This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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 identification of genome-edited (GE) rice lines in the journal Briefings in Bioinformatics.
Building on a previously reported genetic-fingerprint concept, RiSpy generalizes the approach into a data-driven framework able to distinguish multiple rice lines. Supported by newly developed bioinformatics and statistical feature-selection pipelines, it can generate genetic fingerprints even for GE lines belonging to cultivars that are not included in public resources such as the 3K Rice Genomes database, and it works with whole-genome sequencing data from both Illumina and Oxford Nanopore Technologies platforms. Using two in-house GE rice lines from different cultivars alongside publicly available data sets, the authors demonstrated the method's robustness, scalability and specificity.
The results offer a methodological foundation for the data-driven traceability of GE rice lines, supporting regulatory compliance, intellectual property protection and the responsible implementation of EU GMO/NGT legislation. Amin Zolfaghari et al, RiSpy: a feature selection-based fingerprinting framework for accurate identification of genome-edited rice lines, Briefings in Bioinformatics (2026). DOI: 10.1093/bib/bbag406 BA art history, MA material culture.
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