Literature DB >> 29029174

Statistical Package for Growth Rates Made Easy.

Portia Mira1, Miriam Barlow2, Juan C Meza2, Barry G Hall3.   

Abstract

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.
© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  bootstrap; fitness; fitness assay; growth rates; statistics

Mesh:

Year:  2017        PMID: 29029174      PMCID: PMC5850790          DOI: 10.1093/molbev/msx255

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  10 in total

1.  Darwinian evolution can follow only very few mutational paths to fitter proteins.

Authors:  Daniel M Weinreich; Nigel F Delaney; Mark A Depristo; Daniel L Hartl
Journal:  Science       Date:  2006-04-07       Impact factor: 47.728

2.  Growth rates made easy.

Authors:  Barry G Hall; Hande Acar; Anna Nandipati; Miriam Barlow
Journal:  Mol Biol Evol       Date:  2013-10-28       Impact factor: 16.240

Review 3.  Experimental evolution and the dynamics of genomic mutation rate modifiers.

Authors:  Y Raynes; P D Sniegowski
Journal:  Heredity (Edinb)       Date:  2014-05-21       Impact factor: 3.821

Review 4.  Predictive microbiology.

Authors:  T Ross; T A McMeekin
Journal:  Int J Food Microbiol       Date:  1994-11       Impact factor: 5.277

5.  Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change.

Authors:  Portia M Mira; Juan C Meza; Anna Nandipati; Miriam Barlow
Journal:  Mol Biol Evol       Date:  2015-06-25       Impact factor: 16.240

6.  Evolution of TEM-type extended-spectrum beta-lactamases in clinical Enterobacteriaceae strains in Poland.

Authors:  Anna Baraniak; Janusz Fiett; Agnieszka Mrówka; Jarosław Walory; Waleria Hryniewicz; Marek Gniadkowski
Journal:  Antimicrob Agents Chemother       Date:  2005-05       Impact factor: 5.191

Review 7.  Bacterial growth: global effects on gene expression, growth feedback and proteome partition.

Authors:  Stefan Klumpp; Terence Hwa
Journal:  Curr Opin Biotechnol       Date:  2014-02-02       Impact factor: 9.740

8.  Rational design of antibiotic treatment plans: a treatment strategy for managing evolution and reversing resistance.

Authors:  Portia M Mira; Kristina Crona; Devin Greene; Juan C Meza; Bernd Sturmfels; Miriam Barlow
Journal:  PLoS One       Date:  2015-05-06       Impact factor: 3.240

9.  Protocols and programs for high-throughput growth and aging phenotyping in yeast.

Authors:  Paul P Jung; Nils Christian; Daniel P Kay; Alexander Skupin; Carole L Linster
Journal:  PLoS One       Date:  2015-03-30       Impact factor: 3.240

10.  Population dynamics of a Salmonella lytic phage and its host: implications of the host bacterial growth rate in modelling.

Authors:  Sílvio B Santos; Carla Carvalho; Joana Azeredo; Eugénio C Ferreira
Journal:  PLoS One       Date:  2014-07-22       Impact factor: 3.240

  10 in total
  4 in total

1.  Exploiting evolutionary trade-offs for posttreatment management of drug-resistant populations.

Authors:  Sergey V Melnikov; David L Stevens; Xian Fu; Hui Si Kwok; Jin-Tao Zhang; Yue Shen; Jeffery Sabina; Kevin Lee; Harry Lee; Dieter Söll
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-13       Impact factor: 11.205

2.  Clinical Mutations That Partially Activate the Stringent Response Confer Multidrug Tolerance in Staphylococcus aureus.

Authors:  Duncan Bryson; Andrew G Hettle; Alisdair B Boraston; Joanne K Hobbs
Journal:  Antimicrob Agents Chemother       Date:  2020-02-21       Impact factor: 5.191

3.  Adaptive Processes Change as Multiple Functions Evolve.

Authors:  Portia M Mira; Bjørn Østman; Candace Guzman-Cole; Suzanne Sindi; Miriam Barlow
Journal:  Antimicrob Agents Chemother       Date:  2021-03-18       Impact factor: 5.191

4.  Estimating microbial population data from optical density.

Authors:  Portia Mira; Pamela Yeh; Barry G Hall
Journal:  PLoS One       Date:  2022-10-13       Impact factor: 3.752

  4 in total

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