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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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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-5F56-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-830F-8
Genre: Journal Article

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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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 Creators:
Döring, Matthias1, Author              
Kreer, Christoph2, Author
Lehnen, Nathalie2, Author
Klein, Florian2, Author
Pfeifer, Nico1, Author              
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2External Organizations, ou_persistent22              

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 Abstract: 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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Language(s): eng - English
 Dates: 2018-11-292019-06-102019-07-24
 Publication Status: Published online
 Pages: 11 p.
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: DöringPfeiferModel
DOI: 10.1038/s41598-019-47173-w
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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: 11 p. Volume / Issue: 9 Sequence Number: 10748 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322