Proteomics — Editorial Board Home

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Editorial Board

orcid logoPratik Jagtap avatar Pratik Jagtaporcid logoBjörn Grüning avatar Björn Grüningorcid logoMelanie Föll avatar Melanie Föllorcid logoSubina Mehta avatar Subina Mehta

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Topic Materials

Material Contributions v2 Pre-requisites Follow up trainings Data on Zenodo Notebook Server Compatibility
Introduction to proteomics, protein identification, quantification and statistical modelling ❌ ✅
DIA Analysis using OpenSwathWorkflow ❌ ✅ ✅
EncyclopeDIA ❌ ✅
Label-free data analysis using MaxQuant ❌ ✅
Label-free versus Labelled - How to Choose Your Quantitation Method ✅ ❌
Library Generation for DIA Analysis ❌ ✅
MaxQuant and MSstats for the analysis of TMT data ❌ ✅
MaxQuant and MSstats for the analysis of label-free data ❌ ✅ ❌
Peptide and Protein ID using OpenMS tools ✅ ✅ ✅ ✅
Peptide and Protein ID using SearchGUI and PeptideShaker ✅ ✅
Peptide and Protein Quantification via Stable Isotope Labelling (SIL) ✅ ✅ ✅
Protein FASTA Database Handling ✅ ❌
Annotating a protein list identified by LC-MS/MS experiments ❌ ✅
Biomarker candidate identification ❌ ❌
Secretome Prediction ✅ ✅
Single Cell Proteomics data analysis with bioconductor-scp ✅ ✅
Statistical analysis of DIA data ❌ ✅ ✅
Detection and quantitation of N-termini (degradomics) via N-TAILS ✅ ❌
Mass spectrometry imaging: Loading and exploring MSI data ✅ ✅
Metaproteomics tutorial ❌ ✅
Multiomics data analysis using MultiGSEA ✅ ✅
Proteogenomics 1: Database Creation ❌ ✅ ✅
Proteogenomics 2: Database Search ✅ ✅ ✅ ✅
Proteogenomics 3: Novel peptide analysis ❌ ✅ ✅
metaQuantome 1: Data creation ❌ ✅ ✅
metaQuantome 2: Function ❌ ✅ ✅ ✅
metaQuantome 3: Taxonomy ❌ ✅ ✅ ✅
Machine Learning Modeling of Anticancer Peptides ❌ ✅
Peptide Library Data Analysis ❌ ✅
Clinical Metaproteomics 1: Database-Generation ✅ ✅ ✅ ✅
Clinical Metaproteomics 2: Discovery ✅ ✅ ✅ ✅
Clinical Metaproteomics 3: Verification ✅ ✅ ✅ ✅
Clinical Metaproteomics 4: Quantitation ✅ ✅ ✅ ✅
Clinical Metaproteomics 5: Data Interpretation ✅ ✅ ✅ ✅
Neoantigen 1a: Fusion-Database-Generation ✅ ✅ ✅ ✅
Neoantigen 1b: Non-Reference-Database-Generation ✅ ✅ ✅ ✅
Neoantigen 2: Database merge and FragPipe discovery ✅ ✅ ✅ ✅
Neoantigen 3: PepQuery2 Verification ✅ ✅ ✅ ✅
Neoantigen 4: Variant Annotation ✅ ✅ ✅ ✅
Neoantigen 5a: Predicting HLA Binding ✅ ✅ ✅ ✅
Neoantigen 5b: IEDB binding Validated Neopeptides ✅ ✅ ✅

