ArticleBMC bioinformatics2024
gcplyr: an R package for microbial growth curve data analysis.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Genomic signatures of dairy adaptation inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Comparative genomic analysis ofMicrobiology spectrum · 2026Article
- Predictable shifts in microbial species composition lead to community-wide robustness to environmental stress.Nature microbiology · 2026Article
- Extended lag phases and variable antibiotic tolerance in β-lactam-induced small colony variants of Enterococcus faecalis.BMC microbiology · 2026Article
- SubCom analysis: dissecting microbial community functions into taxon-specific contributions and interaction contexts.Microbiome · 2026Article
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- Influence of Ploidy and Genetic Background on Stress Tolerance of Intraspecific Yeast Hybrids.Microbial biotechnology · 2026Article
- Mutations in filamentous bacteriophages spark eco-evolutionary feedbacks in Pseudomonas aeruginosa.The ISME journal · 2026Article
- Phage receptor specificity drives cross-resistance patterns and governs fitness trade-offs during sequential resistance acquisition in Salmonella.The ISME journal · 2026Article
- Adaptation of Fe-S Cluster Assembly to Rising OResearch square · 2026Article
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- Hurdles to horizontal gene transfer: species-specific effects of synonymous variation and plasmid copy number determine antibiotic resistance phenotype.Microbiology (Reading, England) · 2026Article
- OmniBio: an easy-to-use web app for kinetic growth parameter calculation from microplate reader data.Bioinformatics advances · 2026Article
- Human milk oligosaccharide metabolism and antibiotic resistance in early gut colonizers: insights from bifidobacteria and lactobacilli in the maternal-infant microbiome.Gut microbes · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCharacterization 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.
resultsTo address this need, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in common tabular formats, and reshapes it under a tidy framework that is flexible and extendable, enabling users to design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free (non-parametric) analyses. These analyses do not require mathematical assumptions about microbial growth dynamics, and gcplyr is able to extract a broad range of important traits, including growth rate, doubling time, lag time, maximum density and carrying capacity, diauxie, area under the curve, extinction time, and more.
conclusionsgcplyr makes scripted analyses of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.