PyPlaque is an open-source python package for phenotypic analysis of virus plaque assays

Virological plaque assays are the primary method for quantifying infectious particles in a suspension, achieved by incubating a serial dilution of the virus with a monolayer of indicator cells. Existing software tools for quantification of plaque assay images lack modularity, show measurements disag...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 36187 - 18
Main Authors: De, Trina, Andriasyan, Vardan, Yakimovich, Artur
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 16.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
631/114
631/326/596
Data analysis
Data science
Experimental design
Experiments
Flexibility
Humanities and Social Sciences
Humans
Infections
Microscopy
multidisciplinary
Object oriented programming
Open source software
Pathogens
Phenotype
Phenotypes
Plaque assay
Python
Science
Science (multidisciplinary)
Software
Viral Plaque Assay - methods
Viruses
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Virological plaque assays are the primary method for quantifying infectious particles in a suspension, achieved by incubating a serial dilution of the virus with a monolayer of indicator cells. Existing software tools for quantification of plaque assay images lack modularity, show measurements disagreement or are closed-source - a common hurdle in bioimage analysis. We introduce PyPlaque, an open-source Python package focusing on flexibility and modularity rather than a bulky graphic user interface. Unlike previous methods, an abstracted architecture using object-oriented programming allows accommodation of various experimental containers and specimen carriers as data structures while focusing on phenotype-specific information. Aligned with the logical flow of experimental design and desired quantifications, it delivers insights at multiple granularity levels, facilitating detailed analysis. We demonstrate how this approach allows to focus on alleviating the disagreement in measurements. Furthermore, similar design is generalisable to diverse datasets in various biological contexts that fit our structural paradigm.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-20075-w