Biology subjects

Schulz, M. H.

Publications and source records attributed to Schulz, M. H..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

A statistical approach to identify regulatory DNA variations

Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data. ResultsWe investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes. ConclusionsOur approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP. Contactmarcel.schulz@em.uni-frankfurt.de

bioinformatics↗

bioRxiv2023

Efficiently Quantifying DNA Methylation for Bulk- and Single-cell Bisulfite Data

MotivationDNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. ResultsWe present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy. AvailabilityAn implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME.

bioinformatics↗

bioRxiv2022

Circular RNA circPLOD2 regulates pericyte function by targeting the transcription factor KLF4

Circular RNAs (circRNAs) are generated by back-splicing and control cellular signaling and phenotypes. Pericytes stabilize the capillary structure and play an important role in the formation and maintenance of new blood vessels. Here, we characterized hypoxia-regulated circRNAs in human pericytes and showed that circPLOD2 is induced by hypoxia and regulates pericyte function. Silencing of circPLOD2 increased pericyte proliferation, endothelial-pericyte interaction and tube formation. Transcriptional profiling of circPLOD2-depleted cells and epigenomic analyses revealed widespread changes in gene expression and identified the circPLOD2-dependent regulation of the transcription factor KLF4 as a key effector of these changes. Importantly, overexpression of KLF4 was sufficient to reverse the effects on pericyte proliferation and endothelial-pericyte interactions observed after circPLOD2 depletion. Together, these data revealed a novel function of circPLOD2 in the control of pericyte proliferation and capillary formation and showed that circPLOD2-mediated regulation of KLF4 significantly contributes to the transcriptional response to hypoxia. HighlightsO_LIcircPLOD2 is upregulated in hypoxia in human vascular pericytes C_LIO_LISelective depletion of circPLOD2, but not linear PLOD2 mRNA, changes pericyte migration and endothelial-pericyte interaction C_LIO_LIcircPLOD2 depletion triggers widespread changes in gene expression that are mirrored in the transcriptional hypoxia response C_LIO_LIEpigenomic analyses pinpoint the transcription factor KLF4 as a central player in circPLOD2-mediated expression changes C_LIO_LIKLF4 overexpression is sufficient to rescue the changes in pericyte function caused by circPLOD2 depletion C_LI

molecular biology↗

bioRxiv2022

TF-Prioritizer: a java pipeline to prioritize condition-specific transcription factors

BackgroundEukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic data sets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multi-modal data sets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., ChIP-seq, ATAC-seq, or DNase-seq) and RNA-seq data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results. ResultsWe developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multi-modal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE data sets for cell lines K562 and MCF-7, including twelve histone modification ChIP-seq as well as ATAC-seq and DNase-seq datasets, where we observe and discuss assay-specific differences. ConclusionTF-Prioritizer accepts ATAC-seq, DNase-seq, or ChIP-seq and RNA-seq data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

bioinformatics↗

bioRxiv2022

The epigenetic modifier DOT1L regulates gene regulatory networks necessary for cardiac patterning and cardiomyocyte cell cycle withdrawal

Mechanisms by which specific histone modifications regulate distinct gene regulatory networks remain little understood. We investigated how H3K79me2, a modification catalyzed by DOT1L and previously considered a general transcriptional activation mark, regulates gene expression in mammalian cardiogenesis. Early embryonic cardiomyocyte ablation of Dot1l revealed that H3K79me2 does not act as a general transcriptional activator, but rather regulates highly specific gene regulatory networks at two critical cardiogenic junctures: left ventricle patterning and postnatal cardiomyocyte cell cycle withdrawal. Mechanistic analyses revealed that H3K79me2 in two distinct domains, gene bodies and regulatory elements, synergized to promote expression of genes activated by DOT1L. Surprisingly, these analyses also revealed that H3K79me2 in specific regulatory elements contributed to silencing genes usually not expressed in cardiomyocytes. As DOT1L mutants had increased numbers of postnatal mononuclear cardiomyocytes and prolonged cardiomyocyte cell cycle activity, controlled inhibition of DOT1L might be a strategy to promote cardiac regeneration post-injury.

