GMOseek: an algorithm to improve the detection of GMOs in food, feed and environmental samples D. Morisset*1, P. Kralj Novak2, N. Lavrač2, D. Zupanič2, K. Gruden1, J. Žel1 1
Department of biotechnology and systems biology, National institute of biology, Večna pot 111, 1000 Ljubljana, Slovenia; 2 Department of Knowledge Technologies, Jožef Štefan Institute, Jamova 39, 1000 Ljubljana, Slovenia. * corresponding author (dany.morisset@nib.si)
Background and aims With the increasing number of EU-approved genetically modified organisms (GMO) expected for the next years (Stein and Rodriguez-Cerezo, 2009), the number of tests to be carried out will need to be increased accordingly. In addition, laboratories should also be able to distinguish between material derived from authorised and non-authorised GMOs. The increasing number and diversity among GMO traits raise an urgent challenge: while cost should be reduced, throughput and speed must be increased, in order to keep GMO monitoring programs time- and cost-affordable for enforcement laboratories. The goal of our work developed within the European SAFEFOODERA GMOseek project is to set up informatics tools including decision support system to implement the most effective testing strategy for GMO traceability. Methods A heuristic algorithm for a solution search was developed. It directs the solution composition following rules including the maximum coverage of GMOs. A decision support system (DSS) is integrated to allow the analyst interpreting results of analysis. Key results Numerous simulations were performed on real routine samples and on several scenarios foreseeing the future status of GMO commercialization. These simulations have demonstrated that the GMOseek algorithm ensures a better coverage of the GMOs potentially present in samples. It also targets the need for development of new detection methods for improved GMO detection. The algorithm is also very flexible to diverse scenarios (diversity and complexity of GMOs,...). Finally, the algorithm provides significant savings in terms of cost and time for analysis in comparison with the current diagnostics strategies. Conclusions The newly developed GMOseek algorithm is useful to all analysts wishing to improve their GMO detection strategies with better flexibility to the sample type, and with possibility to alert in case unauthorized GMOs are detected. References Stein,A.J. and Rodriguez-Cerezo,E. (2009). The global pipeline of new GM crops: Implications of asynchronous approval for international trade. Office for Official Publications of the European Communities, Luxembourg. report EUR 23486 EN
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