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POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis.

Pol Castellano-Escuder1,2,3, Raúl González-Domínguez1,3, Francesc Carmona-Pontaque2,3

  • 1Biomarkers and Nutritional & Food Metabolomics Research Group, Department of Nutrition, Food Science and Gastronomy, Food Innovation Network (XIA), University of Barcelona, Barcelona, Spain.

Plos Computational Biology
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Summary
This summary is machine-generated.

POMAShiny simplifies complex metabolomics and proteomics data analysis with a user-friendly web tool. It offers flexible statistical methods for biomarker discovery and precision medicine, enhancing biological interpretation.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Metabolomics and proteomics data analysis presents significant data mining and statistical challenges.
  • Interpreting complex omics data is crucial for biomarker discovery and advancing precision medicine.
  • Existing bioinformatics tools often have limitations in flexibility and statistical method integration.

Purpose of the Study:

  • To introduce POMAShiny, a web-based tool designed to streamline metabolomics and proteomics data analysis.
  • To provide a flexible, user-friendly platform for data visualization, exploration, and statistical analysis.
  • To enhance the reproducibility and accessibility of omics data analysis.

Main Methods:

  • Development of POMAShiny, a web application based on the POMA R/Bioconductor package.
  • Integration of diverse statistical methods for omics data analysis.
  • Implementation of a structured workflow for data visualization and exploration.

Main Results:

  • POMAShiny offers a structured and flexible workflow for analyzing metabolomics and proteomics data.
  • The tool integrates multiple statistical methods, expanding analytical capabilities.
  • It enhances user-friendliness for researchers without extensive programming skills.

Conclusions:

  • POMAShiny provides a valuable, accessible resource for researchers in metabolomics and proteomics.
  • The tool facilitates more robust biomarker discovery and precision medicine applications.
  • Its integration with the POMA package ensures reproducibility and flexibility in omics data analysis.