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  Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features

Döring, M., Kreer, C., Lehnen, N., Klein, F., & Pfeifer, N. (2019). Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features. Scientific Reports, 9: 10748. doi:10.1038/s41598-019-47173-w.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0004-5F56-2 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0004-830F-8
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© The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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 Urheber:
Döring, Matthias1, Autor           
Kreer, Christoph2, Autor
Lehnen, Nathalie2, Autor
Klein, Florian2, Autor
Pfeifer, Nico1, Autor           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2External Organizations, ou_persistent22              

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 Zusammenfassung: Successful primer design for polymerase chain reaction (PCR) hinges on the ability to identify primers
that efciently amplify template sequences. Here, we generated a novel Taq PCR data set that reports
the amplifcation status for pairs of primers and templates from a reference set of 47 immunoglobulin
heavy chain variable sequences and 20 primers. Using logistic regression, we developed TMM, a model
for predicting whether a primer amplifes a template given their nucleotide sequences. The model
suggests that the free energy of annealing, ΔG, is the key driver of amplifcation (p=7.35e-12) and that
3′ mismatches should be considered in dependence on ΔG and the mismatch closest to the 3′ terminus
(p=1.67e-05). We validated TMM by comparing its estimates with those from the thermodynamic
model of DECIPHER (DE) and a model based solely on the free energy of annealing (FE). TMM
outperformed the other approaches in terms of the area under the receiver operating characteristic
curve (TMM: 0.953, FE: 0.941, DE: 0.896). TMM can improve primer design and is freely available via
openPrimeR (http://openPrimeR.mpi-inf.mpg.de).

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Sprache(n): eng - English
 Datum: 2018-11-292019-06-102019-07-24
 Publikationsstatus: Online veröffentlicht
 Seiten: 11 p.
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: DöringPfeiferModel
DOI: 10.1038/s41598-019-47173-w
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Titel: Scientific Reports
  Kurztitel : Sci. Rep.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: London, UK : Nature Publishing Group
Seiten: 11 p. Band / Heft: 9 Artikelnummer: 10748 Start- / Endseite: - Identifikator: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322