CHAPTER 8. GMO Detection and Identification Using Next-generation Sequencing

2019 ◽  
pp. 96-106 ◽  
Author(s):  
Marie-Alice Fraiture ◽  
Nina Papazova ◽  
Kevin Vanneste ◽  
Sigrid C. J. de Keersmaecker ◽  
Nancy H. Roosens
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation ◽  
Gmo Detection ◽  
Detection And Identification ◽  
Generation Sequencing

scholarly journals Detection and identification of transgenic events by next generation sequencing combined with enrichment technologies

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frédéric Debode ◽  
Julie Hulin ◽  
Benoît Charloteaux ◽  
Wouter Coppieters ◽  
Marc Hanikenne ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetically Modified Organisms ◽  
Plant Genome ◽  
Structural Elements ◽  
Next Generation ◽  
Promising Tool ◽  
Detection And Identification ◽  
Food And Feed ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract Next generation sequencing (NGS) is a promising tool for analysing the quality and safety of food and feed products. The detection and identification of genetically modified organisms (GMOs) is complex, as the diversity of transgenic events and types of structural elements introduced in plants continue to increase. In this paper, we show how a strategy that combines enrichment technologies with NGS can be used to detect a large panel of structural elements and partially or completely reconstruct the new sequence inserted into the plant genome in a single analysis, even at low GMO percentages. The strategy of enriching sequences of interest makes the approach applicable even to mixed products, which was not possible before due to insufficient coverage of the different genomes present. This approach is also the first step towards a more complete characterisation of agrifood products in a single analysis.

scholarly journals Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction

2018 ◽  
Author(s):  
Sergey Knyazev ◽  
Viachaslau Tsyvina ◽  
Anupama Shankar ◽  
Andrew Melnyk ◽  
Alexander Artyomenko ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Treatment Plan ◽  
Sequencing Error ◽  
Next Generation ◽  
Accurate Identification ◽  
Detection And Identification ◽  
Consistent Performance ◽  
Sequencing Platforms ◽  
Generation Sequencing

ABSTRACTRapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing (NGS), but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

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