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GXP: Analyze and Plot Plant Omics Data in Web Browsers.

Constantin Eiteneuer1, David Velasco2, Joseph Atemia3

  • 1IBG-2 Plant Sciences, Forschungszentrum Jülich, 52428 Jülich, Germany.

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PubMed
Summary
This summary is machine-generated.

Gene Expression Plotter (GXP) is a web-based tool for visualizing and analyzing RNA sequencing (RNAseq) and metabolomics data. It requires no installation or data uploading, accelerating interdisciplinary omics research.

Keywords:
MapmanMercatorRNA sequencingcluster analysiscorrelationdata visualizationmetabolomicsoverrepresentation analysisprincipal component analysisscientific plotting

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics
  • Metabolomics

Background:

  • Next-generation sequencing (NGS) and metabolomics are crucial in life sciences, generating large datasets.
  • Analysis of omics data requires visualization and exploration tools to support hypothesis generation.
  • Existing tools often necessitate installation or programming, posing barriers to accessibility.

Purpose of the Study:

  • To develop an accessible, web-based tool for analyzing and visualizing RNA sequencing (RNAseq) and metabolomics data.
  • To enable researchers to perform in-depth data analyses and candidate identification directly in their web browser.
  • To facilitate the acceleration of interdisciplinary omics projects and downstream analyses without custom installation or data sharing.

Main Methods:

  • Developed "Gene Expression Plotter" (GXP), a novel software tool.
  • GXP runs entirely within the user's web browser, eliminating the need for installation.
  • The tool processes quantitative data from RNAseq and metabolomics analyses.

Main Results:

  • GXP provides immediate, interactive data exploration and analysis capabilities.
  • It supports knowledge-driven analyses for identifying biological patterns and candidates.
  • The tool facilitates easy sharing of data, plots, and results via export or custom websites.

Conclusions:

  • GXP significantly enhances the accessibility and efficiency of omics data analysis.
  • It empowers researchers to conduct complex analyses and accelerate discovery.
  • The browser-based nature of GXP ensures data privacy and broad usability in life science research.