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Holomics - a user-friendly R shiny application for multi-omics data integration and analysis.

Katharina Munk1, Daria Ilina1, Lisa Ziemba1

  • 1Center for Health & Bioresources, AIT Austrian Institute of Technology, Konrad-Lorenz-Straße 24, 3430, Tulln, Austria.

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|March 4, 2024
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Summary
This summary is machine-generated.

This study introduces Holomics, a user-friendly R application for multi-omics data integration. It simplifies complex analyses, enabling scientists to uncover biological interactions and identify biomarkers for improved crop traits.

Keywords:
Biomarker discoveryData integrationMulti-omicsMultivariate data analysisR shiny applicationStorabilitySugar beet

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Area of Science:

  • Biological sciences
  • Bioinformatics
  • Genomics and Systems Biology

Background:

  • Phenotypes arise from complex interactions between genes, proteins, metabolites, and environmental factors.
  • Multi-omics data integration is crucial for understanding biological systems but faces challenges in analysis due to scale and complexity.
  • Existing analytical tools often require advanced bioinformatics expertise, limiting accessibility for many scientists.

Purpose of the Study:

  • To develop an accessible and user-friendly R shiny application, Holomics, for multi-omics data integration.
  • To provide a streamlined workflow for analyzing diverse omics datasets, catering to scientists with limited bioinformatics knowledge.
  • To facilitate the discovery of key biological components and biomarkers through holistic systems analysis.

Main Methods:

  • Development of Holomics, an R shiny application with a guided workflow for data pre-filtering and analysis.
  • Implementation of single-omics analysis to refine datasets, followed by multi-omics analysis to identify cross-dataset correlations.
  • Application of Holomics to integrate microbiomics, transcriptomics, and metabolomics data for a sugar beet storability case study.

Main Results:

  • Holomics successfully integrated multiple omics datasets, revealing correlations between different biological layers.
  • The application reproduced findings from previous single-omics studies, validating its consistency and reliability.
  • The case study demonstrated Holomics' utility in identifying factors associated with improved sugar beet storability.

Conclusions:

  • Holomics offers a versatile and accessible platform for multi-omics data analysis, empowering researchers without extensive bioinformatics backgrounds.
  • The tool enhances the ability to gain holistic biological insights and identify relevant biomarkers.
  • Integrated multi-omics approaches, facilitated by user-friendly tools like Holomics, are essential for advancing fields like crop improvement and diagnostics.