A universal tool for visualisation of networks and trees in Python

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Visualisation is an important step to gain insight into network data, however most network analysis software packages, such as igraph and NetworkX, focus on statistical tools and offer limited visualisation options. Other packages offer powerful graphical user interfaces but rely on custom formats and implementations, which restricts interoperability with other scientific software. Methods iplotx is a Python software package designed to act as a universal visualiser for any network analysis package. A simple yet rich declarative style grammar enables customisation of any visual element including edge geometry, marker size and shape, labels, and groupings. Rendering relies on custom Matplotlib artists, which enables broad interoperability with other data visualisation libraries. Results iplotx supports eight different network and tree analysis packages (NetworkX, igraph, graph tool, Biopython, ETE, cogent3, dendropy, and scikit-bio) and implements a plug-in mechanism for universal input compatibility. It supports a wide spectrum of outputs including raster and vector images, animations, and interactive plots. The package implements unique features such as three-dimensional network visualisations and is fully compatible with the Python scientific software stack including seaborn, scipy, pandas, and Cartopy. Areas of application for iplotx include biomolecular and gene regulatory networks, protein-protein interactions, biochemical pathways, ecological networks, phylogenetics, and extend beyond biomedicine into economics, social, and transportation sciences. Conclusions Compared to extant software, iplotx offers broader interoperability, richer styling, a large gallery of examples, and extensive code testing. This makes iplotx a robust choice for the visualisation of any networks and trees in biomedicine and beyond. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1377", "name": "A universal tool for visualisation of networks and trees in Python" } } ] } Home Browse A universal tool for visualisation of networks and trees in Python ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Zanini F. A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.12688/f1000research.173131.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Software Tool Article A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] Fabio Zanini https://orcid.org/0000-0001-7097-8539 1-3 Fabio Zanini https://orcid.org/0000-0001-7097-8539 1-3 PUBLISHED 09 Dec 2025 Author details Author details 1 School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, 2052, Australia 2 Ecology & Evolution Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia 3 UNSW Cellular Genomics Futures Institute, UNSW, NSW, 2052, Australia Fabio Zanini Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Python collection. Abstract Background Graphs or networks are ubiquitous data structures across scientific disciplines, from ecological and molecular to social networks. Visualisation is an important step to gain insight into network data, however most network analysis software packages, such as igraph and NetworkX , focus on statistical tools and offer limited visualisation options. Other packages offer powerful graphical user interfaces but rely on custom formats and implementations, which restricts interoperability with other scientific software. Methods iplotx is a Python software package designed to act as a universal visualiser for any network analysis package. A simple yet rich declarative style grammar enables customisation of any visual element including edge geometry, marker size and shape, labels, and groupings. Rendering relies on custom Matplotlib artists, which enables broad interoperability with other data visualisation libraries. Results iplotx supports eight different network and tree analysis packages (NetworkX, igraph, graph tool, Biopython, ETE, cogent3, dendropy, and scikit-bio) and implements a plug-in mechanism for universal input compatibility. It supports a wide spectrum of outputs including raster and vector images, animations, and interactive plots. The package implements unique features such as three-dimensional network visualisations and is fully compatible with the Python scientific software stack including seaborn, scipy, pandas, and Cartopy. Areas of application for iplotx include biomolecular and gene regulatory networks, protein-protein interactions, biochemical pathways, ecological networks, phylogenetics, and extend beyond biomedicine into economics, social, and transportation sciences. Conclusions Compared to extant software, iplotx offers broader interoperability, richer styling, a large gallery of examples, and extensive code testing. This makes iplotx a robust choice for the visualisation of any networks and trees in biomedicine and beyond. READ ALL READ LESS Keywords networks, graphs, trees, visualisation, phylogenetics, plotting, interactions. Corresponding Author(s) Fabio Zanini ( [email protected] ) Close Corresponding author: Fabio Zanini Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Zanini F. