Scaling up the detection of genome-edited rice lines
A new open-access study introduces RiSpy, a data-driven fingerprinting framework that makes the identification of genome-edited (GE) rice lines more robust, scalable and broadly applicable. Researchers from Sciensano, together with CIRAD and DARWIN project partners, in collaboration with colleagues from Ghent University, have presented the new methodological framework for the reliable…
A new open-access study introduces RiSpy, a data-driven fingerprinting framework that enhances the identification of genome-edited (GE) rice lines. Developed by researchers from Sciensano, CIRAD, DARWIN project partners, and Ghent University, RiSpy generalizes the genetic-fingerprint concept into a scalable, robust, and broadly applicable method for distinguishing multiple rice lines.
Supported by advanced bioinformatics and statistical feature-selection pipelines, RiSpy can generate genetic fingerprints for GE lines in cultivars not present in public resources like the 3K Rice Genomes database, using data from both Illumina and Oxford Nanopore Technologies platforms. Demonstrated through two in-house GE rice lines and public datasets, the method's robustness, scalability, and specificity offer a foundation for data-driven traceability of GE rice lines, supporting regulatory compliance, intellectual property protection, and the responsible implementation of EU GMO/NGT legislation.
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