RiSpy: a feature selection-based fingerprinting framework for accurate identification of genome-edited rice lines

The European Union (EU) enforces strict regulations on the traceability and labeling of genetically modified organisms (GMOs), including genome-edited (GE) lines produced through new genomic techniques (NGTs). Identifying GE organisms created by single nucleotide variations (SNVs) is however challenging, as a single SNV alone cannot unambiguously define a GE line. Recently, we introduced the concept of generating a genetic fingerprint to distinguish a specific GE rice line. This proof-of-concept approach integrated whole-genome sequencing (WGS)-based characterization with the Illumina technology, the public 3 K Rice Genomes (3KRG) database, and statistical feature-selection tools, to select and combine key genetic elements, including GE on-target site(s) and cultivar-specific 2-SNV barcodes, into a unique genetic fingerprint. In the present study, we expand this concept into a generalized data-driven framework allowing identification of multiple rice lines. Supported by newly developed bioinformatics and statistical feature-selection-based pipelines, this optimized strategy enables the generation of genetic fingerprints irrespective of a rice cultivar's inclusion in publicly available databases like 3KRG. In addition, this refined strategy can leverage WGS data generated from both Illumina and Oxford Nanopore Technologies (ONT) platforms for fingerprint generation and GE line identification. Using two distinct in-house GE rice lines from different cultivars, along with various publicly available WGS datasets, we demonstrated the robustness, scalability, and specificity of this approach for reliable GE rice line identification. Our findings provide a methodological foundation for data-driven traceability of GE rice lines, reinforcing regulatory compliance, supporting intellectual property (IP) protection, and contributing to the responsible implementation of EU GMO/NGT legislation.