Biology subjects

Jennen, D.

Publications and source records attributed to Jennen, D..

3 recordsLinked to original sources

bioRxiv2026

User-friendly transcriptomic data analysis with ArrayAnalysis

Transcriptomic profiling has become a cornerstone of modern biomedical research. To make transcriptomic analyses accessible to a broader scientific community, specifically including researchers with limited bioinformatics expertise, we introduced ArrayAnalysis in 2013 as a user-friendly web-based application for microarray data analysis. We now present a major update (https://arrayanalysis.org), introducing a strongly interactive platform that facilitates the dedicated exploration and analysis of both microarray and RNA-seq data, and allows for the generation of publication-ready outputs. Users can perform key analysis steps, including data pre-processing and quality control, differential expression analysis, and gene set analysis, via a sequential, interactive workflow. At each step, the application provides interactive visualizations accompanied by information pages to support interpretation. Users can dynamically adjust figure layouts and colour palettes and export figures as vector graphics and high-resolution raster images. For non-expert users, ArrayAnalysis offers step-by-step guidance to support correct usage and facilitate learning, while for experienced bioinformaticians, it provides a streamlined and flexible workflow ideal for large-scale analyses requiring efficient and consistent processing. ArrayAnalysis is available both as a web application and for local deployment as a desktop application, Docker image, or R package, making it suitable for diverse computational environments, user groups, and analytical purposes. Together, ArrayAnalysis empowers a broad community of biomedical researchers to unlock the full potential of transcriptomic data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/738193v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1072123org.highwire.dtl.DTLVardef@11095e0org.highwire.dtl.DTLVardef@1dfaee7org.highwire.dtl.DTLVardef@53d31e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Contextualization of causal regulatory networks from toxicogenomics data applied to drug-induced liver injury

Toxicogenomics studies typically reveal a group of genes relevant to the pathophysiology of drug-induced organ injury. In recent years, network-based methods have become an attractive analytical approach as they can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach additionally depicts the mechanisms of regulation that connect upstream regulators in signaling networks towards their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream regulatory signaling networks based on gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to drug-induced liver injury (DILI) from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induce the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally.

systems biology↗

bioRxiv2019

Design principles of ROS dynamic networks relevant to precision therapies for age-related diseases

The eminently complex regulatory network protecting the cell against oxidative stress, surfaces in several disease maps, including that of Parkinsons disease (PD). How this molecular networking achieves its various functionalities and how processes operating at the seconds-minutes time scale cause a disease at a time scale of multiple decennia is enigmatic.\n\nBy computational analysis, we here disentangle the reactive oxygen species (ROS) regulatory network into a hierarchy of subnetworks that each correspond to a different functionality. The detailed dynamic model of ROS management obtained integrates these functionalities and fits in vitro data sets from two different laboratories.\n\nThe model shows effective ROS-management for a century, followed by a sudden systems collapse due to the loss of p62 protein. PD related conditions such as lack of DJ-1 protein or increased -synuclein accelerated the systems collapse. Various in-silico interventions (e.g. addition of antioxidants or caffeine) slowed down the collapse of the system in silico, suggesting the model may help discover new medicinal and nutritional therapies.

systems biology↗