<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Willems, Sander</style></author><author><style face="normal" font="default" size="100%">Marie-Alice Fraiture</style></author><author><style face="normal" font="default" size="100%">Deforce, Dieter</style></author><author><style face="normal" font="default" size="100%">Sigrid C.J. De Keersmaecker</style></author><author><style face="normal" font="default" size="100%">Philippe Herman</style></author><author><style face="normal" font="default" size="100%">De Loose, Marc</style></author><author><style face="normal" font="default" size="100%">Ruttink, Tom</style></author><author><style face="normal" font="default" size="100%">Van Nieuwerburgh, Filip</style></author><author><style face="normal" font="default" size="100%">Nancy Roosens</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical framework for detection of Genetically Modified Organisms based on Next Generation Sequencing</style></title><secondary-title><style face="normal" font="default" size="100%">Food Chemistry</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bioinformatics</style></keyword><keyword><style  face="normal" font="default" size="100%">detection</style></keyword><keyword><style  face="normal" font="default" size="100%">GM rice</style></keyword><keyword><style  face="normal" font="default" size="100%">GMO</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">Processed food</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical framework</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">21/07/2015</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">798</style></number><volume><style face="normal" font="default" size="100%">192</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops.&lt;/p&gt;
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