Cited by 28Evolution and Genetic DynamicsCarcinogens and Genotoxicity AssessmentBacterial Genetics and Biotechnology

A Simple Formula for Obtaining Markedly Improved Mutation Rate Estimates

Open access
Gerrish, Philip·2008·Genetics

Abstract

In previous work by M. E. Jones and colleagues, it was shown that mutation rate estimates can be improved and corresponding confidence intervals tightened by following a very easy modification of the standard fluctuation assay: cultures are grown to a larger-than-usual final density, and mutants are screened for in only a fraction of the culture. Surprisingly, this very promising development has received limited attention, perhaps because there has been no efficient way to generate the predicted mutant distribution to obtain non-moment-based estimates of the mutation rate. Here, the improved fluctuation assay discovered by Jones and colleagues is made amenable to quantile-based, likelihood, and other Bayesian methods by a simple recursion formula that efficiently generates the entire mutant distribution after growth and dilution. This formula makes possible a further protocol improvement: grow cultures as large as is experimentally possible and severely dilute before plating to obtain easily countable numbers of mutants. A preliminary look at likelihood surfaces suggests that this easy protocol adjustment gives markedly improved mutation rate estimates and confidence intervals.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1534/genetics.108.091777

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The simplest way to model phenotypic lag, considered by Armitage and further developed by Angerer, is to assume that a certain amount of time since the mutational event must pass for the cell to express the mutant phenotype.

    “The simplest way to model phenotypic lag, which was considered by Armitage and further developed by Angerer, is to assume that a certain amount of time (say, t lag ) since mutational event must pass for the cell to be able to express mutant phenotype [35].”

    From Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur) (2023) · cites this paper as [35]

    The Abstract discusses when mutations occur and whether mutants form observable colonies; the Discussion lists simplifying assumptions of the model. Neither passage addresses a fixed delay before mutant phenotype expression or attributes such a model to Armitage and Angerer (searched: phenotypic lag, Angerer, delayed expression). (LLM, conf=0.74)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Bailey, N. T. J., 1964 The Elements of Stochastic Processes With Applica- tions to the Natural Sciences. Wiley, New York.
  2. Bartlett, M. S., 1978 An Introduction to Stochastic Processes. Cam- bridge University Press, London.
  3. Kendal, W. S., and P. Frost, 1988 Pitfalls and practice of Luria- Delbru ¨ck fluctuation analysis: a review. Cancer Res. 48: 1060-1065.
  4. Wolfram Research, 2005 Mathematica Edition, Version 5.

Cited by

Sorted by citations

Authors
Gerrish, P.
Affiliations
University of New Mexico (US)
Corresponding author
Gerrish, Philip
Venue
Genetics
Published
2008-11-01
Volume
180
Issue
3
Pages
1773-1778
ISSN
  • 0016-6731 (ISSN-L)
  • 1943-2631
OpenAlex
W2120086627
Cited by
28
References
2
Citations by year
2026: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 4
FWCI
0.57
Type
journal article
Language
en
Open access
Locations
  • Genetics (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute of General Medical Sciences (R01GM079483)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Carcinogens and Genotoxicity Assessment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
Keywords
mutation rate estimation, recursion formula, likelihood estimation, Bayesian estimation, confidence intervals
MeSH terms
Animals, Computer Simulation, Humans, Models, Genetic, Mutation, Genetic Variation, Models, Statistical, Likelihood Functions

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review