developmental biology↗

bioRxiv2022

The endothelial-specific LINC00607 mediates endothelial angiogenic function

Long non-coding RNAs (lncRNAs) can act as regulatory RNAs which, by altering the expression of target genes, impact on the cellular phenotype and cardiovascular disease development. Endothelial lncRNAs and their vascular functions are largely undefined. Deep RNA-Seq and FANTOM5 CAGE analysis revealed the lncRNA LINC00607 to be highly enriched in human endothelial cells. LINC00607 was induced in response to hypoxia, arteriosclerosis regression in non-human primates and also in response to propranolol used to induce regression of human arteriovenous malformations. siRNA knockdown or CRISPR/Cas9 knockout of LINC00607 attenuated VEGF-A-induced angiogenic sprouting. LINC00607 knockout in endothelial cells also integrated less into newly formed vascular networks in an in vivo assay in SCID mice. Overexpression of LINC00607 in CRISPR knockout cells restored normal endothelial function. RNA- and ATAC-Seq after LINC00607 knockout revealed changes in the transcription of endothelial gene sets linked to the endothelial phenotype and in chromatin accessibility around ERG-binding sites. Mechanistically, LINC00607 interacted with the SWI/SNF chromatin remodeling protein BRG1. CRISPR/Cas9-mediated knockout of BRG1 in HUVEC followed by CUT&RUN revealed that BRG1 is required to secure a stable chromatin state, mainly on ERG-binding sites. In conclusion, LINC00607 is an endothelial-enriched lncRNA that maintains ERG target gene transcription by interacting with the chromatin remodeler BRG1.

molecular biology↗

bioRxiv2022

The adapted Activity-By-Contact-model for enhancer-gene assignment and its combination with transcription factor affinities in single cell data

Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model (Fulco et al., 2019) which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a genes perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC-model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is no implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data. We demonstrate that the ABC-score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalised ABC-model (gABC) with TF binding information and illustrate an analysis of a single-cell ATAC-seq data set of the human heart, where we were able to characterise cell type-specific regulatory interactions and predict gene expression based on transcription factor affinities. All executed processing steps are incorporated into our new computational pipeline STARE. The software is available at https://github.com/schulzlab/STARE.

bioinformatics↗

bioRxiv2021

HIF1α-AS1 is a DNA:DNA:RNA triplex-forming lncRNA interacting with the HUSH complex

DNA:DNA:RNA triplexes that are formed through Hoogsteen base-pairing have been observed in vitro, but the extent to which these interactions occur in cells and how they impact cellular functions remains elusive. Using a combination of bioinformatic techniques, RNA/DNA pulldown and biophysical studies, we set out to identify functionally important DNA:DNA:RNA triplex-forming long non-coding RNAs (lncRNA) in human endothelial cells. The lncRNA HIF1-AS1 was retrieved as a top hit. Endogenous HIF1-AS1 reduced the expression of numerous genes, including EPH Receptor A2 and Adrenomedullin through DNA:DNA:RNA triplex formation by acting as an adapter for the repressive human silencing hub complex (HUSH). Moreover, the oxygen-sensitive HIF1-AS1 was down-regulated in pulmonary hypertension and loss-of-function approaches not only resulted in gene de-repression but also enhanced angiogenic capacity. As exemplified here with HIF1-AS1, DNA:DNA:RNA triplex formation is a functionally important mechanism of trans-acting gene expression control.

molecular biology↗

bioRxiv2021

Energy efficient convolutional neural networks for arrhythmia detection

Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.

bioinformatics↗

bioRxiv2021

Comparative Analysis of common alignment tools for single cell RNA sequencing

With the rise of single cell RNA sequencing new bioinformatic tools became available to handle specific demands, such as quantifying unique molecular identifiers and correcting cell barcodes. Here, we analysed several datasets with the most common alignment tools for scRNA-seq data. We evaluated differences in the whitelisting, gene quantification, overall performance and potential variations in clustering or detection of differentially expressed genes. We compared the tools Cell Ranger 5, STARsolo, Kallisto and Alevin on three published datasets for human and mouse, sequenced with different versions of the 10X sequencing protocol. Striking differences have been observed in the overall runtime of the mappers. Besides that Kallisto and Alevin showed variances in the number of valid cells and detected genes per cell. Kallisto reported the highest number of cells, however, we observed an overrepresentation of cells with low gene content and unknown celtype. Conversely, Alevin rarely reported such low content cells. Further variations were detected in the set of expressed genes. While STARsolo, Cell Ranger 5 and Alevin released similar gene sets, Kallisto detected additional genes from the Vmn and Olfr gene family, which are likely mapping artifacts. We also observed differences in the mitochondrial content of the resulting cells when comparing a prefiltered annotation set to the full annotation set that includes pseudogenes and other biotypes. Overall, this study provides a detailed comparison of common scRNA-seq mappers and shows their specific properties on 10X Genomics data. Key messagesO_LIMapping and gene quantifications are the most resource and time intensive steps during the analysis of scRNA-Seq data. C_LIO_LIThe usage of alternative alignment tools reduces the time for analysing scRNA-Seq data. C_LIO_LIDifferent mapping strategies influence key properties of scRNA-SEQ e.g. total cell counts or genes per cell C_LIO_LIA better understanding of advantages and disadvantages for each mapping algorithm might improve analysis results. C_LI

bioinformatics↗

bioRxiv2020

Computational prediction of CRISPR-impaired non-coding regulatory regions

Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our approach on the analysis of a genome-wide CRISPR screen in hTERT-RPE-1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our approach is general and can be applied on any cell type and with different CRISPR enzymes.

