Biology subjects

Iliopoulos, I.

Publications and source records attributed to Iliopoulos, I..

8 recordsLinked to original sources

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↗

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

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↗

bioRxiv2022

LIRcentral: a manually curated online database of experimentally validated functional LIR-motifs

Several selective macroautophagy receptor and adaptor proteins bind members of the Autophagy-related protein 8 (Atg8) family using short linear motifs (SLiMs), most often referred to as Atg8-interacting motifs (AIM) or LC3-interacting motifs (LIR). AIM/LIR-motifs have been extensively studied during the last fifteen years, since they can uncover the underlying biological mechanisms and possible substrates for this key catabolic process of eukaryotic cells. Prompted by the fact that experimental information regarding LIR-motifs can be found scattered across heterogeneous literature resources, we have developed LIRcentral (https://lircentral.eu), a freely available online repository for user-friendly access to comprehensive, high-quality information regarding LIR-motifs from manually curated publications. Herein, we describe the development of LIRcentral and showcase currently available data and features, along with our plans for the expansion of this resource. Information incorporated in LIRcentral is useful for accomplishing a variety of research tasks, including: (i) guiding wet biology researchers for the characterization of novel instances of LIR-motifs, (ii) giving bioinformaticians/computational biologists access to high-quality LIR-motifs for building novel prediction methods for LIR-motifs and LIR containing proteins (LIRCPs) and (iii) performing analyses to better understand the biological importance/features of functional LIR-motifs. We welcome feedback on the LIRcentral content and functionality by all interested researchers and anticipate this work to spearhead a community effort for sustaining this resource which will further promote progress in studying LIR-motifs/LIRCPs.

bioinformatics↗

bioRxiv2022

Distinct chromosomal niches in the genome of S. cerevisiae provide the background for genomic innovation and shape the fate of gene duplicates

Gene duplication is a major source of genomic innovation in all eukaryotes, with large proportions of genes being the result of either small-scale (SSD) or genome-wide duplication (WGD) events. In the model unicellular eukaryote Saccharomyces cerevisiae, of which nearly one third of the genome corresponds to gene duplicates, the two modes of duplication have been shown to follow different evolutionary fates, with SSD genes being more prone to acquire novel functionalities (neofunctionalization) and WGD more likely to retain different parts of the original ancestral function (subfunctionalization). Having previously described aspects of functional compartmentalization for the genes of S. cerevisiae, in this work we set out to investigate the existence of positional preferences of gene duplicates. We found that SSD and WGD genes are organized in distinct gene clusters that are, furthermore, segregated, occupying different regions, with SSD being more peripheral and WGD more centrally positioned close to centromeric chromatin. Duplicate gene clusters differ from the rest of the genome in terms of gene size and spacing, gene expression variability and regulatory complexity. What is more interesting, some of these properties are also shared by singleton genes residing in duplicate-rich regions in a position-dependent manner. Our analysis further reveals particular chromatin architectures in the promoters of duplicate genes, which are generally longer, with less pronounced nucleosome-free regions, strong structural constraints and a larger number of regulatory elements. Such structural features appear to be important for gene evolution as we find SSD gene-pair co-expression to be strongly associated with the similarity of nucleosome positioning patterns. We propose that specific regions of the yeast genome provide a favourable environment for the generation and maintenance of small-scale gene duplicates. The existence of such genomic "niches" is supported by the enrichment of these regions in singleton genes bearing similarities with gene "relics", remnants of recent duplications that have reverted to single gene status. Our findings provide a valuable framework for the study of genomic innovation and suggest taking into account positional preferences in the study of gene emergence and fixation in experimentally and naturally evolving populations.

genomics↗

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↗

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↗

bioRxiv2019

Hypothesis, analysis and synthesis: it's all Greek to me!

The linguistic foundations of science and technology have relied on a range of terms many of which are borrowed from ancient languages, a known but little researched fact from a statistical perspective. Precise definitions and novel concepts are often crafted with those -- frequently used -- terms, yet their etymology from Greek or Latin might not always be fully appreciated. Herein, we demonstrate that frequently used terms span almost the entire PubMed(R) database, while a handful of terms of Greek origin retrieve 80% of all entries. We argue that the etymology of those critical terms needs to be fully grasped to ensure correct use, in conjunction with other concepts. We further propose a number of terms for genomics, using prepositions that can accurately define subtle sub-disciplines of this ever-expanding field. Finally, we invite commentary by both the science community and the humanities, for possible adoption of suggested terms, not least to avoid inaccurate usage or inappropriate notions that may compromise clarity of meaning.

scientific communication and education↗