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dc.contributor.author
Poovathingal, Suresh Kumar
dc.contributor.author
Gruber, Jan
dc.contributor.author
Ng, Li Fang
dc.contributor.author
Halliwell, Barry
dc.contributor.author
Gunawan, Rudiyanto
dc.date.accessioned
2019-04-02T12:13:39Z
dc.date.available
2017-06-09T17:20:24Z
dc.date.available
2019-04-02T12:13:39Z
dc.date.issued
2012-03
dc.identifier.other
10.1093/nar/gkr1221
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/41592
dc.identifier.doi
10.3929/ethz-b-000041592
dc.description.abstract
The ‘Random Mutation Capture’ assay allows for the sensitive quantitation of DNA mutations at extremely low mutation frequencies. This method is based on PCR detection of mutations that render the mutated target sequence resistant to restriction enzyme digestion. The original protocol prescribes an end-point dilution to about 0.1 mutant DNA molecules per PCR well, such that the mutation burden can be simply calculated by counting the number of amplified PCR wells. However, the statistical aspects associated with the single molecular nature of this protocol and several other molecular approaches relying on binary (on/off) output can significantly affect the quantification accuracy, and this issue has so far been ignored. The present work proposes a design of experiment (DoE) using statistical modeling and Monte Carlo simulations to obtain a statistically optimal sampling protocol, one that minimizes the coefficient of variance in the measurement estimates. Here, the DoE prescribed a dilution factor at about 1.6 mutant molecules per well. Theoretical results and experimental validation revealed an up to 10-fold improvement in the information obtained per PCR well, i.e. the optimal protocol achieves the same coefficient of variation using one-tenth the number of wells used in the original assay. Additionally, this optimization equally applies to any method that relies on binary detection of a small number of templates.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Oxford University Press
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/3.0/
dc.title
Maximizing signal-to-noise ratio in the random mutation capture assay
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial 3.0 Unported
dc.date.published
2011-12-16
ethz.journal.title
Nucleic acids research
ethz.journal.volume
40
en_US
ethz.journal.issue
5
en_US
ethz.pages.start
e35
en_US
ethz.size
9 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
000038633
ethz.publication.place
Oxford
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03898 - Gunawan, Rudiyanto (ehemalig)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03898 - Gunawan, Rudiyanto (ehemalig)
ethz.date.deposited
2017-06-09T17:20:29Z
ethz.source
ECIT
ethz.identifier.importid
imp59364ea9d636a89320
ethz.ecitpid
pub:69498
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T22:27:43Z
ethz.rosetta.lastUpdated
2019-04-02T12:13:52Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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