In silico prediction of loop-mediated isothermal amplification using a generalized linear model

Loop-mediated isothermal amplification (LAMP), a DNA amplification technique under isothermal conditions, provides the important benefits of high sensitivity, specificity, rapidity, and simplicity. Maximizing LAMP features necessitates the design of a complex LAMP primer set (LPS) consisting of four primers for six regions of a given target DNA. Furthermore, the LPS of a given target DNA is designed with LPS design support software such as Primer Explorer. However, even if the design is completed, we still must do many in vitro experiments and evaluations. Consequently, designing LPS often fails to achieve high performance, including efficient amplification. For this study, we examined in silico LAMP: a generalized linear model to predict DNA amplification from LPS. Using logistic regression with elastic net regularization, we identified factors that strongly affect LPS design. These factors, combined with domain knowledge for LPS design, led to the creation of LAMP kernel variables that are highly essential for high LAMP reaction. In silico LAMP, constructed using logistic regression with LAMP kernel variables, allows classification and performance prediction of LPS with an area under the curve of 0.86. These results suggest that a high LAMP reaction can be predicted using LAMP kernel variables and generalized linear regression model. Moreover, an LPS with high performance can be constructed without in vitro experimentation.