtl.DTLVardef@aaecddorg.highwire.dtl.DTLVardef@1a4de08org.highwire.dtl.DTLVardef@ed0811_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

CAdir: Fast Clustering and Visualization of Single-Cell Transcriptomics Data by Direction in CA Space

Clustering for single-cell RNA-seq aims at finding similar cells and grouping them into biologically meaningful clusters. Many available clustering algorithms however do not not provide the cluster defining marker genes or are unable to infer the number of clusters in an unsupervised manner as well as lack tools to easily determine the quality of the label assignments. Therefore, clustering quality is commonly evaluated by visually inspecting low-dimensional embeddings as produced by e.g. UMAP or t-SNE. These embeddings can, however, distort the true cluster structure and are known to produce radically different embeddings depending on the chosen hyperparameters. Determining clustering quality therefore still heavily relies on domain knowledge to assess if cells should be clustered together. In order to improve the interpretability of clustering results, we developed CAdir (https://github.com/VingronLab/CAdir), a clustering algorithm that can infer the number of clusters in the data, determine cluster specific genes and provides easy to interpret diagnostic plots. CAdir exploits the geometry induced by correspondence analysis (CA) to cluster cells as well as cluster associated genes based on their direction in CA space. Using the angle between the cluster directions, it is able to automatically infer the number of clusters in the data by merging and splitting clusters. A comprehensive set of diagnostic and explanatory plots provides users with valuable feedback about the clustering decisions and the quality of the final as well as intermediary clusters. CAdir is scalable to even the largest data set and provides similar clustering performance to other state-of-the-art cell clustering algorithms in our benchmarking. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/643234v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@bcabd5org.highwire.dtl.DTLVardef@b53ec4org.highwire.dtl.DTLVardef@12bbe6dorg.highwire.dtl.DTLVardef@e31d13_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Transfer learning and DNA language models enhance transcription factor binding predictions

Identification of in vivo transcription factor (TF) binding sites is crucial to understand gene regulatory networks, but the lack of scalability in the methods for their experimental identification directs researchers towards computational models. TF binding site prediction models are often specific for a given TF, which also hinders the generalizability of models to previously unseen TFs. Here, we present an approach to predict in vivo TF binding sites using DNA accessibility, TF RNA expression and TF binding motifs. Our novel method leverages DNA language model embeddings and transfer learning to improve its accuracy and generalizability, achieving a mean area under the precision-recall curve (AUPR) of 0.51 in held-out cell types and chromosomes in the ENCODE-DREAM in vivo TFBS prediction challenge, outperforming the top-ranked methods. Furthermore, we show that prediction accuracy increases when TFs are highly active and exhibit cell-type specific expression. We finally test our models in an independent dataset on previously unseen TFs, and report a mean AUPR of 0.36, which is state-of-the-art in a cross-TF, cross-cell type and cross-chromosomal setting.

bioinformatics↗

bioRxiv2024

Comparative single-cell analyses reveal evolutionary repurposing of a conserved gene program in bat wing development

Bats are the only mammals capable of self-powered flight, an evolutionary innovation based on the transformation of forelimbs into wings. The bat wing is characterized by an extreme elongation of the second to fifth digits and a wing membrane called chiropatagium connecting them. Here we investigated the developmental and cellular origin of this structure by comparing bat and mouse limbs using omics tools and single-cell analyses. Despite the substantial morphological differences between the species, we observed an overall conservation of cell populations and gene expression patterns including interdigital apoptosis. Single-cell analyses of micro-dissected embryonic chiropatagium identified a specific fibroblast population, independent of apoptosis-associated interdigital cells, as the origin of this tissue. These distal cells express a conserved gene program including the transcription factors MEIS2 and TBX3, which are commonly known to specify and pattern the early proximal limb. Transgenic ectopic expression of MEIS2 and TBX3 in mouse distal limb cells resulted in the activation of genes expressed during wing development and phenotypic changes related to wing morphology, such as the fusion of digits. Our results elucidate fundamental molecular mechanisms of bat wing development and illustrate how drastic morphological changes can be achieved through repurposing of existing developmental programs during evolution.

evolutionary biology↗

bioRxiv2024

Reporter CRISPR screens decipher cis- and trans-regulatory principles at the Xist locus