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Zanini F. A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.12688/f1000research.173131.1 ) First published: 09 Dec 2025, 14 :1377 ( https://doi.org/10.12688/f1000research.173131.1 ) Latest published: 09 Dec 2025, 14 :1377 ( https://doi.org/10.12688/f1000research.173131.1 ) Introduction Graphs or networks are widely used to encode relationships between entities including people (social networks), species (e.g. food networks, phylogenies), places (e.g. transportation networks) and domain-specific objects (e.g. gene regulatory networks). Network analysis is an important research field across scientific disciplines and supported by a number of software packages including NetworkX ( Hagberg, Schult, and Swart 2008 ), igraph ( Antonov et al. 2023 ), and graph-tool ( Peixoto 2017 ) in Python and rigraph ( Antonov et al. 2023 ) and statnet ( Pavel N. Krivitsky, Mark S. Handcock and Hunter, David R. and Butts, Carter T. and Bojanowski, Michal and Klumb, Chad and Goodreau, Steven M. and Morris, Martina 2003/2024 ) in R. Among all networks, trees are often used to represent hierarchical relationships, such as animal ( Redelings and Holder 2017 ) and viral evolution ( Sagulenko, Puller, and Neher 2018 ; Drummond and Rambaut 2007 ). Mature software libraries that focus on or include manipulation and analysis of trees include cogent3 ( Huttley et al. 2025 ), dendropy ( Moreno, Holder, and Sukumaran 2024 ), Environment for Tree Exploration ( ETE ) ( Huerta-Cepas, Serra, and Bork 2016 ), Biopython ( Cock et al. 2009 ), and scikit-bio ( Team 2020 ). The centrality of statistics notwithstanding, visualisation is an equally important tool to explore and understand a network’s topology and other properties. As mathematical objects, networks and trees exist outside of geometric spaces, therefore their visualisation requires two steps: i) Embedding the network in either a two- or a three-dimensional space, and ii) Visualising the resulting layout in a way that facilitates interpretation of the network properties. Network analysis libraries already implement many layout algorithms ( Antonov et al. 2023 ; Hagberg, Schult, and Swart 2008 ; Peixoto 2017 ). Underscoring the importance of visualisation is the existence of domain-specific software that centers the entire workflow of network analysis and interpretation on a graphical user interface (GUI). Popular examples in this space include Cytoscape , developed for biomolecular networks ( Shannon et al. 2003 ), SocNetV , focused on social networks ( “Free and Open-Source Tool for Social Network Analysis,” n.d. ), and nextstrain , dedicated to pathogen evolution ( Hadfield et al. 2018 ). While visualisation functionality varies across these tools, they tend to fall into one of two categories. Some packages, including NetworkX , igraph , cogent3 , and dendropy offer limited plot customisation and rely on standard plotting backends such as Matplotlib ( Hunter 2007 ), plotly ( Plotly Technologies Inc. 2015 ), and the terminal. Others, including graph-tool ( Peixoto 2017 ) , Cytoscape ( Shannon et al. 2003 ), SocNetV (“Free and Open-Source Tool for Social Network Analysis,” n.d.) and ETE ( Huerta-Cepas, Serra, and Bork 2016 ), offer a higher level of customisation but use non-interactive or custom rendering backends, which precludes interoperability with other chart types beyond the network itself. iplotx was developed to enable highly customisable network visualisation that is fully compatible with a generic plotting framework. By relying on a Matplotlib, it naturally supports animations, post-render editing, multiple output types (e.g. vector and raster files, interactive widgets), and compatibility with both Matplotlib features (e.g. other chart types, annotations, inset axes, colour bars) and a large library of third-party tools including seaborn ( Waskom et al. 2014 ) and scipy ( Virtanen et al. 2020 ). Unlike other tools, iplotx accepts data from multiple input libraries ( NetworkX , igraph , graph-tool , Biopython , cogent3 , ETE4 , dendropy , and scikit-bio ) and includes a mechanism to extend compatibility to any input data stream. Because of this universal compatibility, iplotx guarantees identical visual appearance for networks and trees independent of how they were processed. Methods Implementation iplotx follows best practices in free open-source software development. The codebase is versioned on GitHub has a stable API. Documentation ( https://iplotx.readthedocs.io ) includes a reference library, a gallery of about 90 examples, and installation instructions. Code integrity is verified via continuous integration/development (CI/CD) pipelines and 232 unit tests covering >90% of the codebase. Code formatting tools and pre-commit hooks ensure high readability. User and contributor interactions take place online transparently and respectfully. Technically, the library leverages custom