bioinformatics↗

bioRxiv2020

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcript factor binding models

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MO_SCPLOWEC_SCPLOWDO_SCPLOWEC_SCPLOWMO_SCPLOWOC_SCPLOW, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that are affected by DNA methylation. Overall, we find that CpG methylation decreases the likelihood of binding for the majority of TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding.

bioinformatics↗

bioRxiv2019

Integrative prediction of gene expression with chromatin accessibility and conformation data

BackgroundEnhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organisation of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.\n\nResultsWe have extended our Tepic framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We found that including long-range PEIs deduced from both HiC and HiChIP data indeed improves model performance. We designed a novel machine learning approach that allows to prioritize TFs in distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.\n\nConclusionWe show that the integration of chromatin conformation data improves gene expression prediction, underlining the importance of enhancer looping for gene expression regulation. Our general approach can be used to prioritize TFs that are involved in distal and promoter-proximal regulation using accessibility, conformation and expression data.

bioinformatics↗

bioRxiv2019

Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data

Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-Seq, ATAC-Seq and NOMe-Seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However we also find a high proportion of assay specific NDRs that are often "called" by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-Seq and DNAse I-Seq provide a high NDR calling rate for relatively low sequencing costs in comparison to NOMe-Seq, NOMe-Seq singles out as it provides a multitude of information: it allows to not only detect NDRs but also endogenous DNA methylation, genome wide segmentation into heterochromatic A/B domains and local phasing of nucleosomes outside of NDRs. In summary our comparison strongly suggest to consider assay specific differences for the experimental desgin and for generalized and comparative functional interpretations.

genomics↗

bioRxiv2018

Fast Detection of Differential Chromatin Domains with SCIDDO

The generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a common approach to dissect the complexity of the epigenome. However, interpretation and differential analysis of histone ChIP-seq datasets remains challenging due to the genomic co-occurrence of several marks and their difference in genomic spread. Here we present SCIDDO, a fast statistical method for the detection of differential chromatin domains (DCDs) from chromatin state maps. DCD detection simplifies relevant tasks such as the characterization of chromatin changes in differentially expressed genes or the examination of chromatin dynamics at regulatory elements. SCIDDO is available at github.com/ptrebert/sciddo

bioinformatics↗

bioRxiv2018

Identification of Transcription Factor Binding Sites using ATAC-seq

1Transposase-Accessible Chromatin (ATAC) followed by sequencing (ATAC-seq) is a simple and fast protocol for detection of open chromatin. However, computational footprinting in ATAC-seq, i.e. search for regions with depletion of cleavage events due to transcription factor binding sites, has been poorly explored so far. We propose HINT-ATAC, a footprinting method that addresses ATAC-seq specific protocol artifacts. HINT-ATAC uses a probabilistic framework based on Variable-order Markov models to learn the complex sequence cleavage preferences of the transposase enzyme. Moreover, we observed specific strand specific cleavage patterns around the binding sites of transcription factors, which are determined by local nucleosome architecture. HINT-ATAC explores local nucleosome architecture to significantly outperform competing footprinting methods in predicting transcription factor binding sites by ChIP-seq. HINT-ATAC is an open source software and available online at www.regulatory-genomics.org/hint

bioinformatics↗

bioRxiv2018

Integrative analysis of single cell expression data reveals distinct regulatory states in bidirectional promoters

BackgroundBidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single cell technologies are not yet established, in the context of BPs.\n\nResultsWe performed integrative analyses of novel human single cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles.\n\nConclusionsOur results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

genomics↗

bioRxiv2018

Automated analysis of small RNA datasets with RAPID

Summary: Understanding the role of short-interfering RNA (siRNA) in diverse biological processes is of current interest and often approached through small RNA sequencing. However, analysis of these datasets is difficult due to the complexity of biological RNA processing pathways, which differ between species. Several properties like strand specificity, length distribution, and distribution of soft-clipped bases are few parameters known to guide researchers in understanding the role of siRNAs. We present RAPID, a generic eukaryotic siRNA analysis pipeline, which captures information inherent in the datasets and automatically produces numerous visualizations as user-friendly HTML reports, covering multiple categories required for siRNA analysis. RAPID also facilitates an automated comparison of multiple datasets, with one of the normalization techniques dedicated for siRNA knockdown analysis, and integrates differential expression analysis using DESeq2. RAPID is available under MIT license at https://github.com/SchulzLab/RAPID. We recommend using it as a conda environment available from https://anaconda.org/bioconda/rapid.

bioinformatics↗