Biology subjects

Bentsen, M.

Publications and source records attributed to Bentsen, M..

10 recordsLinked to original sources

bioRxiv2025

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

The accelerated development of single-cell technologies has profoundly impacted the field of biological research, facilitating unparalleled insights into cellular heterogeneity. However, this progress has also produced new computational challenges in the field of bioinformatics: single-cell datasets are increasingly high-dimensional, multimodal, and large-scale, while analysis workflows often remain fragmented, data type specific, ad hoc, and difficult to reproduce. The prevailing methodologies are dependent on a combination of public tools, which hinders the reproducibility of results, limits scalability, and complicates the efforts to establish benchmarks. The necessity for a higher-level, unified framework for single-cell data analysis is paramount to address these inherent limitations. Here, we introduce the SC-Framework, providing the integration of standardized data structures, declarative workflows and standardized computational backends in a containerized environment, enabling analysts to focus on biological interpretation rather than technical overhead. SC-Framework is available at GitHub (https://github.com/loosolab/SC-Framework).

bioinformatics↗

bioRxiv2025

PEAKQC: Periodicity Evaluation in scATAC-seq data for quality assessment

ATAC-seq is a common protocol to identify regulatory regions in the genome. While quality control (QC) standards are well-established for bulk ATAC-seq, application to single-cell ATAC-seq remains challenging due to data sparsity, noise, and a lack of consensus on effective QC metrics and thresholds. Existing methods, such as fragment length ratio offer limited resolution and fail to fully utilize fragment length distribution (FLD) patterns. We present PEAKQC, a Python-based tool designed to assess single-cell ATAC-seq data quality using a wavelet-based convolution of FLD patterns. Benchmarking PEAKQC against established QC metrics demonstrates an overall more consistent, linear quality assessment of ATAC data. PEAKQC improves downstream analyses by effectively filtering low-quality cells while preserving biologically meaningful data. When combined with other metrics, such as the ratio of fragments in peaks and total counts, PEAKQC enhances clustering accuracy and cell-type identification. The tool is modular, easily installable via Python Package Index, and integrates seamlessly into Python-based single-cell analysis frameworks. PEAKQC provides a robust and scalable solution for single-cell ATAC-seq QC, addressing current gaps in the field and suggesting FLD patterns as a new standard for data quality assessment.

bioinformatics↗

bioRxiv2023

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only [~]700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality i) to isolate FPs without binding motifs, ii) to perform de novo motif generation and iii) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

bioinformatics↗

bioRxiv2023

Scanpro: robust proportion analysis for single cell resolution data

In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation such as adjustment of gene expression. However, in addition to shifting their transcriptional profile, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.

bioinformatics↗

bioRxiv2023

Endocardial HDAC3 is required for myocardial trabeculation

Failure of proper ventricular trabeculation is often associated with congenital heart disease (CHD). Support from endocardial cells, including the secretion of extracellular matrix (ECM) and growth factors is critical for trabeculation. However, it is poorly understood how the secretion of ECM and growth factors is initiated and regulated by endocardial cells. We found that genetic knockout (KO) of histone deacetylase 3 (Hdac3) in the endocardium in mice resulted in early embryo lethality and ventricular hypotrabeculation. Single cell RNA sequencing identified significant downregulation of ECM components in Hdac3 KO endocardial cells. Secretome from cultured Hdac3 KO mouse cardiac endothelial cells lacked transforming growth factor {beta}3 (TGF{beta}3) and showed significantly reduced capacity in stimulating cultured cardiomyocyte proliferation, which was remarkably rescued by TGF{beta}3 supplementation. Mechanistically, we identified that HDAC3 induced Tgf{beta}3 expression through repressing microRNA (miR)-129-5p. Our findings provide novel insights into the pathogenesis of CHD and conceptual strategies to promote myocardial regeneration.

