4.7 Article

Prediction of PCR amplification from primer and template sequences using recurrent neural network

期刊

SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-021-86357-1

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资金

  1. Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT)-Supported Program for the Strategic Research Foundation at Private Universities
  2. Rakuno Gakuen University
  3. Lagrange Co. Ltd (Tokyo, Japan)

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A novel method was developed to predict the success of PCR amplification using a recurrent neural network, achieving 70% accuracy in predicting PCR results. This study suggests that neural networks could be used for primer design and PCR result prediction.
We have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.

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