Proteomics

Training material for proteomics workflows in Galaxy

Requirements

Before diving into this topic, we recommend you to have a look at:

• Introduction to Galaxy Analyses

Material

Introduction

Start here if you are new to proteomic analysis in Galaxy.

Protein identification and quantification

These tutorials cover protein identification and/or label-free and label based quantification from data dependent acquisition (DDA) and data independent acquisition (DIA).

Postprocessing of proteomics data

These tutorial cover statistical analyses and visualizations after protein identification and quantification.

Special proteomics techniques

These tutorials focus on special techniques such as N-terminomics and mass spectrometry imaging.

Multi-omics analyses

These tutorials combine proteomics with other -omics technologies such as transcriptomics.

Prediction of peptide properties

These tutorials explain in-silico analyses of different peptide properties.

Metaproteomics analysis of clinical data

These tutorials are step by step analysis from database generation to the discovery of peptides to verification, quantitation, and interpretation of the results.

Prediction of potential neoantigens

These tutorials outline the identification, prediction, and validation of potential neoantigens.

Frequently Asked Questions

Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.

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Community Resources

Editorial Board

This material is reviewed by our Editorial Board:

Melanie Föll

Subina Mehta

Pratik Jagtap

Björn Grüning

Contributors

This material was contributed to by:

Matthias Fahrner

Melanie Föll

David Christiany

Ray Sajulga

Clemens Blank

Helena Rasche

Emma Leith

Florence Combes

Timothy J. Griffin

Praveen Kumar

Saskia Hiltemann

Yves Vandenbrouck

Marie Crane

Valentin Loux

Klemens Fröhlich

Björn Grüning

Dechen Bhuming

Katherine Do

Bérénice Batut

Jayadev Joshi

Florian Christoph Sigloch

Pratik Jagtap

James Johnson

Daniel Blankenberg

Subina Mehta

Funding

These individuals or organisations provided funding support for the development of this resource

References

• Kumar D, Yadav AK and Dash D: Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data.
• Vaudel M, et al.: Shedding light on black boxes in protein identification.
  An extensive tutorial for peptide and protein identification, available at http://compomics.com/bioinformatics-for-proteomics. The material is completely based on freely available and open-source tools.
• Cappadona S, et al.: Current challenges in software solutions for mass spectrometry-based quantitative proteomics
  A comprehensive review of current quantitative techniques, their advantages and pitfalls.
• Tholen S, et al.: Limited and Degradative Proteolysis in the Context of Posttranslational Regulatory Networks: Current Technical and Conceptional Advances
  Review on LC-MS/MS based proteomic methods to identify neo-N-termini, e.g. generated by protease cleavage.