LIFE-Seq: a universal Large Integrated DNA Fragment Enrichment Sequencing strategy for deciphering the transgene integration of genetically modified organisms.
Hanwen ZhangRong LiYongkun GuoYuchen ZhangDabing ZhangLitao YangPublished in: Plant biotechnology journal (2022)
Molecular characterization of genetically modified organisms (GMOs) yields basic information on exogenous DNA integration, including integration sites, entire inserted sequences and structures, flanking sequences and copy number, providing key data for biosafety assessment. However, there are few effective methods for deciphering transgene integration, especially for large DNA fragment integration with complex rearrangement, inversion and tandem repeats. Herein, we developed a universal Large Integrated DNA Fragments Enrichment strategy combined with PacBio Sequencing (LIFE-Seq) for deciphering transgene integration in GMOs. Universal tilling DNA probes targeting transgenic elements and exogenous genes facilitate specific enrichment of large inserted DNA fragments associated with transgenes from plant genomes, followed by PacBio sequencing. LIFE-Seq were evaluated using six GM events and four crop species. Target DNA fragments averaging ~6275 bp were enriched and sequenced, generating ~26 352 high fidelity reads for each sample. Transgene integration structures were determined with high repeatability and sensitivity. Compared with next-generation whole-genome sequencing, LIFE-Seq achieved better data integrity and accuracy, greater universality and lower cost, especially for transgenic crops with complex inserted DNA structures. LIFE-Seq could be applied in molecular characterization of transgenic crops and animals, and complex DNA structure analysis in genetics research.
Keyphrases
- circulating tumor
- single molecule
- cell free
- single cell
- genome wide
- rna seq
- copy number
- nucleic acid
- circulating tumor cells
- dna methylation
- mitochondrial dna
- magnetic resonance
- drug delivery
- small molecule
- machine learning
- cancer therapy
- climate change
- transcription factor
- gene expression
- deep learning
- electronic health record
- computed tomography
- big data
- artificial intelligence
- fluorescence imaging
- contrast enhanced