developmental biology↗

bioRxiv2022

TF-COMB - discovering grammar of transcription factor binding sites

Cooperativity between transcription factors is important to regulate target gene expression. In particular, the binding grammar of TFs in relation to each other, as well as in the context of other genomic elements, is crucial for TF functionality. However, tools to easily uncover co-occurrence between DNA-binding proteins, and investigate the regulatory modules of TFs, are limited. Here we present TF-COMB (Transcription Factor Co-Occurrence using Market Basket analysis) - a tool to investigate co-occurring TFs and binding grammar within regulatory regions. We found that TF-COMB can accurately identify known co-occurring TFs from ChIP-seq data, as well as uncover preferential localization to other genomic elements. With the use of ATAC-seq footprinting and TF motif locations, we found that TFs exhibit both preferred orientation and distance in relation to each other, and that these are biologically significant. Finally, we extended the analysis to not only investigate individual TF pairs, but also TF pairs in the context of networks, which enabled the investigation of TF complexes and TF hubs. In conclusion, TF-COMB is a flexible tool to investigate various aspects of TF binding grammar. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/496953v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@e37cccorg.highwire.dtl.DTLVardef@1165241org.highwire.dtl.DTLVardef@725a2corg.highwire.dtl.DTLVardef@17e1893_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

i2dash: Creation of Flexible, Interactive and Web-based Dashboards for Visualization of Omics-pipeline Results

Data visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data. A suitable visualization has to be intuitive and accessible. Web-based dashboards have become popular tools for the arrangement, consolidation and display of such visualizations. However, the combination of automated data processing pipelines handling omics data and dynamically generated, interactive dashboards is poorly solved. Here, we present i2dash, an R package intended to encapsulate functionality for programmatic creation of customized dashboards. It supports interactive and responsive (linked) visualizations across a set of predefined graphical layouts. i2dash addresses the needs of data analysts for a tool that is compatible and attachable to any R-based analysis pipeline, thereby fostering the separation of data visualization on one hand and data analysis tasks on the other hand. In addition, the generic design of i2dash enables data analysts to generate modular extensions for specific needs. As a proof of principle, we provide an extension of i2dash optimized for single-cell RNA-sequencing analysis, supporting the creation of dashboards for the visualization needs of single-cell sequencing experiments. Equipped with these features, i2dash is suitable for extensive use in large scale sequencing/bioinformatics facilities. Along this line, we provide i2dash as a containerized solution, enabling a straightforward large-scale deployment and sharing of dashboards using cloud services.i2dash is freely available via the R package archive CRAN.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2020

multicrispr: fast gRNA designer enables prime editing and parallel targeting of thousands of targets

Targeting the coding genome to introduce single nucleotide deletions/insertions via Crispr/Cas9 technology has become a standard procedure in recent years. It has quickly spawned a multitude of methods such as Prime Editing, Crispr/Cas9 assisted APEX proximity labeling of proteins, or homology directed repair (HDR), for which supporting bioinformatic tools are, however, lagging behind. New applications often require specific guide-RNA (gRNA) design functionality, and a generic gRNA design tool is critically missing. Here we review gRNA designer software and introduce multicrispr, an R based tool intended to design individual gRNAs as well as gRNA libraries targeting many genomic loci in parallel. The package is easy to use, detects, scores and filters gRNAs on both efficiency and specificity, visualizes and aggregates results per target or Crispr/Cas9 sequence, and finally returns both genomic ranges as well as sequences of preferred, off target-free gRNAs. In order to be generic, multicrispr defines and implements a genomic arithmetics framework as a basis for facile adaptation to techniques yet to arise. Its performance and new gRNA design concepts such as target set specific filtering for gRNA libraries render multicrispr the tool of choice when dealing with screening-like approaches.

bioinformatics↗

bioRxiv2019

Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation (ZGA) in both humans and mice, and explore how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. TOBIAS is freely available at: https://github.com/loosolab/TOBIAS.

bioinformatics↗

bioRxiv2018

UROPA GUI: A web platform for genomic region annotation

The annotation of genomic ranges such as peaks resulting from ChIP-seq/ATAC-seq or other techniques represents a fundamental task of bioinformatics analysis with considerable impact on many downstream analyses. In our previous work, we introduced the Universal Robust Peak Annotator (UROPA), a flexible command line based tool which improves upon the functionality of existing annotation software. In order to reduce the complexity for biologists and clinicians, we have implemented an intuitive web-based graphical user interface (GUI) and fully functional service platform for UROPA. This extension will empower all users to generate annotations for regions of interest interactively.\n\nAvailability and ImplementationThe open source UROPA GUI server was implemented in R Shiny and Python and is available from http://loosolab.mpi-bn.mpg.de. The source code of our App can be downloaded at https://github.molgen.mpg.de/loosolab/UROPA_GUI under the MIT license.

bioinformatics↗