Developmental genes are controlled by an ensemble of cis-acting regulatory elements (REs), which in turn respond to multiple trans-acting transcription factors (TFs). Understanding how a cis-regulatory landscape integrates information from many dynamically expressed TFs has remained a challenge. We develop a combined CRISPR-screening approach using endogenous RNA and RE-reporters as readouts. Applied to the Xist locus, crucial for X-chromosome inactivation in females, this method allows us to comprehensively identify Xist-controlling TFs and map their TF-RE wiring. We find a group of transiently expressed TFs that regulate proximal REs, driving the binary activation of Xist expression. These basal activators are more highly expressed in cells with two X chromosomes, potentially driving female-specific Xist upregulation. A second set of developmental TFs is upregulated later during differentiation and targets distal REs. This regulatory axis is crucial to achieve high levels of Xist RNA, which is necessary for X-chromosome inactivation. Our findings support a model for developmental gene regulation in which factors targeting proximal REs drive binary ON-OFF decisions, while factors interacting with distal REs control the transcription output.

genetics↗

bioRxiv2024

Conservation of Regulatory Elements with Highly Diverged Sequences Across Large Evolutionary Distances

Embryonic gene expression is remarkably conserved across vertebrates as observed, for instance, in the developing hearts of chicken and mouse which diverged >300 million years ago. However, most cis regulatory elements (CREs) are highly divergent, which makes orthology tracing based on sequence similarity difficult, especially at larger evolutionary distances. Some evidence suggests functional conservation of CREs despite sequence divergence. However, it remains unclear how widespread such functional conservation might be. Here, we address this question by profiling the regulatory genome in the embryonic hearts of chicken and mouse at equivalent developmental stages. Gene expression and 3D chromatin structure show remarkable similiarity, while the majority of CREs are non-alignable between the two species. To identify orthologous CREs independent of sequence alignability, we introduce a synteny-based strategy called Interspecies Point Projection (IPP). Compared to alignment-based approaches, IPP identifies up to 5-fold more putative orthologs in chicken, and up to 9-fold across distantly related vertebrates. We term these sequence-diverged orthologs indirectly conserved and characterize their functional conservation compared to sequence-alignable, directly conserved CREs. Indirectly and directly conserved elements show similar enrichment of functional chromatin signatures and cell-type specific enhancer sequence composition. Yet, shared transcription factor binding sites between orthologs are more heavily rearranged in indirectly conserved elements. Finally, we validate functional conservation of indirectly conserved chicken enhancers in mouse using in vivo reporter assays. Taken together, by overcoming the limitations of alignment-based methods our results reveal functional conservation of CREs across large evolutionary distances is more widespread than previously recognized.

genetics↗

bioRxiv2024

Hepatocyte differentiation requires anisotropic expansion of bile canaliculi

During liver development, bipotential progenitor cells called hepatoblasts differentiate into hepatocytes or cholangiocytes. Hepatocyte differentiation is uniquely associated with multi-axial polarity, enabling the anisotropic expansion of apical lumina between adjacent cells and formation of a three-dimensional network of bile canaliculi (BC). Cholangiocytes, the cells forming the bile ducts, exhibit the vectorial polarity common to epithelial cells. Whether and how cell polarization feeds back on the gene regulatory pathways governing hepatoblast differentiation is unknown. Here, we used primary hepatoblasts to investigate the contribution of anisotropic apical expansion to hepatocyte differentiation. Silencing of the small GTPase Rab35 caused isotropic lumen expansion and formation of multicellular cysts with the vectorial polarity of cholangiocytes. Gene expression profiling revealed that these cells express reduced levels of hepatocyte markers and upregulate genes associated with cholangiocyte identity. Time-course RNA sequencing demonstrated that loss of lumen anisotropy precedes these transcriptional changes. Independent alterations in apical lumen morphology induced either by modulation of the subapical actomyosin cortex or increased intraluminal pressure caused similar transcriptional changes. These findings suggest that cell polarity and lumen morphogenesis feedback to hepatoblast-to-hepatocyte differentiation. Summary statementDifferentiation of liver progenitors to functional hepatocytes requires anisotropic elongation of their nascent apical surfaces into tubular bile canaliculi.

