Biology subjects

Koutrouli, M.

Publications and source records attributed to Koutrouli, M..

11 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↗

bioRxiv2024

Targeted disruption of linkage-specific ubiquitylation reveals a key role of K29-linked ubiquitylation in epigenome integrity

Linkage-specific ubiquitin chains dictate the functional outcome of numerous critical ubiquitin-dependent signaling processes. However, the functions and targets of several poly-ubiquitin topologies remain poorly defined due to a paucity of tools for their specific detection and manipulation. To remedy this knowledge gap, we applied a cell-based ubiquitin replacement strategy enabling targeted conditional abrogation of each of the seven lysine-based ubiquitin chain types in human cells to profile system-wide impacts of disabling formation of individual chain types. Focusing on K29-linked ubiquitylation, we reveal a strong association of this linkage type with chromatin-associated proteins and show that the H3K9me3 methyltransferase SUV39H1 is a prominent cellular target of this modification. We demonstrate that K29-linked ubiquitylation is essential for proteasomal degradation of SUV39H1 despite its extensive modification by K48-linked ubiquitylation, and that K29-linked ubiquitylation of SUV39H1 is catalyzed and reversed by TRIP12 and TRABID, respectively. Preventing K29-linked ubiquitylation-mediated control of SUV39H1 stability deregulates the H3K9me3 landscape, but not other histone marks. Collectively, our ubiquitin replacement cell line panel and datasets provide valuable resources for illuminating cellular functions of linkage-specific ubiquitin chains and establish a key role of K29-linked ubiquitylation in epigenome integrity.

cell biology↗

bioRxiv2024

Lifestyle factors in the biomedical literature: comprehensive resources for named entity recognition

MotivationDespite lifestyle factors (LSFs) being increasingly acknowledged in shaping individual health trajectories, particularly in chronic diseases, they have still not been systematically described in the biomedical literature. This is in part because no named entity recognition (NER) system exists, which can comprehensively detect all types of LSFs in text. The task is challenging due to their inherent diversity, lack of a comprehensive LSF classification for dictionary-based NER, and lack of a corpus for deep learning-based NER. ResultsWe present a novel Lifestyle Factor Ontology (LSFO), which we used to develop a dictionarybased system for recognition and normalization of LSFs. Additionally, we introduce a manually annotated corpus for LSFs (LSF200) suitable for training and evaluation of NER systems, and use it to train a transformer-based system. Evaluating the performance of both NER systems on the corpus revealed an F-score of 64% for the dictionary-based system and 76% for the transformer-based system. Largescale application of these systems on PubMed abstracts and PMC Open Access articles identified over 300 million mentions of LSF in the biomedical literature. AvailabilityLSFO, the annotated LSF200 corpus, and the detected LSFs in PubMed and PMC-OA articles using both NER systems, are available under open licenses via the following GitHub repository: https://github.com/EsmaeilNourani/LSFO-expansion. This repository contains links to two associated GitHub repositories and a Zenodo project related to the study. LSFO is also available at BioPortal: https://bioportal.bioontology.org/ontologies/LSFO. Contactkaterina.nastou@cpr.ku.dk, soren.brunak@cpr.ku.dk, lars.juhl.jensen@cpr.ku.dk Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

CoNECo: A Corpus for Named Entity recognition and normalization of protein Complexes

MotivationDespite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Normalization (NEN) of protein-containing complexes. Current resources inadequately address the recognition of protein-containing complex names across different organisms, underscoring the crucial need for a dedicated corpus. ResultsWe introduce the Complex Named Entity Corpus (CoNECo), an annotated corpus for NER and NEN of complexes. CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, with F1-scores of 73.7% and 61.2%, respectively. Subsequently, we applied the best taggers for comprehensive tagging of the entire openly accessible biomedical literature. AvailabilityAll resources, including the annotated corpus, training data, and code, are available to the community through Zenodo https://zenodo.org/records/11263147 and GitHub https://zenodo.org/records/10693653.

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics Sequencing of Mouse Liver at 2 Micron Resolution Using a Novel Spatial DNA Chip

Spatial transcriptomics enables analysis of gene expression that is spatially resolved within a tissue section, making it possible to elucidate the relationship between individual cells within the context of the tissue. This transformative technology enables researchers to better understand gene function within the context of health tissue, developmental processes, and disease. In this study, we present an innovative spatial transcriptomics technology and data using a high-resolution DNA chip with a total capture region size of 6.5 x 6.5 mm containing 2 x 2 {micro}m features for spatial barcoding with no gaps between the features, thereby maximizing the capture area. These chips are manufactured at wafer scale using photolithography and are transferred to hydrogels, making them compatible with existing workflows for fresh frozen or paraffin-embedded samples. Herein, we examined a fresh frozen sample from an adult mouse liver. To analyze the data, we binned 10 x 10 features to represent a 20 {micro}m x 20 {micro}m capture area. We obtained 1.3 billion unique mapped reads, 68.78% sequencing saturation, with a median of 16,967 unique reads per region, indicating the potential for more unique reads with deeper sequencing. This high-resolution mapping of liver cell types and the visualization of gene expression patterns illustrate significant advancements in spatial sequencing technology.

genomics↗

bioRxiv2023

Improving dictionary-based named entity recognition with deep learning

MotivationDictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. ResultsIn this work we aim to improve block lists by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score=96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. Additionally, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by[~] 5.5% on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%). AvailabilityAll resources are available through Zenodo https://doi.org/10.5281/zenodo.10800530 and GitHub https://doi.org/10.5281/zenodo.10289360.

bioinformatics↗

bioRxiv2022

FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, co-expression analysis becomes complex. To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4,210 interactions between 1,039 understudied proteins. Our findings showcase FAVAs capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

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

U-CIE : Color encoding of high-dimensional data

Data visualization is essential to discover patterns and anomalies in large high-dimensional datasets. New dimensionality reduction techniques have thus been developed for visualizing omics data, in particular from single-cell studies. However, jointly showing several types of data, e.g. single-cell expression and gene networks, remains a challenge. Here, we present U-CIE, a visualization method that encodes arbitrary high-dimensional data as colors using a combination of dimensionality reduction and the CIELAB color space to retain the original structure to the extent possible. U-CIE first uses UMAP to reduce high-dimensional data to three dimensions, partially preserving distances between entities. Next, it embeds the resulting three-dimensional representation within the CIELAB color space. This color model was designed to be perceptually uniform, meaning that the Euclidean distance between any two points should correspond to their relative perceptual difference. Therefore, the combination of UMAP and CIELAB thus results in a color encoding that captures much of the structure of the original high-dimensional data. We illustrate its broad applicability by visualizing single-cell data on a protein network and metagenomic data on a world map and on scatter plots.

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↗

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↗