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ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics

Plaque assay has been used for a long time to determine infectious titers and characterize prokaryotic and eukaryotic viruses forming plaques. Indeed, plaque morphology and dimensions can provide information regarding the replication kinetics and the virulence of a particular virus. In this work, we...

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Detalles Bibliográficos
Autores principales: Cacciabue, Marco, Currá, Anabella, Gismondi, Maria I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764358/
https://www.ncbi.nlm.nih.gov/pubmed/31579606
http://dx.doi.org/10.7717/peerj.7729
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author Cacciabue, Marco
Currá, Anabella
Gismondi, Maria I.
author_facet Cacciabue, Marco
Currá, Anabella
Gismondi, Maria I.
author_sort Cacciabue, Marco
collection PubMed
description Plaque assay has been used for a long time to determine infectious titers and characterize prokaryotic and eukaryotic viruses forming plaques. Indeed, plaque morphology and dimensions can provide information regarding the replication kinetics and the virulence of a particular virus. In this work, we present ViralPlaque, a fast, open-source and versatile ImageJ macro for the automated determination of viral plaque dimensions from digital images. Also, a machine learning plugin is integrated in the analysis algorithm for adaptation of ViralPlaque to the user’s needs and experimental conditions. A high correlation between manual and automated measurements of plaque dimensions was demonstrated. This macro will facilitate reliable and reproducible characterization of cytolytic viruses with an increased processing speed.
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spelling pubmed-67643582019-10-02 ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics Cacciabue, Marco Currá, Anabella Gismondi, Maria I. PeerJ Bioinformatics Plaque assay has been used for a long time to determine infectious titers and characterize prokaryotic and eukaryotic viruses forming plaques. Indeed, plaque morphology and dimensions can provide information regarding the replication kinetics and the virulence of a particular virus. In this work, we present ViralPlaque, a fast, open-source and versatile ImageJ macro for the automated determination of viral plaque dimensions from digital images. Also, a machine learning plugin is integrated in the analysis algorithm for adaptation of ViralPlaque to the user’s needs and experimental conditions. A high correlation between manual and automated measurements of plaque dimensions was demonstrated. This macro will facilitate reliable and reproducible characterization of cytolytic viruses with an increased processing speed. PeerJ Inc. 2019-09-24 /pmc/articles/PMC6764358/ /pubmed/31579606 http://dx.doi.org/10.7717/peerj.7729 Text en ©2019 Cacciabue et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Cacciabue, Marco
Currá, Anabella
Gismondi, Maria I.
ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics
title ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics
title_full ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics
title_fullStr ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics
title_full_unstemmed ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics
title_short ViralPlaque: a Fiji macro for automated assessment of viral plaque statistics
title_sort viralplaque: a fiji macro for automated assessment of viral plaque statistics
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764358/
https://www.ncbi.nlm.nih.gov/pubmed/31579606
http://dx.doi.org/10.7717/peerj.7729
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