The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Statistical Package for Growth Rates Made Easy
Open accessAbstract
Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.
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“This difference is important to consider when performing experiments that depend on the number of cellular divisions or cells present, such as cellular communication [12, 13] and antibiotic susceptibility [14–17] and biofilms [18].”
From Estimating microbial population data from optical density (2022) · cites this paper as [16]
Citations
Cites
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins
Weinreich, D. M., Delaney, N. F., DePristo, M. A., & Hartl, D. L. (2006) · Science · doi:10.1126/science.1123539 · Cited by 1,418
Hall, B. G., Acar, H., Nandipati, A., & Barlow, M. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/mst187 · Cited by 507
Self-citationRoss, T., & McMeekin, T. (1994) · International Journal of Food Microbiology · doi:10.1016/0168-1605(94)90155-4 · Cited by 237
Bacterial growth: global effects on gene expression, growth feedback and proteome partition
Klumpp, S., & Hwa, T. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.01.001 · Cited by 218
Protocols and Programs for High-Throughput Growth and Aging Phenotyping in Yeast
Jung, P. P., Christian, N., Kay, D. P., Skupin, A., & Linster, C. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119807 · Cited by 84
Santos, S. R. B., Carvalho, C. M., Azeredo, J., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102507 · Cited by 73
Mira, P., Crona, K. A., Greene, D., Meza, J. C., Sturmfels, B., & Barlow, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122283 · Cited by 70
Self-citationExperimental evolution and the dynamics of genomic mutation rate modifiers
Raynes, Y., & Sniegowski, P. (2014) · Heredity · doi:10.1038/hdy.2014.49 · Cited by 65
Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change
Mira, P., Meza, J. C., Nandipati, A., & Barlow, M. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv146 · Cited by 53
Self-citationCavallo, J. A., Strumia, M. C., & Gomez, C. G. (2014) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2014.03.021 · Cited by 36
Baraniak, A., Fiett, J., Mrówka, A., et al. (2005) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.49.5.1872-1880.2005 · Cited by 34
Cited by
Sorted by citations
Estimating microbial population data from optical density
Mira, P., Yeh, P., & Hall, B. G. (2022) · PLOS ONE · doi:10.1371/journal.pone.0276040 · Cited by 175
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Metadata
- Authors
- Mira, P., Barlow, M., Meza, J. C., & Hall, B. G.
- Affiliations
- University of California, Merced (US)
- Bellingham Research Institute, Portland, OR
- Corresponding author
- Mira, Portia
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-09-26
- Volume
- 34
- Issue
- 12
- Pages
- 3303-3309
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- PMC
- PMC5850790
- OpenAlex
- W2760415306
- Cited by
- 13
- References
- 12
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2022: 3 · 2021: 3 · 2020: 1 · 2019: 1
- FWCI
- 0.36
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Molecular Biology and Evolution (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (01-R41-AI122740_01A1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- bacterial growth rate, fitness measurement, statistical software, bootstrapping, growth rate variation, antibiotic resistance, fitness landscape
- MeSH terms
- Bacteria, Biometry, Software, Colony Count, Microbial, Reproducibility of Results, Microbial Viability
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