Proteomics
Training material for proteomics workflows in Galaxy
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.Requirements
Before diving into this topic, we recommend you to have a look at:
Material
Introduction
Start here if you are new to proteomic analysis in Galaxy.Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows | Galaxy servers |
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Introduction to proteomics, protein identification, quantification and statistical modelling
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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.Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows | Galaxy servers |
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Annotating a protein list identified by LC-MS/MS experiments | tutorial Toggle Dropdown |
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Biomarker candidate identification | tutorial Toggle Dropdown | instances | ||||
Secretome Prediction | tutorial Toggle Dropdown | instances | ||||
Statistical analysis of DIA data | tutorial Toggle Dropdown | instances |
Special proteomics techniques
These tutorials focus on special techniques such as N-terminomics and mass spectrometry imaging.Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows | Galaxy servers |
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Detection and quantitation of N-termini (degradomics) via N-TAILS
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Mass spectrometry imaging: Loading and exploring MSI data | tutorial Toggle Dropdown | instances |
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.Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows | Galaxy servers |
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Machine Learning Modeling of Anticancer Peptides | tutorial Toggle Dropdown | instances | ||||
Peptide Library Data Analysis
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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
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.Maintainers
This material is maintained by:
Melanie Föll Subina Mehta Pratik Jagtap Björn GrüningFor 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:
Melanie Föll Valentin Loux Florence Combes David Christiany Yves Vandenbrouck Florian Christoph Sigloch Björn Grüning Matthias Fahrner Emma Leith Subina Mehta James Johnson Pratik Jagtap Timothy J. Griffin Jayadev Joshi Daniel Blankenberg Klemens Fröhlich Clemens Blank Marie Crane Praveen Kumar Ray SajulgaReferences
- Kumar D, Yadav AK and Dash D: Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data.
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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.