Biology subjects

Pavlopoulos, G.

Publications and source records attributed to Pavlopoulos, G..

13 recordsLinked to original sources

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↗

bioRxiv2024

invertiaDB: A Database of Inverted Repeats Across Organismal Genomes

Inverted repeats are repetitive elements that can form hairpin and cruciform structures. They are linked to genomic instability, however they also have various biological functions. Their distribution differs markedly across taxonomic groups in the tree of life, and they exhibit high polymorphism due to their inherent genomic instability. Advances in sequencing technologies and declined costs have enabled the generation of an ever-growing number of complete genomes for organisms across taxonomic groups in the tree of life. However, a comprehensive database encompassing inverted repeats across diverse organismal genomes has been lacking. We present InvertiaDB, the first comprehensive database of inverted repeats spanning multiple taxa, featuring repeats identified in the genomes of 118,070 organisms across all major taxonomic groups. The database currently hosts 30,067,666 inverted repeat sequences, serving as a centralized, user-friendly repository to perform searches, interactive visualization, and download existing inverted repeat data for independent analysis. invertiaDB is implemented as a web portal for browsing, analyzing and downloading inverted repeat data. invertiaDB is publicly available at https://invertiadb.netlify.app/homepage.html.

genomics↗

bioRxiv2024

Quadrupia: Derivation of G-quadruplexes for organismal genomes across the tree of life

G-quadruplex DNA structures exhibit a profound influence on essential biological processes, including transcription, replication, telomere maintenance, and genomic stability. These structures have demonstrably shaped organismal evolution. However, a comprehensive, organism-wide G-quadruplex map encompassing the diversity of life has remained elusive. Here, we introduce Quadrupia, the most extensive and well-characterized G-quadruplex database to date, facilitating the exploration of G-quadruplex structures across the evolutionary spectrum. Quadrupia has identified G-quadruplex sequences in 108,449 reference genomes, with a total of 140,181,277 G-quadruplexes. The database also hosts a collection of 319,784 G-quadruplex clusters of 20 or more members, annotated by taxonomic distributions, multiple sequence alignments, profile Hidden Markov Models and cross-references to G-quadruplex 3D structures. Examination of G-quadruplexes across functional genomic elements in different taxa indicates preferential orientation and positioning, with significant differences between individual taxonomic groups. For example, we find that G-quadruplexes in bacteria with a single replication origin display profound preference for the leading orientation. Finally, we experimentally validate the most frequently observed G-quadruplexes using CD-spectroscopy, UV melting, and fluorescent-based approaches. Quadrupia is publicly available through https://www.pavlopoulos-lab.org/quadrupia.

genomics↗

bioRxiv2024

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19

MotivationComputational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. ResultsWe introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests co-expression and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. Availability and ImplementationAPNets R, Python scripts and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet Contactggeorav@certh.gr Supplementary informationSupplementary information can be accessed in Zenodo (10.5281/zenodo.10438830).

systems biology↗

bioRxiv2023

Nucleic Quasi-Primes: Identification of the Shortest Unique Oligonucleotide Sequences in a Species

Despite the exponential increase in sequencing information driven by massively parallel DNA sequencing technologies, universal and succinct genomic fingerprints for each organism are still missing. Identifying the shortest species-specific nucleic sequences offers insights into species evolution and holds potential practical applications in agriculture, wildlife conservation, and healthcare. We propose a new method for sequence analysis termed nucleic "quasi-primes", the shortest occurring sequences in each of 45,785 organismal reference genomes, present in one genome and absent from every other examined genome. In the human genome, we find that the genomic loci of nucleic quasi-primes are most enriched for genes associated with brain development and cognitive function. In a single-cell case study focusing on the human primary motor cortex, nucleic quasi-prime genes account for a significantly larger proportion of the variation based on average gene expression. Non-neuronal cell-types, including astrocytes, endothelial cells, microglia perivascular-macrophages, oligodendrocytes, and vascular and leptomeningeal cells, exhibited significant activation of quasi-prime containing gene associations related to cancer, while simultaneously suppressing quasi-prime containing genes were associated with cognitive, mental, and developmental disorders. We also show that human disease-causing variants, eQTLs, mQTLs and sQTLs are 4.43-fold, 4.34-fold, 4.29-fold and 4.21-fold enriched at human quasi-prime loci, respectively. These findings indicate that nucleic quasi-primes are genomic loci linked to the evolution of species-specific traits and in humans they provide insights in the development of cognitive traits and human diseases, including neurodevelopmental disorders.

genomics↗

bioRxiv2023

kmerDB: A Database Encompassing the Set of Genomic and Proteomic Sequence Information for Each Species

The rapid decline in sequencing cost has enabled the generation of reference genomes and proteomes for a growing number of organisms. However, at the present time, there is no established repository that provides information about organism-specific genomic and proteomic sequences of certain lengths, also known as kmers, that are either present or absent in each genome or proteome. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 45,785 and 22,386 reference genomes and proteomes, respectively, as well as 14,658,776 and 149,264,442 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences that are absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.

genomics↗

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

bioinformatics↗

bioRxiv2020

The Network Analysis Profiler (NAP v2.0): A web tool for visual topological comparison between multiple networks

In this article we present the Network Analysis Profiler (NAP v2.0), a web tool to directly compare the topological features of multiple networks simultaneously. NAP is written in R and Shiny and currently offers both 2D and 3D network visualization as well as simultaneous visual comparisons of node- and edge-based topological features both as bar charts or as a scatterplot matrix. NAP is fully interactive and users can easily export and visualize the intersection between any pair of networks using Venn diagrams or a 2D and a 3D multi-layer graph-based visualization. NAP supports weighted, unweighted, directed, undirected and bipartite graphs and is available at: http://bib.fleming.gr:3838/NAP/. Its code can be found at: https://github.com/PavlopoulosLab/NAP

bioinformatics↗

bioRxiv2018

Large-scale analyses of human microbiomes reveal thousands of small, novel genes and their predicted functions

Small proteins likely abound in prokaryotes, and may mediate much of the communication that occurs between organisms within a microbiome and their host. Unfortunately, small proteins are traditionally overlooked in biology, in part due to the computational and experimental difficulties in detecting them. To systematically identify novel small proteins, we carried out a large comparative genomics study on 1,773 HMP human-associated metagenomes from four different body sites (mouth, gut, skin and vagina). We describe more than four thousand conserved protein families, the majority of which are novel; ~30% of these protein families are predicted to be secreted or transmembrane. Over 90% of the small protein families have no known domain, and almost half are not represented in reference genomes, emphasizing the incompleteness of knowledge in this space. Our analysis exposes putative novel housekeeping small protein families, including a potential novel ribosomally associated protein, as well as mammalian-specific or human-specific protein families. By analyzing the genomic neighborhood of small genes, we pinpoint a subset of families that are potentially associated with defense against bacteriophage. Finally, we identify families that may be subject to horizontal transfer and are thus potentially involved in adaptation of bacteria to the changing human environment. Our study suggest that small proteins are highly abundant and that those of the human microbiome, in particular, may perform diverse functions that have not been previously reported.

microbiology↗