Biology subjects

Pavlopoulos, G. A.

Publications and source records attributed to Pavlopoulos, G. A..

17 recordsLinked to original sources

bioRxiv2026

NORMA 3.0: aesthetic and group-aware visualization and analysis of annotated biological networks

Summary: Interpreting a biological network often rests on the relationship between its connectivity and the functional groups annotated on it, such as pathways, complexes, or enriched terms. For example, whether a group forms a cohesive module, how groups overlap, and which proteins connect them. These relationships are often hard to assess visually when groups are numerous or overlap, and most tools represent membership only by node color. NORMA 3.0 is a complete client-side rewrite of its predecessors, delineating and laying out groups with convex hulls, bubble sets, or fog clouds. Multi-group nodes are rendered as pies and directed, and parallel edges retain their evidence channels. Eighteen 2D and nine 3D layouts are scored by silhouette width, outline intrusion and overlap, and benchmarked for group separation and run time. The implementation also includes edge bundling, network importers for six databases, 21 topological measures, group-level and enrichment statistics, comparison of up to ten networks, export of high-resolution images, and an Application Programming Interface. Availability and implementation: NORMA 3.0 is freely available at https://normaviz.org. The source code is available at https://github.com/PavlopoulosLab/NORMA.

bioinformatics↗

bioRxiv2026

Arena3D 3.0: a web server for exploring, analyzing, annotating and sharing multilayered networks in 3D

Multilayer networks describe biological entities linked by several kinds of relation or measured under several conditions. Arena3D 3.0, a complete reimplementation of Arena3Dweb, gives each layer its own place in an interactive 3D scene. Networks are uploaded or imported from STRING, IntAct, OmniPath, NDEx, Reactome and the Gene Ontology. Twelve layouts, seven in 3D, are available, and node copies can be aligned across layers. Layers are drawn as floors or 3D containers in thirteen arrangements, including 3D hive plots. Communities and annotated groups can be laid out apart and outlined as hulls or fog clouds, and edges can be bundled. Analyses cover node and multilayer metrics, distributions and perturbation, with chord, Sankey, heatmap, UpSet and UMAP views. Networks go to Cytoscape, Gephi Lite, NORMA and JBrowse 2, gene lists to Flame, and a REST API gives programmatic access. Arena3D 3.0 is free at www.arena3d.org without login.

bioinformatics↗

bioRxiv2026

ResLit: A Large-Scale Automated Literature Mining Database for Antimicrobial Resistance

Antimicrobial resistance generates a vast, rapidly growing literature, yet no resource offers a comprehensive, evidence-linked repository of AMR findings at scale. We present ResLit, an automated pipeline and public database that mines the AMR literature for resistance genes, mutations, organisms, and mechanisms. From 2 million candidate PubMed records, BioMistral-7B screened abstracts to 356,000 relevant papers; multi-tier retrieval yielded 117,000 full texts, from which Qwen3-30B performed two-step extraction. ResLit contains 3,120 genes and 13,593 mutations, cross-linked to CARD, ResFinder, and NCBI Reference Gene Catalog across four evidence tiers. It further supports community-driven curation of automated outputs and reference databases. Freely available at www.reslit.info.

microbiology↗

bioRxiv2026

ArthroVerse: mapping protein family diversity across arthropod-associated microbiomes

Metagenomic studies of arthropod-associated microbiomes have generated vast amounts of sequence data, yet the functional and structural organization of these proteins remains largely unexplored. Here, we present ArthroVerse, the first comprehensive database of protein families derived from arthropod-associated metagenomes. Non-redundant protein families were generated after rigorous filtering, deduplication, and clustering. The protein families were further annotated with microbial taxonomy, host associations, protein structural information, and Carbohydrate-active enzymes (CAZyme) predictions. The resulting dataset integrates both metagenomic and reference genome-derived proteins, enabling systematic exploration of functional diversity, evolutionary relationships, and host-microbe interactions in insect microbiomes. ArthroVerse provides a valuable resource for the study of microbial ecology and arthropod physiology, offering unprecedented insight into the protein landscape of insect-associated microbial communities.

bioinformatics↗

bioRxiv2026

Agroforestry transition increases insect diversity and reorganizes soil function in a Mediterranean orchard

