bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.12.20.521192

Plant-PLMview: a database for identifying cis-regulatory sequences with preferential positions in gene-proximal regions of plants

Roziere, J.·Samson, F.·Guichard, C.·Correa, M.·Coursol, S.·Martin, M.-L.·Brunaud, V.
View original source ↗Export metadata ↓

Abstract

BackgroundEstablishing relationships between transcription factors and target genes is essential for understanding the mechanisms regulating gene expression, which play a fundamental role in plant adaptation to the local environment. Despite the importance of this research area and the tremendous progress in sequencing methods such as ChIP-seq and DAP-seq, we are still far from a complete reconstruction of the cis-regulatory landscape. Only a small number of transcription factors can be assessed from experimental data to identify their cis-regulatory binding sites. This highlights the role that in silico approaches can play to complement experimental data. ResultsWe have developed Plant-PLMview, a web-accessible database for detecting preferentially cis-regulatory sequences in the gene-proximal regions of 20 plant species. Users of Plant-PLMview can (i) access the proximal regions of controlled genes from 20 plant species, (ii) query their own DNA motifs or access 840 cis-regulatory sequences from various plant resources, and (iii) use a tool called PLMdetect to search for preferentially located motifs in the gene-proximal regions of a list of genes. Results are displayed via a web interface with a list of DNA motifs preferentially located in a region near the start or end of genes, the distribution of these motifs and associated annotations. In addition, a graphical map of the preferential locations of the motifs in the 5 and 3-proximal regions of genes provides an overview of all motifs and genes examined. ConclusionPlant-PLMview provides the opportunity to study the proximal landscape of gene regulation in 20 plant genomes. The originality of the database lies in its ease of use thanks to the curated data (cis-regulatory sequences and proximal regions of genes) and the possibility to search through PLMdetect in the 5-gene-proximal region, but also in the 3-gene-proximal region, which is rarely explored. The web interface provides numerous graphical views that allow the users to get an overview and interpret the results more easily.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Roziere, J., Samson, F., Guichard, C., Correa, M., Coursol, S., Martin, M.-L., Brunaud, V.. 2022-12-20. Plant-PLMview: a database for identifying cis-regulatory sequences with preferential positions in gene-proximal regions of plants. https://doi.org/10.1101/2022.12.20.521192

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