developmental biology↗

bioRxiv2024

scATAcat: Cell-type annotation for scATAC-seq data

Cells whose accessibility landscape has been profiled with scATAC-seq cannot readily be annotated to a particular cell type. In fact, annotating cell-types in scATAC-seq data is a challenging task since, unlike in scRNA-seq data, we lack knowledge of "marker regions" which could be used for cell-type annotation. Current annotation methods typically translate accessibility to expression space and rely on gene expression patterns. We propose a novel approach, scATAcat, that leverages characterized bulk ATAC-seq data as prototypes to annotate scATAC-seq data. To mitigate the inherent sparsity of single-cell data, we aggregate cells that belong to the same cluster and create pseudobulk. To demonstrate the feasibility of our approach we collected a number of datasets with respective annotations to quantify the results and evaluate performance for scATAcat. scATAcat is available as a python package at https://github.com/aybugealtay/scATAcat.

bioinformatics↗

bioRxiv2023

CENTRE: A gradient boosting algorithm for Cell-type-specific ENhancer-Target pREdiction

MotivationIdentifying target promoters of active enhancers is a crucial step for realizing gene regulation and deciphering phenotypes and diseases. Up to now, several computational methods were developed to predict enhancer gene interactions but they require either many epigenomic and transcriptomic experimental assays to generate cell-type-specific predictions or a single experiment applied to a large cohort of cell types to extract correlations between activities of regulatory elements. Thus, inferring cell-type-specific enhancer gene interactions in unstudied or poorly annotated cell types becomes a laborious and costly task. ResultsHere, we aim to infer cell-type-specific enhancer target interactions, using minimal experimental input. We introduce CENTRE, a machine learning framework that predicts enhancer target interactions in a cell-type-specific manner, using only gene expression and ChIP-seq data for three histone modifications for the cell type of interest. CENTRE exploits the wealth of available datasets and extracts cell-type agnostic statistics to complement the cell-type specific information. CENTRE is thoroughly tested across many datasets and cell types and achieves equivalent or superior performance than existing algorithms that require massive experimental data. AvailabilityCENTREs open source code is available at GitHub via https://github.com/slrvv/CENTRE

bioinformatics↗

bioRxiv2022

CAbiNet: Joint visualization of cells and genes based on a gene-cell graph

In routine single-cell RNA-sequencing (scRNA-seq) analysis workflows, cells are commonly visualized in 2D to show the patterns in the data. However, these visualization approaches do not give any information about the genes that define the cell groups or clusters. It is therefore desirable to display cells and genes simultaneously such that by their relative position to each other information about the genes expression in a cluster can be obtained. Here we propose "Correspondence Analysis based Biclustering on Networks" (CAbiNet) as a novel approach to jointly visualize cells and genes by a non-linear embedding approach, called biMAP. The biMAP allows for easy and interactive exploration of cells and their corresponding marker genes in a single plot. CabiNet additionally offers an intuitive way to perform biclustering jointly on cells and genes, providing a simplified workflow to annotate cell types on the biMAP. CAbiNet is accessible through GitHub as an R package.

bioinformatics↗

bioRxiv2022

Co-option of the transcription factor SALL1 in mole ovotestis formation

Changes in gene expression represent an important source for phenotypical innovation. Yet, how such changes emerge and impact the evolution of traits remains elusive. Here, we explore the molecular mechanisms associated with the development of masculinizing ovotestes in female moles. By performing comparative analyses of epigenetic and transcriptional data in mole and mouse, we identified SALL1 as a co-opted gene for the formation of testicular tissue in mole ovotestes. Chromosome conformation capture analyses highlight a striking conservation of the 3D organization at the SALL1 locus, but a prominent evolutionary turnover of enhancer elements. Interspecies reporter assays support the capability of mole-specific enhancers to activate transcription in urogenital tissues. Through overexpression experiments in transgenic mice, we further demonstrate the capability of SALL1 to induce the ectopic gene expression programs that are a signature of mole ovotestes. Our results highlight the co-option of gene expression, through changes in enhancer activity, as a prominent mechanism for the evolution of traits.

evolutionary biology↗

bioRxiv2022

Horizontal transferred T-DNA and haplotype-based phylogenetic analysis uncovers the origin of sweetpotato

