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.

Galaxy instances

You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.

You can also use the following Docker image for these tutorials:

docker run -p 8080:80 quay.io/galaxy/proteomics-training

NOTE: Use the -d flag at the end of the command if you want to automatically download all the data-libraries into the container.

It will launch a flavored Galaxy instance available on http://localhost:8080. This instance will contain all the tools and workflows to follow the tutorials in this topic. Login as admin with password password to access everything.

Frequently Asked Questions

Maintainers

This material is maintained by:

Melanie Föll

Subina Mehta

Pratik Jagtap

Björn Grüning

For any question related to this topic and the content, you can contact them or visit our Gitter channel.

Contributors

This material was contributed to by:

David Christiany

Timothy J. Griffin

Florence Combes

James Johnson

Daniel Blankenberg

Jayadev Joshi

Marie Crane

Björn Grüning

Valentin Loux

Yves Vandenbrouck

Praveen Kumar

Florian Christoph Sigloch

Matthias Fahrner

Emma Leith

Subina Mehta

Pratik Jagtap

Ray Sajulga

Klemens Fröhlich

Clemens Blank

Melanie Föll

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.