gcplyr: an R package for microbial growth curve data analysis

Background Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights. Results To addr...

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Veröffentlicht in:	BMC bioinformatics Jg. 25; H. 1; S. 232 - 10
1. Verfasser:	Blazanin, Michael
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 09.07.2024 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Applications software Bacteria Bioinformatics Biomedical and Life Sciences Carrying capacity Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data Analysis Design Diauxie Doubling time Electronic data processing Growth Growth curve Growth curves Growth rate Imports Lag time Life Sciences Mathematics Metadata Methods Microarrays Microbiological research Microbiology Microorganisms Population growth R (Programming language) Software Statistical analysis Technology application Visualization Visualization (Computers) United States Carrying capacity Microbiology Growth rate Doubling time Growth Lag time Software Tidy data Modeling Growth curve
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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