Biology subjects

Vingron, M.

Publications and source records attributed to Vingron, M..

35 records · Page 2Linked to original sources

bioRxiv2021

Integrative annotation of genomic features reveal functional elements and epigenetic landscapes in the developing zebrafish

Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.

developmental biology↗

bioRxiv2021

Long-read transcriptome sequencing analysis with IsoTools

Long-read transcriptome sequencing (LRTS) holds the promise to boost our understanding of alternative splicing. Recent advances in accuracy and throughput have diminished the major limitations and enabled the direct quantification of isoforms. Considering the complexity of the data and the broad range of potential applications, it is clear that highly flexible, accurate analysis tools are crucial. Here, we present IsoTools, a comprehensive Python-based analysis package, for the improvement of alternative and differential splicing analysis. Iso-Tools provides a comprehensive data structure that integrates genomic information from LRTS transcripts together with the reference annotation, and enables broad functionality to quality control, visualize and analyze the data. Additionally, we implemented a graph-based method for the identification of alternative splicing events and a statistical approach based on the beta binomial distribution for the detection of differential events. To demonstrate our methods, we generated PacBio Iso-Seq data of human hepatocytes treated with the HDAC inhibitor valproic acid, a compound known to induce widespread transcriptional changes. Contrasted with short read RNA-Seq of the same samples, this analysis shows that LRTS provides valuable additional insights for a better understanding of alternative splicing, in particular with respect to complex novel and differential splicing events. IsoTools is made available for the community along with extensive documentation at https://github.com/MatthiasLienhard/isotools.

genetics↗

bioRxiv2021

Assessing genome-wide dynamic changes in enhancer activity during early mESC differentiation by FAIRE-STARR-seq

Embryonic stem cells (ESCs) can differentiate into any given cell type and therefore represent a versatile model to study the link between gene regulation and differentiation. To quantitatively assess the dynamics of enhancer activity during the early stages of murine ESC differentiation, we analyzed accessible genomic regions using STARR-seq, a massively parallel reporter assay. This resulted in a genome-wide quantitative map of active mESC enhancers, in pluripotency and during the early stages of differentiation. We find that only a minority of accessible regions is active and that such regions are enriched near promoters, characterized by specific chromatin marks, enriched for distinct sequence motifs, and modeling shows that active regions can be predicted from sequence alone. Regions that change their activity upon retinoic acid-induced differentiation are more prevalent at distal intergenic regions when compared to constitutively active enhancers. Further, analysis of differentially active enhancers verified the contribution of individual TF motifs toward activity and inducibility as well as their role in regulating endogenous genes. Notably, the activity of retinoic acid receptor alpha (RAR) occupied regions can either increase or decrease upon the addition of its ligand, retinoic acid, with the direction of the change correlating with spacing and orientation of the RAR consensus motif and the co-occurrence of additional sequence motifs. Together, our genome-wide enhancer activity map elucidates features associated with enhancer activity levels, identifies regulatory regions disregarded by computational prediction tools, and provides a resource for future studies into regulatory elements in mESCs.

genomics↗

bioRxiv2021

Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences

Recent efforts to measure epigenetic marks across a wide variety of different cell types and tissues provide insights into the cell type-specific regulatory landscape. We use this data to study if there exists a correlate of epigenetic signals in the DNA sequence of enhancers and explore with computational methods to what degree such sequence patterns can be used to predict cell type-specific regulatory activity. By constructing classifiers that predict in which tissues enhancers are active, we are able to identify sequence features that might be recognized by the cell in order to regulate gene expression. While classification performances vary greatly between tissues, we show examples where our classifiers correctly predict tissue specific regulation from sequence alone. We also show that many of the informative patterns indeed harbor transcription factor footprints.

bioinformatics↗

bioRxiv2021

Effect of imputation on gene network reconstruction from single-cell RNA-seq data

Despite the advances in single-cell transcriptomics the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA-seq data make it hard to infer networks from transcriptome data. Data imputation can be applied in order to enhance gene-gene correlations and facilitate downstream data analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks. To study this question, we evaluate the effect of imputation methods on the performance and structure of the reconstructed networks in different experimental single-cell RNA-seq data sets. We use state-of-the-art algorithms for both imputation and network reconstruction and evaluate the difference in results before and after imputation. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. Yet, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms, while warning against the indiscriminate use of imputation before network reconstruction in general.

bioinformatics↗

bioRxiv2021

Bulkhead-like apical membrane structures between hepatocytes are required for anisotropic lumen expansion and liver tissue morphogenesis

