Literature DB >> 31054687

Omics analyses of potato plant materials using an improved one-class classification tool to identify aberrant compositional profiles in risk assessment procedures.

Esther Kok1, Jeroen van Dijk2, Marleen Voorhuijzen2, Martijn Staats2, Martijn Slot2, Arjen Lommen2, Dini Venema2, Maria Pla3, Maria Corujo4, Eugenia Barros5, Ronald Hutten6, Jeroen Jansen7, Hilko van der Voet8.   

Abstract

The objective of this study was to quantitatively assess potato omics profiles of new varieties for meaningful differences from analogous profiles of commercial varieties through the SIMCA one-class classification model. Analytical profiles of nine commercial potato varieties, eleven experimental potato varieties, one GM potato variety that had acquired Phytophtora resistance based on a single insert with potato-derived DNA sequences, and its non-GM commercial counterpart were generated. The ten conventional varieties were used to construct the one-class model. Omics profiles from experimental non-GM and GM varieties were assessed using the one-class SIMCA models. No potential unintended effects were identified in the case of the GM variety. The model showed that varieties that were genetically more distant from the commercial varieties were recognized as aberrant, highlighting its potential in determining whether additional evaluation is required for the risk assessment of materials produced from any breeding technique, including genetic modification.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Compositional analysis; GMO; Genetically modified organism; Omics profiling; Risk assessment

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Substances:

Year:  2018        PMID: 31054687     DOI: 10.1016/j.foodchem.2018.07.224

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  3 in total

1.  Equivalence Testing Approaches in Genetically Modified Organism Risk Assessment.

Authors:  Hilko van der Voet; Claudia Paoletti
Journal:  J Agric Food Chem       Date:  2019-11-27       Impact factor: 5.279

Review 2.  Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops.

Authors:  Mohamed Bedair; Kevin C Glenn
Journal:  Metabolomics       Date:  2020-10-09       Impact factor: 4.290

3.  Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage.

Authors:  Alberto Brini; Vahe Avagyan; Ric C H de Vos; Jack H Vossen; Edwin R van den Heuvel; Jasper Engel
Journal:  Metabolites       Date:  2021-04-13
  3 in total

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