Topic Workflows

Material Workflow Updated Version Tests Reports Comments
DIA Analysis using OpenSwathWorkflow DIA_Analysis_OSW Feb 4, 2026 1 ❌ ❌ ❌
EncyclopeDIA EncyclopeDIA-GTN Feb 4, 2026 2 ✅ ✅ ❌
Label-free data analysis using MaxQuant Proteomics: MaxQuant workflow Feb 4, 2026 6 ✅ ❌ ❌
Library Generation for DIA Analysis DIA_lib_OSW Feb 4, 2026 1 ❌ ❌ ❌
MaxQuant and MSstats for the analysis of TMT data MaxQuant MSstatsTMT Training Feb 4, 2026 1 ❌ ❌ ❌
MaxQuant and MSstats for the analysis of label-free data Proteomics: MaxQuant and MSstats for the analysis of label-free data Feb 4, 2026 4 ❌ ✅ ❌
Peptide and Protein ID using OpenMS tools Proteomics: Peptide and Protein ID using OpenMS Feb 4, 2026 6 ❌ ❌ ❌
Peptide and Protein ID using OpenMS tools Peptide And Protein ID Via OMS Using Two Search Engines Feb 4, 2026 4 ❌ ❌ ❌
Peptide and Protein ID using SearchGUI and PeptideShaker Peptide And Protein ID Tutorial Feb 4, 2026 4 ❌ ❌ ❌
Peptide and Protein ID using SearchGUI and PeptideShaker ProteinID SG PS Tutorial WF datasetCollection Feb 4, 2026 4 ❌ ❌ ❌
Peptide and Protein Quantification via Stable Isotope Labelling (SIL) Proteomics: Peptide and Protein Quantification via stable istobe labeling Feb 4, 2026 7 ✅ ❌ ❌
Protein FASTA Database Handling Proteomics: database handling Feb 4, 2026 5 ❌ ❌ ❌
Protein FASTA Database Handling Proteomics: database handling including mycoplasma Feb 4, 2026 6 ❌ ❌ ❌
Annotating a protein list identified by LC-MS/MS experiments 'Proteome Annotation' Feb 4, 2026 3 ❌ ❌ ❌
Annotating a protein list identified by LC-MS/MS experiments ProteoRE ProteomeAnnotation Tutorial (release 2.0) Feb 4, 2026 4 ❌ ❌ ❌
Biomarker candidate identification 'Biomarkers4Paris' Workflow Feb 4, 2026 3 ❌ ❌ ❌
Secretome Prediction Secreted Proteins Via GO Annotation And WoLF PSORT For shCTSB Paper Feb 4, 2026 3 ❌ ❌ ❌
Statistical analysis of DIA data DIA_analysis_MSstats Feb 4, 2026 2 ✅ ❌ ❌
Statistical analysis of DIA data DIA_analysis_MSstats Feb 4, 2026 2 ✅ ❌ ❌
Detection and quantitation of N-termini (degradomics) via N-TAILS Tails Triple Dimethyl OpenMS2.1 Feb 4, 2026 4 ❌ ❌ ❌
Mass spectrometry imaging: Loading and exploring MSI data MS Imaging Loading Exploring Data Feb 4, 2026 4 ❌ ❌ ❌
Metaproteomics tutorial Metaproteomics_GTN Feb 4, 2026 7 ❌ ❌ ❌
Multiomics data analysis using MultiGSEA MultiGSEA Feb 4, 2026 1 ✅ ✅ ❌
Proteogenomics 1: Database Creation Proteogenomics 1: Database Creation Feb 4, 2026 10 ❌ ✅ ❌
Proteogenomics 2: Database Search Proteogenomics 2: Database Search Feb 4, 2026 8 ❌ ✅ ❌
Proteogenomics 3: Novel peptide analysis GTN Proteogemics3 Novel Peptide Analysis Feb 4, 2026 7 ❌ ✅ ❌
metaQuantome 1: Data creation metaQuantome_datacreation_workflow Feb 4, 2026 3 ✅ ❌ ❌
metaQuantome 2: Function metaquantome-function-worklow Feb 4, 2026 2 ❌ ❌ ❌
metaQuantome 3: Taxonomy metaquantome-taxonomy-workflow Feb 4, 2026 2 ❌ ❌ ❌
Machine Learning Modeling of Anticancer Peptides PeptideML Feb 4, 2026 1 ❌ ❌ ❌
Peptide Library Data Analysis Copy of PeptideDataAnalysis (imported from uploaded file) Feb 4, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 1: Database-Generation WF1_Database_Generation_Workflow Feb 4, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 2: Discovery WF2_Discovery-Workflow Feb 4, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 3: Verification WF3_VERIFICATION_WORKFLOW Feb 4, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 4: Quantitation WF4_Quantitation_Workflow Feb 4, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 5: Data Interpretation WF5_Data_Interpretation_Worklow Feb 4, 2026 1 ❌ ❌ ❌

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Learning Pathways using materials from this Topic

image of a 3D protein folding structure
Clinical metaproteomics workflows within Galaxy

This learning path aims to teach you the basics of how to perform metaproteomics analysis of the clinical data within the Galaxy platform. You will learn how to use Galaxy for analysis and will be guided through the most common first steps of any metaproteomics database generation to searching the database, verifying the proteins/peptides, and data analysis.

beginner proteomics
image of neoantigen mutation
Neoantigen discovery using the iPepGen pipeline

This learning path introduces a comprehensive immunopeptidogenomics (iPepGen) workflow for neoantigen discovery using label-free mass spectrometry data. The modules guide you through fusion and variant database generation, peptide identification with FragPipe, peptide validation using PepQuery2, and immunogenicity assessment through HLA binding predictions and IEDB screening.

intermediate immunopeptidomics cancer proteogenomics label-free
image of a 3D protein folding structure
Proteogenomics

This learning path aims to teach you the basics of how to perform proteogenomics analysis of the Mass spectrometry data within the Galaxy platform. You will learn how to use Galaxy for analysis and will be guided through the most common first steps of any proteogenomics database generation to searching the database, followed by novel peptide data analysis.

beginner proteomics
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Events using materials from this Topic

TODO once this is merged: https://github.com/galaxyproject/training-material/pull/4963

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