Lumen morphogenesis is key to the function of organs and results from the integration of molecular pathways and mechanical forces1-3. The mechanisms governing anisotropic lumen expansion remain elusive4-6. In contrast to epithelial cells which have simple apico-basal polarity and form tubes, hepatocytes are multi-polar and form narrow lumina that grow anisotropically between adjacent cells, collectively generating a complex 3D network of bile canaliculi (BC)7,8. Here, we studied lumen elongation and BC morphogenesis in differentiating primary mouse hepatoblasts in vitro. Remarkably, we discovered a pattern of specific extensions of the apical membrane traversing the lumen between adjacent hepatocytes and sealed by tight junctions, reminiscent of the bulkheads of boats. These structures were also present in the developing liver. A targeted screen revealed that silencing of Rab35 caused loss of the bulkheads, conversion of hepatocyte into simple epithelial polarity and formation of spherical lumina in vitro. Strikingly, we could re-engineer hepatocyte polarity and tissue morphogenesis in vivo in the embryonic liver, converting BC into simple epithelial tubes. Our results suggest that the apical bulkheads of hepatocytes are cell-intrinsic anisotropic mechanical elements that ensure stability of the elongating lumen between two cells, thus determining the structure of BC during liver tissue morphogenesis.

cell biology↗

bioRxiv2020

SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

MotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes. ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual. Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/svim-asm. Contactvingron@molgen.mpg.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots

SO_SCPLOWUMMARYC_SCPLOWIn molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables.

bioinformatics↗

bioRxiv2020

TADA - a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs

The computational prediction of disease-associated genetic variation is of fundamental importance for the genomics, genetics and clinical research communities. Whereas the mechanisms and disease impact underlying coding single nucleotide polymorphisms (SNPs) and small Insertions/Deletions (InDels) have been the focus of intense study, little is known about the corresponding impact of structural variants (SVs), which are challenging to detect, phase and interpret. Few methods have been developed to prioritise larger chromosomal alterations such as Copy Number Variants (CNVs) based on their pathogenicity. We address this issue with TADA, a method to prioritise pathogenic CNVs through manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigorous enrichment analysis. We demonstrate that our machine-learning classifiers for deletions and duplications are able to accurately predict pathogenic CNVs (AUC: 0.8042 and 0.7869, respectively) and produce a well-calibrated pathogenicity score. The combination of enrichment analysis and classifications suggests that prioritisation of pathogenic CNVs based on functional annotation is a promising approach to support clinical diagnostic and to further the understanding of mechanisms that control the disease impact of larger genomic alterations.View Full Text

genetics↗

bioRxiv2020

Trans control of cardiac mRNA translation in a protein length-dependent fashion

Little is known about the impact of naturally occurring genetic variation on the rates with which proteins are synthesized by ribosomes. Here, we investigate how genetic influences on mRNA translational efficiency are associated with complex disease phenotypes using a panel of rat recombinant inbred lines. We identify a locus for cardiac hypertrophy that is associated with a translatome-wide and protein length-dependent shift in translational efficiency. This master regulator primarily affects the translation of very short and very long protein-coding sequences, altering the physiological stoichiometric translation rates of sarcomere proteins. Mechanistic dissection of this locus points to altered ribosome assembly, characterized by accumulation of polysome half-mers, changed ribosomal configurations and misregulation of the small nucleolar RNA SNORA48. We postulate that this locus enhances a pre-existing negative correlation between protein length and translation initiation in diseased hearts. Our work shows that a single genomic locus can trigger a complex, translation-driven molecular mechanism that contributes to phenotypic variability between individuals. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/133298v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@e46986org.highwire.dtl.DTLVardef@1cc0b08org.highwire.dtl.DTLVardef@e86011org.highwire.dtl.DTLVardef@1fb3706_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIGenetic variability impacts protein synthesis rates in a rat model for cardiac hypertrophy C_LIO_LIA trans locus affects stoichiometric translation rates of cardiac sarcomeric proteins C_LIO_LIThis master regulator locus induces a global, protein length-dependent shift in translation C_LIO_LIDysregulated ribosome assembly induces half-mer formation and affects translation initiation rate C_LI

genetics↗

bioRxiv2020

SDip: A novel graph-based approach to haplotype-aware assembly based structural variant calling in targeted segmental duplications sequencing

Segmental duplications are important for understanding human diseases and evolution. The challenge to distinguish allelic and duplication sequences has hindered their phased assembly as well as characterization of structural variant calls. Here we have developed a novel graph-based approach that leverages single nucleotide differences in overlapping reads to distinguish allelic and duplication sequences information from long read accurate PacBio HiFi sequencing. These differences enable to generate allelic and duplication-specific overlaps in the graph to spell out phased assembly used for structural variant calling. We have applied our method to three public genomes: CHM13, NA12878 and HG002. Our method resolved 86% of duplicated regions fully with contig N50 up to 79 kb and produced <800 structural variant phased calls, outperforming state-of-the-part SDA method in terms of all metrics. Furthermore, we demonstrate the importance of phased assemblies and variant calls to the biologically-relevant duplicated genes such as SMN1, SRGAP2C, NPY4R and FAM72A. Our phased assemblies and accurate variant calling specifically in duplicated regions will enable the study of the evolution and adaptation of various species.