Agricultural intensification has reduced biodiversity and weakened ecosystem functions essential for sustainable crop production. Agroforestry has been proposed as a regenerative strategy to restore these functions through vegetation diversification, yet its ecological effects at the farm scale remain insufficiently documented, particularly in Mediterranean perennial systems. Here, adaptive field monitoring and soil shotgun metagenomics were combined to investigate ecological responses across a 10-year agroforestry transition gradient in a Mediterranean citrus and olive farm. Mature agroforestry plots supported higher insect richness and functional diversity while maintaining stable pollinator communities. Plant-insect interaction analyses suggested that potential pest activity was largely associated with spontaneous and supportive vegetation rather than crops. In contrast, soil communities showed limited changes in overall richness but substantial compositional and functional restructuring, including enrichment of the arbuscular mycorrhizal fungus Rhizophagus and proteins with nutrient-cycling functions in advanced plots. Together, these findings suggest that agroforestry transition can promote functional diversification above and below ground and highlight the value of farmer-led regenerative transitions coupled with integrative ecological monitoring for the development of more resilient agricultural systems.

ecology↗

bioRxiv2025

A genomic atlas of the human gut virome elucidates genetic factors shaping host interactions

Viruses are key modulators of human gut microbiome composition and function. While metagenomic sequencing has enabled culture-independent discovery of gut bacteriophage diversity, existing genomic catalogues suffer from limited geographic representation, sparse taxonomic classification, and insufficient functional annotation, hindering detailed investigation into phage biology. Here, we present the Unified Human Gastrointestinal Virome (UHGV), a collection of 873,994 viral genomes from globally diverse populations that addresses these limitations. UHGV provides high-quality virome references with extensive host predictions, comprehensive functional annotations, protein structures, a classification framework for comparative analysis, and a web portal to facilitate data access. Using UHGV to profile worldwide metagenomes, we found that host range breadth is strongly associated with phage prevalence. Additionally, we identified diversity-generating retroelements and DNA methyltransferases as key factors enabling phage populations to access diverse hosts, revealing how specific genomic features contribute to global phage distribution patterns. UHGV is available at http://uhgv.jgi.doe.gov.

genomics↗

bioRxiv2025

nf-core/proteinfamilies: A scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterised sequences with the few experimentally characterised ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates protein nascent families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage can scale approximately linearly with input size, while the biological benchmarks showed that the generated protein families closely resemble manually curated families found in widely used databases.

bioinformatics↗

bioRxiv2025

metagRoot: A comprehensive database of protein families associated with plant root microbiomes

The plant root microbiome is vital in plant health, nutrient uptake, and environmental resilience. To explore and harness this diversity, we present metagRoot, a specialized and enriched database focused on the protein families of the plant root microbiome. MetagRoot integrates metagenomic, metatranscriptomic, and reference genome-derived protein data to characterize 71,091 enriched protein families, each containing at least 100 sequences. These families are annotated with multiple sequence alignments, CRISPR elements, Hidden Markov Models, taxonomic and functional classifications, ecosystem and geolocation metadata, and predicted 3D structures using AlphaFold2. MetagRoot is a powerful tool for decoding the molecular landscape of root-associated microbial communities and advancing microbiome-informed agricultural practices by enriching protein family information with ecological and structural context. The database is available at https://pavlopoulos-lab.org/metagroot/ or https://www.metagroot.org

bioinformatics↗

bioRxiv2023

MPRAbase: A Massively Parallel Reporter Assay Database

Massively parallel reporter assays (MPRAs) represent a set of high-throughput technologies that measure the functional effects of thousands of sequences/variants on gene regulatory activity. There are several different variations of MPRA technology and they are used for numerous applications, including regulatory element discovery, variant effect measurement, saturation mutagenesis, synthetic regulatory element generation or characterization of evolutionary gene regulatory differences. Despite their many designs and uses, there is no comprehensive database that incorporates the results of these experiments. To address this, we developed MPRAbase, a manually curated database that currently harbors 129 experiments, encompassing 17,718,677 elements tested across 35 cell types and 4 organisms. The MPRAbase web interface (http://www.mprabase.com) serves as a centralized user-friendly repository to download existing MPRA data for independent analysis and is designed with the ability to allow researchers to share their published data for rapid dissemination to the community.

genomics↗

bioRxiv2023

Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely-used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g:Profiler, WebGestalt and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyze union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes or (iv) analyze multiple lists of differentially-expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14,436 organisms.

