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Related Concept Videos

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Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Updated: May 24, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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ClusterApp to visualize, organize, and navigate metabolomics data.

Vinicius Hansel1, Pothuvilage Karunarathne2, Tiago Cabral Borelli1

  • 1Department of Biomolecular Sciences, Computational Chemical Biology Laboratory, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.

Biorxiv : the Preprint Server for Biology
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

ClusterApp offers a user-friendly web application for Principal Coordinate Analysis (PCoA) in metabolomics, simplifying clustering and data visualization for researchers without extensive bioinformatics expertise.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Data Visualization

Background:

  • Principal Component Analysis (PCA) is common in metabolomics but suboptimal for clustering visualization.
  • Metabolomics data requires specialized preprocessing and efficient visualization tools.
  • Lack of user-friendly tools hinders clustering analysis for many researchers.

Purpose of the Study:

  • To introduce ClusterApp, a web application for Principal Coordinate Analysis (PCoA) in metabolomics.
  • To provide an accessible tool for clustering and visualization of metabolomics data.
  • To expand clustering alternatives beyond PCA for metabolomics researchers.

Main Methods:

  • ClusterApp utilizes a QIIME 2 Docker image for PCoA computation and Emperor plot visualization.
  • Supports data input from GNPS, GNPS2, or user-provided spreadsheets.
  • Offers local installation via Docker or integration into Jupyter notebooks.

Main Results:

  • Analyzed LC-MS/MS metabolomics datasets from mouse tissues and coral life stages.
  • Bray-Curtis dissimilarity measure effectively highlighted metabolomic variations.
  • TIC normalization and auto-scaling improved clustering resolution and revealed distinct sample separations.

Conclusions:

  • ClusterApp provides accessible, dynamic tools for exploratory data analysis in metabolomics.
  • Couples data transformation with PCoA for versatile clustering analysis.
  • Empowers researchers to uncover patterns without advanced bioinformatics expertise.