论文标题
改善基线减法以提高定量PCR测量的灵敏度
Improving Baseline Subtraction for Increased Sensitivity of Quantitative PCR Measurements
论文作者
论文摘要
在当前的Covid-19健康危机中，我们研究了定量聚合酶链反应（QPCR）测量的基线减法任务。特别是，我们提出了一种算法，该算法利用了从非模板和/或DNA提取控制实验获得的信息，以从放大曲线中消除系统偏置。我们从数学优化的角度重述了这个问题，即通过找到从放大曲线中减去的控制信号量，可以最大程度地减少背景噪声。我们证明，这种方法相对于标准方法可以提高灵敏度十年，尤其是对于显示晚期周期扩增的数据。至关重要的是，这种提高的敏感性和准确性有望更有效地筛选病毒DNA，并降低诊断环境中的假阴性率。
Motivated by the current COVID-19 health-crisis, we examine the task of baseline subtraction for quantitative polymerase chain-reaction (qPCR) measurements. In particular, we present an algorithm that leverages information obtained from non-template and/or DNA extraction-control experiments to remove systematic bias from amplification curves. We recast this problem in terms of mathematical optimization, i.e. by finding the amount of control signal that, when subtracted from an amplification curve, minimizes background noise. We demonstrate that this approach can yield a decade improvement in sensitivity relative to standard approaches, especially for data exhibiting late-cycle amplification. Critically, this increased sensitivity and accuracy promises more effective screening of viral DNA and a reduction in the rate of false-negatives in diagnostic settings.
