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Related Experiment Video

Updated: May 28, 2026

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato
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High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

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Data integration and network reconstruction with ~omics data using Random Forest regression in potato.

Animesh Acharjee1, Bjorn Kloosterman, Ric C H de Vos

  • 1Graduate School Experimental Plant Sciences, Wageningen, The Netherlands. animesh.acharjee@wur.nl

Analytica Chimica Acta
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

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Integrating multi-omics data, like gene expression and metabolites, aids crop breeding. This study presents a novel network approach to link omics data with potato traits, advancing breeding strategies.

Area of Science:

  • Plant science
  • Genomics
  • Metabolomics
  • Crop breeding

Background:

  • High-throughput technologies generate vast omics data (transcriptomics, metabolomics, proteomics).
  • Integrating diverse omics datasets with phenotypic traits remains a challenge for crop breeding advancement.
  • Understanding the genetic and metabolic basis of traits like tuber flesh color is crucial.

Purpose of the Study:

  • To develop and present a novel network-based approach for integrating multi-omics data (gene expression, metabolites) with phenotypic traits in potato.
  • To identify key genes and metabolites associated with potato tuber flesh color and enzymatic discoloration.
  • To construct integrated networks that incorporate known and uncharacterized components and pathways.

Main Methods:

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Published on: July 2, 2018

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Last Updated: May 28, 2026

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato
06:28

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Published on: June 7, 2024

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Published on: July 2, 2018

  • Collected population-wide gene expression and metabolite (LC-MS) data from diploid potato tubers.
  • Employed Random Forest regression to select significant metabolite and transcript subsets associated with phenotypic traits.
  • Utilized network reconstruction to integrate omics data and identify relationships with the carotenoid biosynthesis pathway.

Main Results:

  • Successfully selected metabolite and transcript subsets linked to potato tuber flesh color and enzymatic discoloration.
  • Constructed integrated networks connecting genes involved in carotenoid biosynthesis with known and uncharacterized metabolites.
  • Demonstrated the ability to build meaningful networks incorporating both identified and unknown molecular components and pathways.

Conclusions:

  • The novel network approach effectively integrates multi-omics data with phenotypic traits in potato.
  • This method facilitates the discovery of molecular players, including novel metabolites, underlying complex traits.
  • The findings provide a valuable framework for advancing potato breeding through integrated omics analysis.