Matplotlib artists ( Hunter 2007 ) to ensure a coherent appearance of nodes, edges, and other element types upon transformations (e.g. zoom, pan, different Density Per Inch (DPI) settings, animations). Two sets of coordinate transforms are used throughout the codebase: (i) Data units to define the location of the node centers and other artists’ properties, and (ii) Figure units to define node size, edge width, and similar “scale-free” properties. This dual coordinate system is consistent with the behaviour of general charting libraries which maintain marker size upon zoom. Operation Installation is achieved via the standard PyPI repository: detailed instructions available on the website. The software package supports Python 3.10 or later and any platform covered by its dependencies, namely Matplotlib and pandas, which includes Linux, MacOS, and Windows. The package is imported as a Python module and used mainly via its iplotx.network and iplotx.tree functions. iplotx autodiscovers network analysis libraries at runtime and provisions input data providers based on the user’s specific installation. For example, if a user’s installation includes igraph but not NetworkX , iplotx will automatically activate the corresponding input data stream: no user input is required. Results Efficient, universal middleware between network analysis and rendering Network visualisation sits between network analysis and graphical rendering onto paper or screen. The goal of iplotx is to unify this intermediate stage for the Python ecosystem ( Figure 1A ). For this purpose, iplotx was designed to accept input from multiple (currently eight) network and tree analysis libraries ( Antonov et al. 2023 ; Hagberg, Schult, and Swart 2008 ; McArthur et al. 2025 ; Team 2020 ; Huerta-Cepas, Serra, and Bork 2016 ; Cock et al. 2009 ). iplotx also implements simple network and tree data structures for users seeking to minimise external package dependencies. A separate mechanism enables external developers to add compatibility with iplotx to their own package. Internally, data from all input providers is represented in the same exact way, guaranteeing the same visual output independently of the input stream ( Figure 1B ). Figure 1. Overview of iplotx’s scope. A) Schematic representation of network visualisation workflows with iplotx as a universal intermediate stage between network/tree analysis libraries [REFs] and the Matplotlib rendering backend [REF]. B) Simple example of a network visualisation from two distinct libraries through iplotx , which guarantees identical appearance independent of the data provider. C) Runtime scalability for Erdős–Rényi graphs with varying number of nodes (n) and edges (m). Black stars indicate graphs with twice as many edges as nodes. All computations were performed on a consumer laptop (Dell Latitude 7440) and ran for less than a minute. Consistent output on multiple rendering backends was achieved through tight integration with Matplotlib ( Hunter 2007 ), a popular library for data plotting that can export charts into multiple image formats (e.g. PNG, SVG) but also offers interactive renderers such as Tkinter, Qt, and GTK, which react to mouse and keyboard events and can be animated ( Figure 1A ). iplotx objects can be combined with other artists such as bar, line, and scatter plots, images, and inset axes. N etworks with hundreds of thousands of nodes and edges can be visualised within seconds on a consumer laptop ( Figure 1C ). Empirical tests confirm the expected runtime complexity as O ( n + m ) where n and m are the number of nodes and edges: the backend needs to render vertices and edges individually. This level of efficiency is abundantly sufficient for many practical applications and can be further improved by strategic user choices (e.g. visualising a subset of or no edges). Examples of practical applications are given below. Simple yet expressive styling of networks and trees iplotx was designed around a declarative styling grammar that covers all elements types of the visualisation including vertices, edges, arrows, loops, and labels ( Figure 2A ). The appearance of each element type can be customised in multiple ways. For example, edge geometry for a simple network with two nodes can follow a straight line, a circular arc, Bezier control points, specified incidence angles (called “ports” in graphviz ( Gansner and North 2000 )), or waypoints ( Figure 2B ). Furthermore, each element type can be specified either globally or on a per-element basis. For instance, one could set a global vertex colour and size but distinct colour and curvature for each edge ( Figure 2C ). A library of default styles is maintained for rapid prototyping and can be used as a basis for further customisation. A complete style format specification that includes all possible style options is part of the public documentation. Figure 2. Style grammar and features. A) Style grammar for network visualisations. B) Edge geometry grammar. C) Example of global (e.g. vertex colour) and per-element styling (e.g. edge colour