bioinformatics↗

bioRxiv2019

Functional dissection of TADs reveals non-essential and instructive roles in regulating gene expression

The genome is organized in megabase-sized three-dimensional units, called Topologically Associated Domains (TADs), that are separated by boundaries. TADs bring distant cis-regulatory elements into proximity, a process dependent on the cooperative action of cohesin and the DNA binding factor CTCF. Surprisingly, genome-wide depletion of CTCF has little effect on transcription, yet structural variations affecting TADs have been shown to cause gene misexpression and congenital disease. Here, we investigate TAD function in vivo in mice by systematically editing components of TAD organization at the Sox9/Kcnj locus. We find that TADs are formed by a redundant system of CTCF sites requiring the removal of all major sites within the TAD and at the boundary for two neighboring TADs to fuse. TAD fusion resulted in leakage of regulatory activity from the Sox9 to the Kcnj TAD, but no major changes in gene expression. This indicates that TAD structures provide robustness and precision, but are not essential for developmental gene regulation. Gene misexpression and resulting disease phenotypes, however, were attained by re-directing regulatory activity through inversions and/or the re-positioning of boundaries. Thus, efficient re-wiring of enhancer promoter interaction and aberrant disease causing gene activation is not induced by a mere loss of insulation but requires the re-direction of contacts.

genetics↗

bioRxiv2019

Preformed Chromatin Topology Assists TranscriptionalRobustness of Shh during Limb Development

Long-range gene regulation involves physical proximity between enhancers and promoters to generate precise patterns of gene expression in space and time. However, in some cases proximity coincides with gene activation, whereas in others preformed topologies already exist before activation. In this study, we investigate the preformed configuration underlying the regulation of the Shh gene by its unique limb enhancer, the ZRS, in vivo during mouse development. Abrogating the constitutive transcription covering the ZRS region led to a shift within the Shh-ZRS contacts and a moderate reduction in Shh transcription. Deletion of the CTCF binding sites around the ZRS resulted in a loss of the Shh-ZRS preformed interaction and a 50% decrease in Shh expression but no phenotype, suggesting an additional, CTCF-independent mechanism of promoter-enhancer communication. This residual activity, however, was diminished by combining the loss of CTCF binding with a hypomorphic ZRS allele resulting in severe Shh loss-of-function and digit agenesis. Our results indicate that the preformed chromatin structure of the Shh locus is sustained by multiple components and acts to reinforce enhancer-promoter communication for robust transcription.

developmental biology↗

bioRxiv2018

CRUP: A comprehensive framework to predict condition-specific regulatory units

We present the software CRUP (Condition-specific Regulatory Units Prediction) to infer from epigenetic marks a list of regulatory units consisting of dynamically changing enhancers with their target genes. The workflow consists of a novel pre-trained enhancer predictor that can be reliably applied across cell lines and species, solely based on histone modification ChIP-seq data. Enhancers are subsequently assigned to different conditions and correlated with gene expression to derive regulatory units. We thoroughly test and then apply CRUP to a rheumatoid arthritis model, identifying enhancer-gene pairs comprising known disease genes as well as new candidate genes.\n\nAvailabilityhttps://github.com/VerenaHeinrich/CRUP

bioinformatics↗

bioRxiv2018

SVIM: Structural Variant Identification using Mapped Long Reads

MotivationStructural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.\n\nResultsWe present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from PacBio and Nanopore sequencing machines.\n\nAvailability and implementationThe source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index.\n\nContactheller_d@molgen.mpg.de

bioinformatics↗

bioRxiv2018

Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise.

The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of dozens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GRs core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed distinct DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise.

systems biology↗

bioRxiv2018

Epigenomic and functional dynamics of human bone marrow myeloid differentiation to mature blood neutrophils

Neutrophils are short-lived blood cells that play a critical role in host defense against infections. To better comprehend neutrophil functions and their regulation, we provide a complete epigenetic and functional overview of their differentiation stages from bone marrow-residing progenitors to mature circulating cells. Integration of epigenetic and transcriptome dynamics reveals an enforced regulation of differentiation, through cellular functions such as: release of proteases, respiratory burst, cell cycle regulation and apoptosis. We observe an early establishment of the cytotoxic capability, whilst the signaling components that activate antimicrobial mechanisms are transcribed at later stages, outside the bone marrow, thus preventing toxic effects in the bone marrow niche. Altogether, these data reveal how the developmental dynamics of the epigenetic landscape orchestrate the daily production of large number of neutrophils required for innate host defense and provide a comprehensive overview of the epigenomes of differentiating human neutrophils.\n\nKey pointsO_LIDynamic acetylation enforces human neutrophil progenitor differentiation.\nC_LI\n\nO_LINeutrophils cytotoxic capability is established early at the (pro)myelocyte stage.\nC_LI\n\nO_LICoordinated signaling component expression prevents unwanted toxic effects to the bone marrow niche.\nC_LI

immunology↗