Our publications

Bonfini, L. et al. A duplex sequencing approach for high-sensitivity detection of genome-edited plants Food Chemistry: Molecular Sciences 11, 100278 (2025).

Buttinger, G. et al. Conversion factors in GMO measurements: the past and the future. JRC144947 (2025).

Colaiacovo, M. et al. Retrieving sequences of genetically modified plants from public databases to widen screening approaches Journal of Consumer Protection and Food Safety (2025).

Colaiacovo, M. et al. Bioinformatics evaluation of GMO reference genes and their amplification systems. JRC136933 (2024).

Edelmann, S. et al. Does DNA extraction affect the specificity of a PCR method claiming the specific detectability of a genome-edited plant? GM Crops Food 15 (1): 352-360 (2024).

Bonfini, L. In Silico Proposal of Screening Strategies for Detecting EU Authorised GMOs. JRC131782 (2023).

Broothaerts, W. et al. Proficiency of European GMO control laboratories to quantify MON89788 soybean in a meat pâté matrix. Food Control145, 109454 (2022).

Corbisier, P. et al. Expression of GM content in mass fraction from digital PCR data. Food Control133 (Part B), 108626 (2022).

Gatto, F. et al. Single and multi-laboratory validation of a droplet digital PCR method. Food Control140, 109117 (2022).

Weidner, C. et al. Assessment of the Real-Time PCR Method Claiming to be Specific for Detection and Quantification of the First Commercialised Genome-Edited Plant. Food Analytical Methods15, 2107–2125 (2022).

Bonfini, L. In Silico Proposal of Screening Strategies for Detecting EU Authorised GMOs. JRC127110 (2021).

Broothaerts, W. et al. New Genomic Techniques: State-of-the-Art Review. JRC121847 (2021).

Broothaerts, W. et al. Log transformation of proficiency testing data on the content of genetically modified organisms in food and feed samples: is it justified? Anal. Bioanal. Chem.412, 1129–1136 (2020).

Broothaerts, W. et al. Response to Letter to the Editor regarding: Log transformation of proficiency testing data on the content of genetically modified organisms in food and feed samples: is it justified? Anal. Bioanal. Chem.412, 3949–3950 (2020).

Broothaerts, W. et al. Ten years of proficiency testing reveals an improvement in the analytical performance of EU National Reference Laboratories for genetically modified food and feed. Food Control114, 107237 (2020).

Huggett, J. et al. The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020. Clin. Chem. 66, 1012-1029 (2020).

Querci, M. et al. The analysis of food samples for the presence of Genetically Modified Organisms - User Manual. JRC120237 (2020).

Trapmann, S. et al. Guidance Document on Measurement Uncertainty for GMO Testing Laboratories. 3rd edition. JRC120898 (2020).

Corbisier, P. et al. Towards metrologically traceable and comparable results in GM quantification Anal. Bioanal. Chem.411, 7-11 (2019).

Emons, H. Chapter 11. Harmonising DNA Methods – The GMO Story. In DNA Techniques to Verify Food Authenticity: Applications in Food Fraud 139–146 (2019).

Jacchia, S. et al. Identification of single target taxon-specific reference assays for the most commonly genetically transformed crops using digital droplet PCR. Food Control93, 191–200 (2018).

Whale, A. et al. Assessment of Digital PCR as a Primary Reference Measurement Procedure to Support Advances in Precision Medicine. Clin. Chem.64, 1296-1307 (2018).

Bonfini, L. et al. Chapter 24. The European Union Reference Methods Database and Decision Supporting Tool for the Analysis of Genetically Modified Organisms. In Genetically Modified Organisms in Food 275–288 (Elsevier, 2016).

Debode, F. et al. Inter-laboratory studies for the validation of two singleplex (tE9 and pea lectin) and one duplex (pat/bar) real-time PCR methods for GMO detection. Food Control 73, 452-461 (2017).

Deprez, L. et al. Validation of a digital PCR method for quantification of DNA copy number concentrations by using a certified reference material. Biomol. Detection Quantification9, 29-39 (2016).

Gatto, F. et al. Semi-Quantification of GM Maize Using Ready-To-Use RTi-PCR Plates. Food Analytical Methods10, 549–558 (2017).

Lievens, A. et al. Measuring Digital PCR Quality: Performance Parameters and Their Optimization. PLoS ONE11 (5), e0153317 (2016).

Rosa, S. F. et al. Development and applicability of a ready-to-use PCR system for GMO screening. Food Chem.201, 110–119 (2016).

Angers-Loustau, A. et al. JRC GMO-Matrix: a web application to support Genetically Modified Organisms detection strategies. BMC Bioinformatics15, 417 (2014).

Bellocchi, G. et al. Fuzzy logic-based procedures for GMO analysis. Accreditation and Quality Assurance15, 637–641 (2010).

Bonfini, L. et al. GMOMETHODS: The European Union Database of Reference Methods for GMO Analysis. J. AOAC Int.95, 1713–1719 (2012).

Broothaerts, W. et al. A Single Nucleotide Polymorphism (SNP839) in the adh1 Reference Gene Affects the Quantitation of Genetically Modified Maize (Zea mays L.). J. Agric. Food Chem.56, 8825–8831 (2008).

Corbisier, P. et al. DNA copy number concentration measured by digital and droplet digital quantitative PCR using certified reference materials. Anal. Bioanal. Chem.407, 1831-1840 (2015).

Jacchia, S. et al. Development, Optimization, and Single Laboratory Validation of an Event-Specific Real-Time PCR Method for the Detection and Quantification of Golden Rice 2 Using a Novel Taxon-Specific Assay. J. Agric. Food Chem.63, 1711–1721 (2015).

Luque-Perez, E. et al. Testing the Robustness of Validated Methods for Quantitative Detection of GMOs Across qPCR Instruments. Food Analytical Methods6, 343–360 (2013).

Petrillo, M. et al. JRC GMO-Amplicons: a collection of nucleic acid sequences related to genetically modified organisms. Database 2015, bav101 (2015).