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Journal Article

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

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Döring,  Matthias
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Pfeifer,  Nico
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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s41598-019-47173-w.pdf
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Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0004-5F56-2
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).