and curvature). D) Vertex clustering visualisation. E) Three dimensional visualisation with depth shading. F) Quantitative edge colouring with colour bar. G) Combination of network plot with scipy ’s Voronoi tassellation [REF]. H) Cascading patches on a radial tree. I) Split edge styling on a rectangular tree. J) Dual tree visualisation with links between the leaves. iplotx also includes features that are either unique or found in distinct subsets of other packages. That includes - but is not limited to - graph clusterings and covers a la igraph ( Figure 2D ), three-dimensional visualisations with node and edge depth shading ( Figure 2E ), and quantitative element colouring via a colour map ( Figure 2F , similar to but easier to use than NetworkX ’s functionality). Moreover, iplotx visualisations are compatible with other packages within the Matplotlib framework including seaborn for statistical data ( Waskom et al. 2014 ), pandas for tabular data ( McKinney 2011 ), scipy for scientific data ( Virtanen et al. 2020 ), and Cartopy for geolocation data ( Met Office 2010 - 2015 ). As an example, network visualisations can be combined with scipy ’s Voronoi tassellation functionality ( Figure 2G ). Tree visualisations also support dedicated features, among others: cascading patches for subtrees ( Figure 2H ), split-edge styles ( Figure 2I ), and a specialty function for pairs of trees facing each other, a common need in coevolutionary studies ( Figure 2J ). For both networks and trees, iplotx also supports animations, post-render artist editing, and interaction via callback functions. Crucially, by virtue of accepting many kinds of live objects as input ( Figure 1A ), iplotx makes all features available to all packages. Use cases Given that networks are widely used across scientific disciplines, a large gallery of examples specific for individual fields was created. In addition to classic examples from network science such as Donald Knuth’s map of US cities ( Figure 3A ) ( Knuth 1993 ), it includes molecular ecological networks ( Figure 3B ) ( Meyer et al. 2020 ), economic networks between financial institutions ( Figure 3C ) ( Schweitzer et al. 2009 ), protein-protein interaction networks within bacteria ( Figure 3D ) and in cancer ( Figure 3E ) ( Szklarczyk et al. 2023 ), models for foraging networks in primates ( Figure 3F ) (De la Fuente et al. 2022 ), diagrams for biochemical reaction chains ( Figure 3G ) ( Kanehisa and Goto 2000 ), social network tutorials from SocNetV ( Figure 3H ) (“Free and Open-Source Tool for Social Network Analysis,” n.d.) , conceptual charts for train lines and stations ( Figure 3I ), and geographic maps with non-Cartesian geometries ( Figure 3J ) ( Met Office 2010 - 2015 ). Moreover, because of its tight integration with Matplotlib, iplotx ’s visualisation can be used to encode generic quantitative data, extending all styling options to any chart type ( Figure 3K ). Figure 3. Applications across disciplines. A) Knuth’s network of distances between US cities, a classic example of network visualisation ( Knuth 1993 ). B) Molecular ecological network of food sources ( Meyer et al. 2020 ). C) Visualisation of a network of financial institutions ( Schweitzer et al. 2009 ). D) Protein-protein interaction network in Escherichia coli from the STRING database, with nodes (protein) coloured by their degree ( Szklarczyk et al. 2023 ). E) Protein-protein interaction network of breast cancer genes ( Szklarczyk et al. 2023 ). F) Foraging strategy models in a cooperatively breeding primate (De la Fuente et al. 2022 ). G) Central part of the citric acid cycle that underpins cellular respiration ( Kanehisa and Goto 2000 ). H) Example of social network from SocNetV (“Free and Open-Source Tool for Social Network Analysis,” n.d .). I) Example of a transportation network diagram with three train lines. J) Network of African cities onto a Cartopy geographic projection ( Met Office 2010 - 2015 ). K) Example of an iplotx network visualisation interpreted as a quantitative data chart. Discussion The landscape of network and tree analysis software tools is rich and diverse, catering to different types of users that require speed ( Antonov et al. 2023 ), a simple programmatic interface ( Hagberg, Schult, and Swart 2008 ), or complex GUIs ( Shannon et al. 2003 ). This diversity, however, also causes feature and format fragmentation. iplotx is a direct attempt to counter this phenomenon and thereby equip many existing network and tree analysis packages with a powerful visualisation toolkit. While this package focuses on Python, ggraph follows similar principles in R ( Pedersen 2025 ), in particular the consideration of analysis, layout, and visualisation of networks as distinct tasks. Compared to ggraph , iplotx includes some additional features such as three-dimensional visualisations, internal styles, a library of arrow and vertex shapes, and dedicated tree features such as cascades and layouts. Ggtree is an R library that specialises in tree layout and visualisation ( Yu et al. 2017 ) and