Proteomics
Training material for proteomics workflows in Galaxy
Requirements
Before diving into this topic, we recommend you to have a look at:
Material
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.Introduction
Start here if you are new to proteomic analysis in Galaxy.
Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
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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 |
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Annotating a protein list identified by LC-MS/MS experiments | |||||
Biomarker candidate identification | |||||
Statistical analysis of DIA data |
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 |
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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 |
Multi-omics analyses
These tutorials combine proteomics with other -omics technologies such as transcriptomics.
Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
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Metaproteomics tutorial | |||||
Proteogenomics 1: Database Creation | |||||
Proteogenomics 2: Database Search | |||||
Proteogenomics 3: Novel peptide analysis | |||||
metaQuantome 1: Data creation | |||||
metaQuantome 2: Function | |||||
metaQuantome 3: Taxonomy |
Prediction of peptide properties
These tutorials explain in-silico analyses of different peptide properties.
Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
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Machine Learning Modeling of Anticancer Peptides | |||||
Peptide Library Data Analysis
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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.
Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
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Clinical Metaproteiomics 1: Database-Generation | |||||
Clinical Metaproteomics 2: Discovery | |||||
Clinical Metaproteomics 3: Verification | |||||
Clinical Metaproteomics 4: Quantitation | |||||
Clinical Metaproteomics 5: Data Interpretation |
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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Editorial Board
This material is reviewed by our Editorial Board:
Melanie Föll Subina Mehta Pratik Jagtap Björn GrüningContributors
This material was contributed to by:
Pratik Jagtap Marie Crane Melanie Föll Dechen Bhuming Emma Leith Valentin Loux Clemens Blank Florian Christoph Sigloch Yves Vandenbrouck Ray Sajulga Florence Combes Matthias Fahrner Timothy J. Griffin Subina Mehta Daniel Blankenberg David Christiany Katherine Do Jayadev Joshi Björn Grüning Praveen Kumar James Johnson Klemens FröhlichFunding
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.
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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.