Biology subjects

Karatzas, E.

Publications and source records attributed to Karatzas, E..

At least 19 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

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

WasteFams: A database of protein families from global wastewater microbiomes

Wastewater surveillance has emerged as a critical tool for global epidemiology, yet the functional diversity of wastewater microbiomes remains poorly characterized at the protein level. Here, we present WasteFams, the first comprehensive database dedicated to the systematic exploration of protein families in wastewater metagenomic and metatranscriptomic studies worldwide. Integrating data from 580 metagenomes, 132 metatranscriptomes, and 1,709 reference genomes, WasteFams catalogs 3,887 non-redundant protein families (containing {succeq}100 members) derived from over 105 million predicted proteins. Each protein family is enriched with multi-layered annotations, including AlphaFold3 structural predictions, taxonomic classifications, and biome-specific metadata. To further expand their functional annotation, we integrated deep genomic context analysis to link protein families to Mobile Genetic Elements (MGEs), Biosynthetic Gene Clusters (BGCs), Antibiotic Resistance Genes (ARGs), and CRISPR elements. Accessible through the EnvoFams portal, WasteFams provides a user-friendly interface featuring advanced search capabilities, sequence and structural similarity tools, and interactive visualization modules. As global initiatives increasingly leverage wastewater for public health and environmental insights, WasteFams can serve as a critical resource for discovering novel microbial functions, monitoring resistance mechanisms, and exploring the biotechnological potential of secondary metabolites within wastewater-engineered ecosystems.

bioinformatics↗

bioRxiv2025

Quadrupling the protein family space with global metagenomics

The known universe of protein families represents only a small fraction of natures molecular diversity. From 40.3 billion sequences across 40,446 metagenomes, 9,540 metatranscriptomes, and 539 million proteins from 167,415 reference genomes, we identified 608,258 previously uncharacterized protein families with [≥]100 members and 6.5 million families with [≥]25 members, none matching known Pfam domains or reference proteins. This effort doubles the known repertoire of large families and quadruples that of smaller families. Integration of AlphaFold2-based structural predictions with gene-neighborhood and taxonomic analyses enables the characterization of previously unannotated proteins, revealing candidates for both novel and known biological functions in understudied microbial lineages and biomes. This expanded repertoire provides insights into microbial adaptation and broadens the molecular toolkit available for biotechnology, highlighting the power of global metagenomics to uncover hidden protein diversity.

bioinformatics↗

bioRxiv2025

lncAPNet enables the deciphering of lncRNA and mRNA connections in patient transcriptomic data

MotivationLong non-coding RNAs (lncRNAs) regulate gene expression through chromatin remodeling, transcriptional control, and post-transcriptional modulation, influencing physiological cell homeostasis but also disease onset. Yet most transcriptomic and network-based studies rely on descriptive linear co-expression analyses, missing nonlinear and mechanistic insights. Emerging ML/DL methods offer promise but remain limited by data sparsity, noise, insufficient biological priors, and poor interpretability, constraining systems-level lncRNA-mRNA motif discovery. ResultsIn this manuscript, we introduce lncAPNet, an extended version of APNet workflow, which integrates graph-based nonlinear inference of lncRNA-mRNA interactions using NetBID2s activity logic within an lncRNA-focused SJARACNe co-expression network, coupled with PASNet, a biologically informed sparse deep learning model. This framework enables explainable identification of lncRNA drivers in two different cancer type case studies, Chronic Lymphocytic Leukemia (CLL) and Prostate Adenocarcinoma (PRAD), uncovering lncRNA drivers that illuminate lncRNA-mediated programs in cancer progression. Availability and implementationlncAPNets R scripts, Python scripts, and methodologies are available at github repository: https://github.com/BiodataAnalysisGroup/lncAPNet

bioinformatics↗

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

Elucidating the mechanisms of action and evolutionary history of phage anti-defence proteins

Phages and bacteria are locked in a molecular arms race, with phage anti-defence proteins (ADPs) enabling them to evade bacterial immune systems. To streamline access to information on ADPs, we developed the Encyclopaedia of Viral Anti-DefencE Systems (EVADES), an online resource containing sequences, structures, protein family annotations, and mechanisms of action (MoA) for 268 ADPs. Through computational structural analysis we predicted MoAs for 21 uncharacterised ADPs. We demonstrate the utility of EVADES by exploring different characteristics of ADPs: (i) DNA mimic ADPs exhibit broad-spectrum activity against bacterial defence systems; (ii) protein sharing across defence systems enables multi-defence inhibitory activity of ADPs; and (iii) eukaryotic dsDNA viruses encode ADP homologs, suggesting conserved immune evasion strategies across domains of life. These results highlight the broader relevance of phage ADPs in understanding interactions between prokaryotic or eukaryotic viruses and their hosts. EVADES is freely accessible at https://www.ebi.ac.uk/finn-srv/evades. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/658234v3_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@18460corg.highwire.dtl.DTLVardef@19ef231org.highwire.dtl.DTLVardef@2e91a5org.highwire.dtl.DTLVardef@15c2547_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIThe EVADES database aggregates knowledge on phage anti-defence proteins (ADPs), facilitating hypothesis generation. C_LIO_LIMechanisms of action are proposed for 21 uncharacterised ADPs. C_LIO_LIADPs that function as DNA mimics can block diverse bacterial defence systems. C_LIO_LIHomology of targets of ADPs underpin their broad counter-defence capacities and enable prediction of further targets. C_LIO_LIEukaryotic double-stranded DNA viruses employ functional homologs of ADPs to evade host immune responses. C_LI

microbiology↗

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

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↗

bioRxiv2022

Arena3Dweb: Interactive 3D visualization of multilayered networks supporting multiple directional information channels, clustering analysis and application integration