does not support general networks. Unlike ggtree , iplotx does not include any functions to directly read trees or network statically stored in files or the web. This is a design choice as many of the tree analysis libraries include parsers for many file formats already ( Huttley et al. 2025 ; Huerta-Cepas, Serra, and Bork 2016 ; Cock et al. 2009 ; Team 2020 ; Moreno, Holder, and Sukumaran 2024 ) and there is no reason for iplotx to duplicate this functionality. The central tenet of iplotx , to democratise rich customisation options to all input packages, also represents a form of architectural robustness. Whether or not each individual analysis package is maintained in the future, users will be able to build network visualisations with the exact same appearance via another package. At the same time, reliance on a high-level rendering backend - Matplotlib ( Hunter 2007 ) - greatly expands the compatibility of the visualisations with dozens of other packages that use the same framework (e.g. ( Waskom et al. 2014 ; Virtanen et al. 2020 )). This design choice, which echoes ggraph ( Pedersen 2025 ) but differs from other packages ( Huerta-Cepas, Serra, and Bork 2016 ; Peixoto 2017 ), facilitates onboarding for a majority of users unfamiliar with low-lever renderers such as Cairo and GTK. In conclusion, iplotx is a universal network and tree visualisation framework that democratises a large set of styling options to all network analysis packages in Python. Software availability statement Source code available from: https://github.com/fabilab/iplotx . Archived software available from: https://doi.org/10.5281/zenodo.17585402 . ( Fabio Zanini, & Matthew Andres Moreno. (2025) . fabilab/iplotx: v1.5.1 (1.5.1). Zenodo.) License: MIT. Acknowledgements I would like to thank Thomas Caswell ( Matplotlib ), Dan Schult and the NetworkX development team, Tamás Nepusz and Vincent Traag ( igraph ), Gavin Huttley ( cogent3 ), and Peter Cock ( Biopython ) for scientific discussions and design suggestions. iplotx was initially designed during a short residency at CIBIO, University of Trento, Italy graciously hosted by Maria Caterina Mione and Nicola Segata. References Antonov M, Csárdi G, Horvát S, et al. : Igraph Enables Fast and Robust Network Analysis across Programming Languages. arXiv [cs.SI]. arXiv. 2023. Reference Source Cock PJA, Antao T, Chang JT, et al. : Biopython: Freely Available Python Tools for Computational Molecular Biology and Bioinformatics. Bioinformatics. 2009; 25 (11): 1422–1423. PubMed Abstract | Publisher Full Text | Free Full Text la Fuente D , Fernanda M, Sueur C, et al. : Foraging Networks and Social Tolerance in a Cooperatively Breeding Primate (Callithrix Jacchus). J. Anim. Ecol. 2022; 91 (1): 138–153. PubMed Abstract | Publisher Full Text Drummond AJ, Rambaut A: BEAST: Bayesian Evolutionary Analysis by Sampling Trees. BMC Evol. Biol. 2007; 7 (1): 214. PubMed Abstract | Publisher Full Text | Free Full Text Free and Open-Source Tool for Social Network Analysis: Social Network Visualizer.n.d. Accessed October 30, 2025. 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Publisher Full Text Moreno MA, Holder MT, Sukumaran J: DendroPy 5: A Mature Python Library for Phylogenetic Computing. Journal of Open Source Software. 2024; 9 (101): 6943. Publisher Full Text Krivitsky PN, Handcock MS, Hunter DR, et al. : Statnet: Tools for the Statistical Modeling of Network Data. 2003/2024. Reference Source Pedersen TL: Ggraph.2025. Reference Source Peixoto TP: The Graph-Tool Python Library.2017. Publisher Full Text Plotly Technologies Inc: Collaborative Data Science. Collaborative Data Science. Montréal, QC: Plotly Technologies Inc.; 2015; 2015 . . Reference Source Redelings BD, Holder MT: A Supertree Pipeline for Summarizing Phylogenetic and Taxonomic Information for Millions of Species. PeerJ. 2017; 5 (March): e3058. PubMed Abstract | Publisher Full Text | Free Full Text Sagulenko P, Puller V, Neher RA: TreeTime: Maximum-Likelihood Phylodynamic Analysis. Virus Evolution. 2018; 4 (1): vex042. PubMed Abstract | Publisher Full Text Schweitzer F, Fagiolo G, Sornette D, et al. : Economic Networks: The New Challenges. Science (New York, N.Y.). 2009; 325 (5939): 422–425. Publisher Full Text Shannon P, Markiel A, Ozier O, et al. : Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003; 13 (11): 2498–2504. PubMed Abstract | Publisher Full Text | Free Full Text Szklarczyk D, Kirsch R, Koutrouli M, et al. : The STRING Database in 2023: Protein-Protein Association Networks and Functional Enrichment Analyses for Any Sequenced Genome of Interest. Nucleic Acids Res. 2023; 51 (D1): D638–D646. PubMed Abstract | Publisher Full Text | Free Full Text Team, The Scikit-Bio Development: Scikit-Bio: A Bioinformatics Library for Data Scientists, Students, and Developers (version 0.5.5).2020. Reference Source Virtanen P, Gommers R, Oliphant TE, et al. : SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods. 2020; 17 (3): 261–272. PubMed Abstract | Publisher Full Text | Free Full Text Waskom M, Botvinnik O, Hobson P, et al. : Seaborn: v0.5.0 (November 2014).2014. Publisher Full Text Yu G, Smith DK, Zhu H, et al. : Ggtree: An R Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data. Methods Ecol. Evol. 2017; 8 (1): 28–36. Publisher Full Text Zanini F, Moreno MA: fabilab/iplotx: v1.5.1 (1.5.1). Zenodo. 