Detection and identification of authorized and unauthorized GMOs using high-throughput sequencing with the support of a sequence-based GMO database

Assia Saltykova; Julien Van Braekel; Nina Papazova; Marie-Alice Fraiture; Dieter Deforce; Kevin Vanneste; Sigrid C J De Keersmaecker; Nancy H Roosens

doi:10.1016/j.fochms.2022.100096

Detection and identification of authorized and unauthorized GMOs using high-throughput sequencing with the support of a sequence-based GMO database

Food Chem (Oxf). 2022 Mar 7:4:100096. doi: 10.1016/j.fochms.2022.100096. eCollection 2022 Jul 30.

Authors

Assia Saltykova^{1

2}, Julien Van Braekel¹, Nina Papazova¹, Marie-Alice Fraiture¹, Dieter Deforce³, Kevin Vanneste¹, Sigrid C J De Keersmaecker¹, Nancy H Roosens¹

Affiliations

¹ Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium.
² Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
³ Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.

Abstract

The increasing number and diversity of genetically modified organisms (GMOs) for the food and feed market calls for the development of advanced methods for their detection and identification. This issue can be addressed by next generation sequencing (NGS). However, the efficiency of NGS-based strategies depends on the availability of bioinformatic methods to find sequences of the transgenic insert and junction regions, which is a challenging topic. To facilitate this task, we have developed Nexplorer, a sequence-based database in which annotated sequences of GM events are stored in a structured, searchable and extractable format. As a proof of concept, we have developed a methodology for the analysis of sequencing data of DNA walking libraries of samples containing GMOs using the database. The efficiency of the method has been tested on datasets representing various scenarios that can be encountered in routine GMO analysis. Database-guided analysis allowed obtaining detailed and reliable information with limited hands-on time. As the database allows for efficient analysis of NGS data, it paves the way for the use of NGS sequencing technology to aid routine detection and identification of GMO.

Keywords: Data analysis workflow; GMO database; GMO detection; GMO identification1; NGS.