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

Area of Science:

• Agricultural Biotechnology
• Genomics
• Bioinformatics

Background:

• The European Union (EU) has strict regulations for genetically modified organisms (GMOs), including genome-edited (GE) lines from new genomic techniques (NGTs).
• Identifying GE organisms based on single nucleotide variations (SNVs) is difficult as one SNV cannot uniquely define a GE line.
• Previous work introduced a proof-of-concept for a genetic fingerprint for a specific GE rice line using whole-genome sequencing (WGS) and public databases.

Purpose of the Study:

• To develop a generalized, data-driven framework for identifying multiple GE rice lines.
• To create a robust method for generating genetic fingerprints independent of public database inclusion.
• To enable GE rice line identification using data from both Illumina and Oxford Nanopore Technologies (ONT) platforms.

Main Methods:

• Development of new bioinformatics and statistical feature-selection pipelines.
• Generation of genetic fingerprints by combining GE on-target sites and cultivar-specific SNV barcodes.
• Utilizing whole-genome sequencing (WGS) data from Illumina and ONT platforms.

Main Results:

• Demonstrated a generalized framework for creating genetic fingerprints for multiple GE rice lines.
• Successfully identified GE rice lines irrespective of their inclusion in public databases like 3K Rice Genomes (3KRG).
• Validated the approach's robustness, scalability, and specificity using diverse GE rice lines and WGS datasets.

Conclusions:

• The developed strategy provides a methodological foundation for data-driven traceability of GE rice lines.
• This approach supports regulatory compliance with EU GMO/NGT legislation.
• The findings contribute to intellectual property protection and the responsible deployment of GE technologies.