High-Resolution Melting (HRM) analysis of DNA methylation using semiconductor chip-based digital PCR.

BACKGROUND: Digital PCR (dPCR) technology allows absolute quantification and detection of disease-associated rare variants, and thus the use of dPCR technology has been increasing in clinical research and diagnostics. The high-resolution melting curve analysis (HRM) of qPCR is widely used to distinguish true positives from false positives and detect rare variants. In particular, qPCR-HRM is commonly used for methylation assessment in research and diagnostics due to its simplicity and high reproducibility. Most dPCR instruments have limited fluorescence channels available and separate heating and imaging systems. Therefore, it is difficult to perform HRM analysis using dPCR instruments. OBJECTIVE: A new digital real-time PCR instrument (LOAA) has been recently developed to integrate partitioning, thermocycling, and imaging in a single dPCR instrument. In addition, a new technique to perform HRM analysis is utilized in LOAA. The aim of the present study is to evaluate the efficiency and accuracy of LOAA dPCR on HRM analysis for the detection of methylation. METHODS: In this study, comprehensive comparison with Bio-Rad qRT-PCR and droplet-based dPCR equipment was performed to verify the HRM analysis-based methylation detection efficiency of the LOAA digital PCR equipment. Here, sodium bisulfite modification method was applied to detect methylated DNA sequences by each PCR method. RESULTS: Melting curve analysis detected four different Tm values using LOAA and qPCR, and found that LOAA, unlike qPCR, successfully distinguished between different Tm values when the Tm values were very similar. In addition, melting temperatures increased by each methylation were about 0.5℃ for qPCR and about 0.2 ~ 0.6℃ for LOAA. The melting temperature analyses of methylated and unmethylated DNA samples were conducted using LOAA dPCR with TaqMan probes and EvaGreen, and the result found that Tm values of methylated DNA samples are higher than those of unmethylated DNA samples. CONCLUSION: The present study shows that LOAA dPCR could detect different melting temperatures according to methylation status of target sequences, indicating that LOAA dPCR would be useful for diagnostic applications that require the accurate quantification and assessment of DNA methylation.

Design of an optimum computer vision-based automatic abalone (Haliotis discus hannai) grading algorithm.

An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively.