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Related Concept Videos

Proteomics01:33

Proteomics

8.9K
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.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Updated: Nov 22, 2025

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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ProteomeExpert: a Docker image-based web server for exploring, modeling, visualizing and mining quantitative

Tiansheng Zhu1,2,3,4, Hao Chen1,2,5, Xishan Yan6

  • 1Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.

Bioinformatics (Oxford, England)
|January 8, 2021
PubMed
Summary
This summary is machine-generated.

ProteomeExpert is a new Docker-based web tool for analyzing large quantitative proteomic and other omics datasets. It simplifies experimental design, data mining, interpretation, and visualization for researchers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Proteomics

Background:

  • High-throughput omics technologies generate large-scale datasets.
  • Mass spectrometry-based proteomics, including data-independent acquisition, is increasingly used in clinical studies.
  • Analyzing large quantitative proteomic and multi-omics datasets presents computational challenges.

Purpose of the Study:

  • To develop an efficient and user-friendly web server-based software tool for analyzing quantitative proteomic and other omics data.
  • To provide a comprehensive platform for experimental design, data mining, interpretation, and visualization.
  • To facilitate the analysis of large-scale proteomic datasets from clinical studies.

Main Methods:

  • Development of ProteomeExpert, a web server-based software tool.
  • Implementation using Docker for easy deployment on various operating systems.
  • Integration of various analysis tools for quantitative proteomic data.
  • Support for R language environment.

Main Results:

  • ProteomeExpert offers a suite of tools for comprehensive analysis of quantitative proteomic datasets.
  • The software is designed for efficient and convenient handling of large datasets.
  • It supports integration with other omics data types like transcriptomics and metabolomics.
  • The tool facilitates experimental design, data mining, interpretation, and visualization.

Conclusions:

  • ProteomeExpert provides a valuable resource for researchers analyzing large-scale quantitative proteomic and multi-omics data.
  • The Docker implementation ensures accessibility and ease of deployment.
  • The web server approach enhances convenience for data analysis in clinical and research settings.