2025. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Dec 2025 ADD YOUR COMMENT Comment Author details Author details 1 School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, 2052, Australia 2 Ecology & Evolution Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia 3 UNSW Cellular Genomics Futures Institute, UNSW, NSW, 2052, Australia Fabio Zanini Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 09 Dec 2025, 14:1377 https://doi.org/10.12688/f1000research.173131.1 Copyright © 2025 Zanini F. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Zanini F. A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.12688/f1000research.173131.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 09 Dec 2025 Views 0 Cite How to cite this report: Velásquez-Zapata V. Reviewer Report For: A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.5256/f1000research.190914.r445964 ) The direct URL for this report is: https://f1000research.com/articles/14-1377/v1#referee-response-445964 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 26 Feb 2026 Valeria Velásquez-Zapata , GreenLight Biosciences, Durham, NC, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.190914.r445964 This article presents iplotx, a universal Python package designed to provide visualization of networks and trees compatible with several libraries. The rationale for the software are clearly explained, addressing a real need for interoperability and customizability that existing tools ... Continue reading READ ALL This article presents iplotx, a universal Python package designed to provide visualization of networks and trees compatible with several libraries. The rationale for the software are clearly explained, addressing a real need for interoperability and customizability that existing tools lack. The paper thoroughly documents the design choices, integrations, and support for a broad range of input formats and outputs. Performance benchmarks and extensive examples across diverse scientific domains. The author shows that iplotx can deliver identical visualizations regardless of input source. The demonstration of scalability and cross-discipline applicability reinforce the claim that iplotx is a robust and broadly useful solution. When checking for software sources and documentation, I could verify the resources offer a aseries of examples for network visualization and on how to install and use the tool. Overall, the article is well written and the software a valuable addition to the Python ecosystem for network visualization. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: BIoinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Velásquez-Zapata V. Reviewer Report For: A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.5256/f1000research.190914.r445964 ) The direct URL for this report is: https://f1000research.com/articles/14-1377/v1#referee-response-445964 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Tyson AL. Reviewer Report For: A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.5256/f1000research.190914.r451458 ) The direct URL for this report is: https://f1000research.com/articles/14-1377/v1#referee-response-451458 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Feb 2026 Adam L Tyson , University College London, London, UK Approved VIEWS 0 https://doi.org/10.5256/f1000research.190914.r451458 This paper outlines iplotx, a general purpose network plotting library. This software presents a unified interface for plotting networks generated by different open-source tools. This is a simple tool, but it fulfils a real need, and does it well. The ... Continue reading READ ALL This paper outlines iplotx, a general purpose network plotting library. This software presents a unified interface for plotting networks generated by different open-source tools. This is a simple tool, but it fulfils a real need, and does it well. The paper is nicely written, and the software itself follows all software development best practices, with linting, testing, type hints, docstrings and continuous integration set up etc. The software is freely available on PyPI and is easy to install (at least on this reviewers machine). The documentation is particularly good, with reference API documentation, details on how to customise the visualisation, along with an extensive gallery of examples. There are limited details about the code and methods within the paper, however, there is plenty of detail within the online documentation and within the code itself (where it should be!). There are a few minor issues within the paper: 1. It is stated that iplotx “guarantees” that networks will be plotted identically regardless of the source software. “Guarantee” is a strong term, I would either provide evidence that this is always true, or just soften the language - maybe “ensures”? 