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Article Abstract

In recent years, the digital polymerase chain reaction has received increasing interest as it has emerged as a tool to provide more sensitive and accurate detection of minimal residual disease. In order to start the process of data alignment, we assessed the consistency of the BCR-ABL1 quantification results of the analysis of 16 RNA samples at different levels of disease. The results were obtained by two different laboratories that relied on The Qx100/Qx200 Droplet Digital PCR System (Bio-Rad) and Quant Studio 3D dPCR System (Thermofisher) platforms. We assessed the compatibility between the estimated values by linear regression, Bland-Altman bias-plot, and Mann-Whitney nonparametric test. The results confirmed the compatibility of the measures, allowing us tocompute an 'alignment factor' (AF), equal to 1.41, which was further validated by a different series of experiments. We conclude that the performed measurements by the two laboratories are comparable, and also equalized through the introduction of an alignment factor.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161814PMC
http://dx.doi.org/10.3390/diseases9020035DOI Listing

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In recent years, the digital polymerase chain reaction has received increasing interest as it has emerged as a tool to provide more sensitive and accurate detection of minimal residual disease. In order to start the process of data alignment, we assessed the consistency of the BCR-ABL1 quantification results of the analysis of 16 RNA samples at different levels of disease. The results were obtained by two different laboratories that relied on The Qx100/Qx200 Droplet Digital PCR System (Bio-Rad) and Quant Studio 3D dPCR System (Thermofisher) platforms.

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Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, SI-1000 Ljubljana, Slovenia. Electronic address:

The increased use of genetically modified organisms (GMOs) is accompanied by increased complexity of the matrices that contain GMOs. The most common DNA-based approach for GMO detection and quantification is real-time quantitative polymerase chain reaction (qPCR). However, as qPCR is sensitive to inhibitors and relies on standard curves for quantification, it has limited application in GMO quantification for complex matrices.

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