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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: Mar 28, 2026

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

Ursgal, Universal Python Module Combining Common Bottom-Up Proteomics Tools for Large-Scale Analysis.

Lukas P M Kremer¹, Johannes Leufken¹, Purevdulam Oyunchimeg¹

¹Institute of Plant Biology and Biotechnology, University of Muenster , Schlossplatz 8, 48143 Münster, Germany.

Journal of Proteome Research

|December 29, 2015

Summary

Ursgal is a Python interface that unifies diverse proteomics software for robust data analysis. It enables complex, scriptable workflows by integrating multiple peptide identification and statistical algorithms.

Keywords:

Python combine search engine results high throughput peptide identification search engine statistical postprocessing engine

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Last Updated: Mar 28, 2026

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

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Published on: October 24, 2018

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Proteomics data analysis faces challenges due to software diversity.
Combining multiple peptide identification algorithms improves result robustness.
Unified and scriptable workflows are needed for complex data analysis.

Purpose of the Study:

Introduce Ursgal, a Python interface for unifying bottom-up proteomics tools.
Enable the creation of complex, scriptable proteomics analysis workflows.
Provide access to various database search engines and statistical postprocessing algorithms.

Main Methods:

Developed Ursgal as a Python interface for common bottom-up proteomics tools.
Integrated multiple database search engines (X!Tandem, OMSSA, MS-GF+, Myrimatch, MS Amanda).
Incorporated statistical postprocessing algorithms (qvality, Percolator) and novel combination algorithms ('combined FDR', 'combined PEP').

Main Results:

Ursgal allows complex workflow composition using Python scripting.
Provides unified access to diverse proteomics analysis tools.
Implemented new algorithms for combining search engine outputs and improving peptide identification.

Conclusions:

Ursgal facilitates unified, scriptable, and extensible proteomics data analysis.
The interface simplifies the integration of multiple algorithms for robust results.
New combination algorithms enhance the reliability of proteomics data interpretation.