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Microbial lag calculator: A shiny‐based application and an R package for calculating the duration of microbial lag phase
by
Opalek, Monika
, Smug, Bogna J.
, Necki, Maks
, Wloch‐Salamon, Dominika
in
Applications programs
/ bacterial growth models
/ Decision trees
/ fitness
/ Lag phase
/ lag phase length
/ lag time
/ Mathematical analysis
/ microbial growth curve
/ Microorganisms
/ Parameters
2024
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Microbial lag calculator: A shiny‐based application and an R package for calculating the duration of microbial lag phase
by
Opalek, Monika
, Smug, Bogna J.
, Necki, Maks
, Wloch‐Salamon, Dominika
in
Applications programs
/ bacterial growth models
/ Decision trees
/ fitness
/ Lag phase
/ lag phase length
/ lag time
/ Mathematical analysis
/ microbial growth curve
/ Microorganisms
/ Parameters
2024
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Do you wish to request the book?
Microbial lag calculator: A shiny‐based application and an R package for calculating the duration of microbial lag phase
by
Opalek, Monika
, Smug, Bogna J.
, Necki, Maks
, Wloch‐Salamon, Dominika
in
Applications programs
/ bacterial growth models
/ Decision trees
/ fitness
/ Lag phase
/ lag phase length
/ lag time
/ Mathematical analysis
/ microbial growth curve
/ Microorganisms
/ Parameters
2024
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Microbial lag calculator: A shiny‐based application and an R package for calculating the duration of microbial lag phase
Journal Article
Microbial lag calculator: A shiny‐based application and an R package for calculating the duration of microbial lag phase
2024
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Overview
The duration of lag phase can be used as an organismal fitness marker; however, it is often underreported as its estimation may be challenging and method and parameters dependent. Moreover, there are no publicly available tools to calculate lag duration by different methods. We developed a shiny‐based web application (https://microbialgrowth.shinyapps.io/lag_calulator/) where the lag duration can be calculated based on the user‐specified growth curve data, and for various explicitly specified methods, parameters and data preprocessing techniques. Additionally, we release an R package ‘miLAG’ that can be further customised and developed. We also describe in short the assumptions, advantages and disadvantages of the most popular lag calculation methods and propose a decision tree to choose a method most suited to one's data. Finally, we show some working examples of how to calculate lag duration using our shiny server.
Publisher
John Wiley & Sons, Inc,Wiley
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