文章基本信息

标题：PrimerROC: accurate condition-independent dimer prediction using ROC analysis
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作者：Andrew D. Johnston ; Jennifer Lu ; Ke-lin Ru 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2019
卷号：9
期号：1
页码：1-14
DOI：10.1038/s41598-018-36612-9
出版社：Springer Nature
摘要：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.