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Proteomics

Proteomics

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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Related Experiment Video

Updated: Apr 11, 2026

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

Metaproteomic analysis using the Galaxy framework.

Pratik D Jagtap^1,2, Alan Blakely³, Kevin Murray²

¹Center for Mass Spectrometry and Proteomics, University of Minnesota, Minneapolis, MN, USA.

|June 11, 2015

Summary

We developed an accessible Galaxy workflow for metaproteomics, simplifying the analysis of complex microbial community proteins. This bioinformatics solution enhances data quality and reproducibility for researchers studying microbiomes.

Keywords:

Bioinformatics Customized database generation Mass spectrometry Metaproteomics Peptide sequence match Sequence database search

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Last Updated: Apr 11, 2026

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

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Published on: April 11, 2019

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Published on: August 19, 2025

Area of Science:

Bioinformatics
Metaproteomics
Microbiome Analysis

Background:

Metaproteomics analyzes proteins from microbial communities, offering insights into their functions.
Conventional proteomics faces challenges with large datasets and complex microbial communities.
Metaproteomics data analysis is complex, limiting its widespread adoption.

Purpose of the Study:

To develop an accessible and flexible metaproteomics workflow.
To address the data analysis challenges in metaproteomics.
To enable comprehensive identification of host and microbial proteins.

Main Methods:

Developed a modular workflow within the Galaxy bioinformatics framework.
Automated database searching for large sequence databases.
Integrated tools for basic local alignment search tool (BLAST) analysis and quality evaluation.
Utilized human oral tissue exudate samples for validation.

Main Results:

Established a reproducible metaproteomics workflow in Galaxy.
Successfully identified both human host proteins and microbial "meta-proteins".
Automated downstream processing enhanced confidence in results.
Workflow outputs are compatible with taxonomic and functional analysis tools.

Conclusions:

Galaxy provides a robust platform for metaproteomic data analysis.
The developed workflow simplifies complex analyses, promoting wider use.
This approach enhances reproducibility and facilitates workflow sharing.