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

Abstract 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 bet...

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Autores principales:	Kotetsu Kayama, Miyuki Kanno, Naoto Chisaki, Misaki Tanaka, Reika Yao, Kiwamu Hanazono, Gerry Amor Camer, Daiji Endoh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/350560cb9322429cb4deb83454ae24cb
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spelling	oai:doaj.org-article:350560cb9322429cb4deb83454ae24cb2021-12-02T14:26:07ZPrediction of PCR amplification from primer and template sequences using recurrent neural network10.1038/s41598-021-86357-12045-2322https://doaj.org/article/350560cb9322429cb4deb83454ae24cb2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86357-1https://doaj.org/toc/2045-2322Abstract 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.Kotetsu KayamaMiyuki KannoNaoto ChisakiMisaki TanakaReika YaoKiwamu HanazonoGerry Amor CamerDaiji EndohNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-24 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kotetsu Kayama Miyuki Kanno Naoto Chisaki Misaki Tanaka Reika Yao Kiwamu Hanazono Gerry Amor Camer Daiji Endoh Prediction of PCR amplification from primer and template sequences using recurrent neural network
description	Abstract 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.
format	article
author	Kotetsu Kayama Miyuki Kanno Naoto Chisaki Misaki Tanaka Reika Yao Kiwamu Hanazono Gerry Amor Camer Daiji Endoh
author_facet	Kotetsu Kayama Miyuki Kanno Naoto Chisaki Misaki Tanaka Reika Yao Kiwamu Hanazono Gerry Amor Camer Daiji Endoh
author_sort	Kotetsu Kayama
title	Prediction of PCR amplification from primer and template sequences using recurrent neural network
title_short	Prediction of PCR amplification from primer and template sequences using recurrent neural network
title_full	Prediction of PCR amplification from primer and template sequences using recurrent neural network
title_fullStr	Prediction of PCR amplification from primer and template sequences using recurrent neural network
title_full_unstemmed	Prediction of PCR amplification from primer and template sequences using recurrent neural network
title_sort	prediction of pcr amplification from primer and template sequences using recurrent neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/350560cb9322429cb4deb83454ae24cb
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Prediction of PCR amplification from primer and template sequences using recurrent neural network

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