Scaling up the detection of genome-edited rice lines

A new open-access study introduces RiSpy, an innovative data-driven fingerprinting framework designed to enhance the detection and identification of genome-edited (GE) rice lines. Developed by researchers from Sciensano, CIRAD, DARWIN project partners, and Ghent University, RiSpy is presented in the journal Briefings in Bioinformatics. This methodological framework aims to make the identification of GE rice lines more reliable, scalable, and broadly applicable.

Building upon a previously reported genetic-fingerprint concept, RiSpy has been adapted into a data-driven framework that can distinguish multiple GE rice lines. The method is supported by newly developed bioinformatics and statistical feature-selection pipelines. These tools enable the generation of genetic fingerprints even for GE lines from cultivars not present in public resources, such as the 3K Rice Genomes database.

The framework is capable of working with whole-genome sequencing data obtained from both Illumina and Oxford Nanopore Technologies platforms. In a demonstration of its robustness and scalability, the authors used two in-house GE rice lines from different cultivars alongside publicly available datasets. The results showcased the method's ability to maintain high specificity and accuracy in identifying GE rice lines.

The introduction of RiSpy offers a methodological foundation for the data-driven traceability of GE rice lines. This advancement can support regulatory compliance, intellectual property protection, and the responsible implementation of EU GMO/NGT legislation. By providing a scalable and specific approach to identifying GE rice lines, RiSpy paves the way for more effective monitoring and management of genetically modified crops.