Tm value of diatoms with different primers ( °C).

High-resolution melting analysis is a technique that leverages the principle that the thermal stability of dsDNA is influenced by its length and base composition. This method generates a melting curve by real-time monitoring of the changes in fluorescence signal as dsDNA melts during the heating process. The melting temperature serves as a fundamental indicator of sample characteristics in HRM analysis. During the initial stages of designing a new HRM experimental system, accurately predicting the Tm position of the established system can significantly enhance research efficiency. Currently, there is a limited number of studies focused on the prediction of Tm values in HRM analysis, with varying levels of predictive accuracy. The nearest-neighbor method model can well reflect the interaction of adjacent base pairs. Therefore, we combined the nearest neighbor method model and applied parameters such as enthalpy change, entropy change, GC content and number of base pairs of the DNA sequence to derive a new empirical formula for predicting Tm values. In this study, five species of seawater diatoms were selected as the research subjects. Four specific primers were employed to amplify the extracted DNA through PCR, and the resulting amplified products underwent HRM analysis and Sanger sequencing. Based on the obtained DNA sequences, we calculated the corresponding GC content, number of base pairs, enthalpy change and entropy change, combined with the Tm value obtained from the experiment. Finally, the following formula for predicting Tm value is obtained: (1) When the GC content is 40%≤GC content≤60%: Tm=ΔH/ΔS–0.27GC%–(150+2n)/n–273.15; (2) When the GC content is

高分辨率熔解分析（High-resolution melting analysis, HRM）是一项依托双链DNA（double-stranded DNA, dsDNA）的热稳定性受其长度与碱基组成影响这一原理的技术。该方法通过实时监测加热过程中dsDNA熔解时的荧光信号变化，生成熔解曲线。熔解温度（melting temperature, Tm）是HRM分析中表征样品特性的核心指标。在搭建新型HRM实验系统的初始阶段，精准预测已构建系统的Tm位点可显著提升研究效率。当前，针对HRM分析中Tm值预测的研究数量有限，且预测精度参差不齐。最近邻模型（nearest-neighbor method）能够较好地反映相邻碱基对之间的相互作用。因此，本研究结合最近邻模型，引入DNA序列的焓变、熵变、GC含量及碱基对数量等参数，推导出全新的Tm值预测经验公式。本研究选取5种海水硅藻作为研究对象，采用4条特异性引物通过聚合酶链式反应（polymerase chain reaction, PCR）扩增提取的DNA，所得扩增产物依次进行HRM分析与桑格测序（Sanger sequencing）。基于获取的DNA序列，我们计算其对应的GC含量、碱基对数量、焓变与熵变，并结合实验测得的Tm值，最终得到如下Tm值预测公式：(1) 当GC含量满足40%≤GC含量≤60%时：Tm=ΔH/ΔS–0.27GC%–(150+2n)/n–273.15；(2) 当GC含量为