2. Fig 1 and Fig 2 seem to be missing references in the caption. 3. In the results section, it should read “covers all element types of the visualisation”, i.e. make “elements” singular. 4. Only part of “De la Fuente et al. 2022” links to the reference. Other than these, the tool and the paper are well written, and seem to address a real need in an efficient and well-thought out manner. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Software engineering, Python, neuroscience, image analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Tyson AL. Reviewer Report For: A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.5256/f1000research.190914.r451458 ) The direct URL for this report is: https://f1000research.com/articles/14-1377/v1#referee-response-451458 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Dec 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 09 Dec 25 read read Adam L Tyson , University College London, London, UK Valeria Velásquez-Zapata , GreenLight Biosciences, Durham, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Velásquez-Zapata V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 26 Feb 2026 | for Version 1 Valeria Velásquez-Zapata , GreenLight Biosciences, Durham, NC, USA 0 Views copyright © 2026 Velásquez-Zapata V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article presents iplotx, a universal Python package designed to provide visualization of networks and trees compatible with several libraries. The rationale for the software are clearly explained, addressing a real need for interoperability and customizability that existing tools lack. The paper thoroughly documents the design choices, integrations, and support for a broad range of input formats and outputs. Performance benchmarks and extensive examples across diverse scientific domains. The author shows that iplotx can deliver identical visualizations regardless of input source. The demonstration of scalability and cross-discipline applicability reinforce the claim that iplotx is a robust and broadly useful solution. When checking for software sources and documentation, I could verify the resources offer a aseries of examples for network visualization and on how to install and use the tool. Overall, the article is well written and the software a valuable addition to the Python ecosystem for network visualization. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise BIoinformatics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Velásquez-Zapata V. Peer Review Report For: A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.5256/f1000research.190914.r445964) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1377/v1#referee-response-445964 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Tyson A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Feb 2026 | for Version 1 Adam L Tyson , University College London, London, UK 0 Views copyright © 2026 Tyson A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper outlines iplotx, a general purpose network plotting library. This software presents a unified interface for plotting networks generated by different open-source tools. This is a simple tool, but it fulfils a real need, and does it well. The paper is nicely written, and the software itself follows all software development best practices, with linting, testing, type hints, docstrings and continuous integration set up etc. The software is freely available on PyPI and is easy to install (at least on this reviewers machine). The documentation is particularly good, with reference API documentation, details on how to customise the visualisation, along with an extensive gallery of examples. There are limited details about the code and methods within the paper, however, there is plenty of detail within the online documentation and within the code itself (where it should be!). There are a few minor issues within the paper: 1. It is stated that iplotx “guarantees” that networks will be plotted identically regardless of the source software. “Guarantee” is a strong term, I would either provide evidence that this is always true, or just soften the language - maybe “ensures”? 2. Fig 1 and Fig 2 seem to be missing references in the caption. 3. In the results section, it should read “covers all element types of the visualisation”, i.e. make “elements” singular. 4. Only part of “De la Fuente et al. 2022” links to the reference. Other than these, the tool and the paper are well written, and seem to address a real need in an efficient and well-thought out manner. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Software engineering, Python, neuroscience, image analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Tyson AL. Peer Review Report For: A universal tool for visualisation of networks and trees in Python [version 1; peer review: 2 approved] . F1000Research 2025, 14 :1377 ( https://doi.org/10.5256/f1000research.190914.r451458) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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