Arena3Dweb is an interactive web tool that visualizes multi-layered networks in 3D space. In this update, Arena3Dweb supports directed networks as well as up to nine different types of connections between pairs of nodes with the use of Bezier curves. It comes with different color schemes (light/gray/dark mode), custom channel coloring, four node clustering algorithms which one can run on-the-fly, visualization in VR mode and predefined layer layouts (zig-zag, star and cube). This update also includes enhanced navigation controls (mouse orbit controls, layer dragging and layer/node selection), while its newly developed API allows integration with external applications as well as saving and loading of sessions in JSON format. Finally, a dedicated Cytoscape app has been developed, through which users can automatically send their 2D networks from Cytoscape to Arena3Dweb for 3D multi-layer visualization. Arena3Dweb is accessible at http://arena3d.pavlopouloslab.info or http://arena3d.org

bioinformatics↗

bioRxiv2022

The network makeup artist (NORMA-2.0): Distinguishing annotated groups in a network using innovative layout strategies

MotivationNetwork biology is a dominant player in todays multi-omics era. Therefore, the need for visualization tools which can efficiently cope with intra-network heterogeneity emerges. ResultsNORMA-2.0 is a web application which uses efficient layouts to group together areas of interest in a network. In this version, NORMA-2.0 utilizes three different strategies to make such groupings as distinct as possible while it preserves all of the properties from its first version where one can handle multiple networks and annotation files simultaneously. AvailabilityThe web resource is available at http://norma.pavlopouloslab.info/ The source code is freely available at https://github.com/PavlopoulosLab/NORMA Contactpavlopoulos@fleming.gr

bioinformatics↗

bioRxiv2021

FLAME: a web tool for functional and literature enrichment analysis of multiple gene lists

Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making a more combinatorial analysis more complicated and prone to errors. In this article, we present FLAME, a web tool for combining multiple lists prior to enrichment analysis. Users can upload several lists of preference and use interactive UpSet plots, as an alternative to Venn diagrams, to handle unions or intersections among the given input files. Functional and literature enrichment along with gene conversions are offered by g:Profiler and aGOtool applications for 197 organisms. In its current version, FLAME can analyze genes/proteins for related articles, Gene Ontologies, pathways, annotations, regulatory motifs, domains, diseases, phenotypes while it can also generate protein-protein interactions derived from STRING. We have herein validated FLAME by interrogating gene expression data associated with the sensitivity of the distal part of the large intestine to experimental colitis-propelled colon cancer. The FLAME application comes with an interactive user-friendly interface which allows easy list manipulation and exploration, while results can be visualized as interactive and parameterizable heatmaps, barcharts, Manhattan plots, networks and tables. AvailabilityFLAME application: http://flame.pavlopouloslab.info Codehttps://github.com/PavlopoulosLab/FLAME

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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

Drug repurposing through a bioinformatics pipeline applied on IPF and other fibrotic diseases

Subject Fibrotic diseases cover a spectrum of systemic and organ-specific maladies that affect a large portion of the population, currently without cure. The shared characteristic these diseases feature is their uncontrollable fibrogenesis deemed responsible for the accumulated damage in the susceptible tissues. Idiopathic Pulmonary Fibrosis (IPF), an interstitial lung disease, is one of the most common and studied fibrotic diseases and still remains an active research target.Objective We highlight unique and common (i) genes, (ii) biological pathways and (iii) candidate repurposed drugs among nine fibrotic diseases. We bibliographically explore the resulting candidate substances for potential anti-fibrotic mode of action and focus on diseases that appear to be more similar to IPF so as to jointly examine potential treatments.Methodology We identify key genes for the 9 fibrotic diseases by analyzing transcriptomics datasets. We construct gene-to-gene networks for each disease and examine these networks to explore functional communities of biological pathways. We also use the most significant genes as input in Drug Repurposing (DR) tools and re-rank the resulting candidates according to their structural properties and functional relationship to each investigated disease.Results We identify 7 biological pathways involved in all 9 fibrotic diseases as well as pathways unique to some of these diseases. Based on our DR results, we suggest captopril and ibuprofen that both appear to slow the progression of fibrotic diseases according to existing bibliography. We also recommend nafcillin and memantine, which haven’t been studied against fibrosis yet, for further wet-lab experimentation. We also observe a group of cardiomyopathy-related pathways that are exclusively highlighted for Oral Submucous Fibrosis (OSF). We suggest digoxin to be tested against OSF, since we observe cardiomyopathy-related pathways implicated in OSF and there is bibliographic evidence that digoxin may potentially clear myocardial fibrosis. Finally, we establish that IPF shares several involved genes, biological pathways and candidate inhibiting-drugs with Dupuytren’s Disease, IgG4-related Disease, SSc and Cystic Fibrosis. We propose that treatments for these fibrotic diseases should be jointly pursued.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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