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Efficient transgenic maize (Zea Mays L.) detection using low-depth next-generation sequencing.

Xin Qi1, Xin Zhao1, Mingjie Lyu1

  • 1State Key Laboratory of Vegetable Biobreeding, Tianjin Academy of Agricultural Sciences, Tianjin, 300384, China.

Scientific Reports
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new method using low-depth next-generation sequencing (NGS) to accurately detect genetically modified (GM) crops. This efficient strategy aids in the cost-effective identification and surveillance of transgenic maize varieties.

Keywords:
Zea Mays L.MaizeNext-generation sequencingTransformantsTransgenic

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Area of Science:

  • Agricultural Science
  • Biotechnology
  • Genetics

Background:

  • Genetically modified (GM) crops offer benefits like increased yield and pest resistance.
  • The widespread use of GM crops necessitates robust regulatory oversight and traceability methods.
  • Identifying specific transformation events in GM crops is crucial for monitoring and compliance.

Purpose of the Study:

  • To develop a targeted detection strategy for identifying transformation events in maize using low-depth next-generation sequencing (NGS).
  • To evaluate key parameters influencing the accuracy and efficiency of GM maize detection.
  • To establish a cost-effective and scalable framework for GM crop surveillance.

Main Methods:

  • Systematic evaluation of tissue/organ selection, DNA extraction, and library preparation methods (PCR-free vs. amplification-based).
  • Optimization of sequencing depth for reliable detection of transgenic sequences.
  • Application of bioinformatics analysis for identifying transformation events in maize.

Main Results:

  • High-quality DNA input improved detection accuracy, while plant tissue type had no significant effect.
  • PCR-free library preparation demonstrated superior performance compared to amplification-based methods.
  • A sequencing depth of 5× was sufficient for reliably detecting transgenic sequences in unknown samples.

Conclusions:

  • The developed NGS-based methodology provides a high-efficiency, multi-target framework for identifying maize transformants.
  • This approach facilitates cost-effective and standardized surveillance of genetically modified crops.
  • The strategy supports scalable monitoring of GM crop proliferation and regulatory compliance.