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PrimerROC: accurate condition-independent dimer prediction using ROC analysis

To-date systematic testing and comparison of the accuracy of available primer-dimer prediction software has never been conducted, due in part to a lack of tools able to measure the efficacy of Gibbs free energy (ΔG) calculations at predicting dimer formation in PCR. To address this we have developed...

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Autores principales: Johnston, Andrew D., Lu, Jennifer, Ru, Ke-lin, Korbie, Darren, Trau, Matt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338771/
https://www.ncbi.nlm.nih.gov/pubmed/30659212
http://dx.doi.org/10.1038/s41598-018-36612-9
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author Johnston, Andrew D.
Lu, Jennifer
Ru, Ke-lin
Korbie, Darren
Trau, Matt
author_facet Johnston, Andrew D.
Lu, Jennifer
Ru, Ke-lin
Korbie, Darren
Trau, Matt
author_sort Johnston, Andrew D.
collection PubMed
description To-date systematic testing and comparison of the accuracy of available primer-dimer prediction software has never been conducted, due in part to a lack of tools able to measure the efficacy of Gibbs free energy (ΔG) calculations at predicting dimer formation in PCR. To address this we have developed a novel online tool called PrimerROC (www.primer-dimer.com/roc/), which uses epidemiologically-based Receiver Operating Characteristic (ROC) curves to assess dimer prediction accuracy. Moreover, by integrating PrimerROC with our PrimerDimer prediction software we can determine a ΔG-based dimer-free threshold above which dimer formation is predicted unlikely to occur. Notably, PrimerROC determines this cut-off without any additional information such as salt concentration or annealing temperature, meaning that our PrimerROC method is an assay and condition independent prediction tool. To demonstrate the broad utility of PrimerROC we assessed the performance of seven publically available primer design and dimer analysis tools using a dataset of over 300 primer pairs. We found that our PrimerROC/PrimerDimer software consistently outperforms these other tools and can achieve predictive accuracies greater than 92%. To illustrate its predictive power this method was used in multiplex PCR design to successfully generate four resequencing assays containing up to 126 primers with no observable primer-primer amplification artefacts.
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spelling pubmed-63387712019-01-23 PrimerROC: accurate condition-independent dimer prediction using ROC analysis Johnston, Andrew D. Lu, Jennifer Ru, Ke-lin Korbie, Darren Trau, Matt Sci Rep Article To-date systematic testing and comparison of the accuracy of available primer-dimer prediction software has never been conducted, due in part to a lack of tools able to measure the efficacy of Gibbs free energy (ΔG) calculations at predicting dimer formation in PCR. To address this we have developed a novel online tool called PrimerROC (www.primer-dimer.com/roc/), which uses epidemiologically-based Receiver Operating Characteristic (ROC) curves to assess dimer prediction accuracy. Moreover, by integrating PrimerROC with our PrimerDimer prediction software we can determine a ΔG-based dimer-free threshold above which dimer formation is predicted unlikely to occur. Notably, PrimerROC determines this cut-off without any additional information such as salt concentration or annealing temperature, meaning that our PrimerROC method is an assay and condition independent prediction tool. To demonstrate the broad utility of PrimerROC we assessed the performance of seven publically available primer design and dimer analysis tools using a dataset of over 300 primer pairs. We found that our PrimerROC/PrimerDimer software consistently outperforms these other tools and can achieve predictive accuracies greater than 92%. To illustrate its predictive power this method was used in multiplex PCR design to successfully generate four resequencing assays containing up to 126 primers with no observable primer-primer amplification artefacts. Nature Publishing Group UK 2019-01-18 /pmc/articles/PMC6338771/ /pubmed/30659212 http://dx.doi.org/10.1038/s41598-018-36612-9 Text en © 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 Creative 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 permitted 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/.
spellingShingle Article
Johnston, Andrew D.
Lu, Jennifer
Ru, Ke-lin
Korbie, Darren
Trau, Matt
PrimerROC: accurate condition-independent dimer prediction using ROC analysis
title PrimerROC: accurate condition-independent dimer prediction using ROC analysis
title_full PrimerROC: accurate condition-independent dimer prediction using ROC analysis
title_fullStr PrimerROC: accurate condition-independent dimer prediction using ROC analysis
title_full_unstemmed PrimerROC: accurate condition-independent dimer prediction using ROC analysis
title_short PrimerROC: accurate condition-independent dimer prediction using ROC analysis
title_sort primerroc: accurate condition-independent dimer prediction using roc analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338771/
https://www.ncbi.nlm.nih.gov/pubmed/30659212
http://dx.doi.org/10.1038/s41598-018-36612-9
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