bioinformatics↗

bioRxiv2022

SCALA: A web application for multimodal analysis of single cell next generation sequencing data

Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single cell resolution are still open challenges in the biomedical field. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) datasets. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to identification of distinct cell population and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis and regulatory network reconstruction. SCALA is fully parameterizable at every step of the analysis, presenting data in tabular format and produces publication-ready 2D and 3D visualizations including heatmaps, barcharts, scatter, violin and volcano plots. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling data from both scRNA-seq and scATAC-seq experiments. SCALA is mainly developed in R, Shiny and JavaScript and is available as a web application at http://scala.pavlopouloslab.info or https://scala.fleming.gr.

bioinformatics↗

bioRxiv2021

Bee foraging preferences, microbiota and pathogens revealed by direct shotgun metagenomics of honey

Honeybees (Apis mellifera) continue to succumb to human and environmental pressures despite their crucial role in providing essential ecosystem services. Owing to their foraging and honey production activities, honeybees form complex relationships with species across all domains, such as plants, viruses, bacteria (symbiotic and pathogenic), and other hive pests, making honey a valuable biomonitoring tool for assessing their ecological niche. Thus, the application of honey shotgun metagenomics (SM) has paved the way for a detailed description of the species honeybees interact with, in order to better assess the multiple factors governing their health. Here, we describe the implementation of optimized honey DNA extraction methodology coupled to direct shotgun metagenomics (Direct-SM) analysis, and to a computationally optimised and validated pipeline for taxonomic classification of species detected in honey. By comparing honey collected across 3 harvesting seasons in a stable apiary, we show that Direct-SM can describe the variability of sampled plant species, revealing honeybee behavioural adaptation. In addition, we reveal that Direct-SM can non-invasively capture the diversity of species comprising the core and non-core bacterial communities of the gut microbiome. Finally, we show that this methodology is applicable for the monitoring of pathogens and particularly for the biomonitoring varroa infestation. These results suggest that Direct-SM can accurately and comprehensively describe honeybee ecological niches and can be deployed to assess bee health in the field.

ecology↗

bioRxiv2021

OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis

Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analysing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly2.0, a web application for extracting biomedical entities from individual files such as plain texts, Office documents, PDF files or images. OnTheFly2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform Named Entity Recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and Gene Ontology terms. Multiple files can be analysed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein-protein and protein-chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly2.0 currently supports 197 species and is available at http://onthefly.pavlopouloslab.info.

bioinformatics↗

bioRxiv2021

VICTOR: A visual analytics web application for comparing cluster sets

Clustering is the process of grouping together different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate clustering results. However, different algorithms or parameterization may result in very different clusters. This way, the user is often forced to manually filter and compare these results in order to decide which of them produce the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics web application which allows the comparison and visualization of various clustering algorithms. VICTOR can handle multiple clustering results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared to each other with the use of interactive heatmaps, bar plots, correlation networks, sankey and circos plots. We demonstrate VICTORs functionality using three examples. In the first case, we compare five different algorithms on a protein-protein interaction dataset whereas in the second example, we test four different parameters of the same clustering algorithm applied on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction. VICTOR is available at http://bib.fleming.gr:3838/VICTOR.

bioinformatics↗

bioRxiv2020

Arena3Dweb: Interactive 3D visualization of multilayered networks.

Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D.

bioinformatics↗

bioRxiv2020

Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms

The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.

systems biology↗

bioRxiv2020

NORMA-The network makeup artist: a web tool for network annotation visualization

NORMA is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations (e.g. Gene Ontology/Pathway enrichment or clustering results) can be uploaded and visualized in a network either as colored pie-chart nodes or as color-filled convex hulls in a Venn-diagram-like style. In the case where no annotation exists, algorithms for automated community detection are offered. Users can adjust the network views using standard layout algorithms or allow NORMA to slightly modify them for visually better group separation. Once a network view is set, users can interactively select and highlight any group of interest in order to generate publication-ready figures. Briefly, with NORMA, users can encode three types of information simultaneously. These are: i) the network, ii) the communities or annotations and iii) node categories or expression values. Finally, NORMA offers basic topological analysis and direct topological comparison across any of the selected networks. NORMA service is available at: http://bib.fleming.gr:3838/NORMA or http://genomics-lab.fleming.gr:3838/NORMA. Code is available at: https://github.com/PavlopoulosLab/NORMA

bioinformatics↗