The hexaploid sweetpotato is one of the most important root crops worldwide. However, its genetic origins are controversial. In this study, we identified two progenitors of sweetpotato by horizontal gene transferred IbT-DNA and haplotype-based phylogenetic analysis. The diploid progenitor is the diploid form of I. aequatoriensis, contributed the B1 subgenome, IbT-DNA2 and lineage 2 type of chloroplast genome to sweetpotato. The tetraploid progenitor of sweetpotato is I. batatas 4x, donating the B2 subgenome, IbT-DNA1 and lineage 1 type of chloroplast genome. Sweetpotato derived from the reciprocal cross between the diploid and tetraploid progenitors and a subsequent whole genome duplication. We also detected biased gene exchanges between subgenomes. The B1 to B2 subgenome conversions were almost 3-fold higher than the B2 to B1 subgenome conversions. This study sheds lights on the evolution of sweetpotato and paves a way for the improvement of sweetpotato.

evolutionary biology↗

bioRxiv2022

Hierarchical Interleaved Bloom Filter: Enabling ultrafast, approximate sequence queries

Searching sequences in large, distributed databases is the most widely used bioinformatics analysis done. This basic task is in dire need for solutions that deal with the exponential growth of sequence repositories and perform approximate queries very fast. In this paper, we present a novel data structure: the Hierarchical Interleaved Bloom Filter (HIBF). It is extremely fast and space efficient, yet so general that it has the potential to serve as the underlying engine for many applications. We show that the HIBF is superior in build time, index size and search time while achieving a comparable or better accuracy compared to other state-of-the art tools (Mantis and Bifrost). The HIBF builds an index up to 211 times faster, using up to 14 times less space and can answer approximate membership queries faster by a factor of up to 129. This can be considered a quantum leap that opens the door to indexing complete sequence archives like the European Nucleotide Archive or even larger metagenomics data sets.

bioinformatics↗

bioRxiv2022

Connecting the Dots: PHF13 and cohesin promote polymer-polymer phase separation of chromatin into chromosomes

How interphase chromatin compacts into mitotic chromosomes has eluded researchers for over a century. Here we show that PHF13, a tightly regulated H3K4me epigenetic reader, and cohesin, a mediator of chromatin architecture, cooperate to drive polymer-polymer-phase-separation (PPPS) and higher order compaction of chromatin into chromosomes. PHF13 interacts with cohesin, shows similar dynamics during mitosis, and their co-depletion dramatically impairs mitotic condensation. Mechanistically, we demonstrate that PHF13 stabilizes cohesin chromatin interactions and that itself oligomerizes, resulting in a polymer with increased chromatin avidity and the ability to bridge neighboring and distant nucleosomes. Consistently, molecular dynamic simulations modelling the ability of PHF13 and cohesin to drive chromatin phase separations recapitulated our in vivo observations and are in line with 2-step condensation models.

cell biology↗

bioRxiv2021

Promoter repression and 3D-restructuring resolves divergent developmental gene expression in TADs

Cohesin loop extrusion facilitates precise gene expression by continuously driving promoters to sample all enhancers located within the same topologically-associated domain (TAD). However, many TADs contain multiple genes with divergent expression patterns, thereby indicating additional forces further refine how enhancer activities are utilised. Here, we unravel the mechanisms enabling a new gene, Rex1, to emerge with divergent expression within the ancient Fat1 TAD in placental mammals. We show that such divergent expression is not determined by a strict enhancer-promoter compatibility code, intra-TAD position or nuclear envelope-attachment. Instead, TAD-restructuring in embryonic stem cells (ESCs) separates Rex1 and Fat1 with distinct proximal enhancers that independently drive their expression. By contrast, in later embryonic tissues, DNA methylation renders the inactive Rex1 promoter profoundly unresponsive to Fat1 enhancers within the intact TAD. Combined, these features adapted an ancient regulatory landscape during evolution to support two entirely independent Rex1 and Fat1 expression programs. Thus, rather than operating only as rigid blocks of co-regulated genes, TAD-regulatory landscapes can orchestrate complex divergent expression patterns in evolution. HIGHLIGHTSO_LINew genes can emerge in evolution without taking on the expression pattern of their surrounding pre-existing TAD. C_LIO_LICompartmentalisation can restructure seemingly evolutionarily stable TADs to control a promoters access to enhancers. C_LIO_LILamina-associated domains neither prevent transcriptional activation nor enhancer-promoter communication. C_LIO_LIRepression rather than promoter-specificity refines when genes respond to promiscuous enhancer activities in specific tissues. C_LI

genetics↗