Proteomics — Editorial Board Home

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Editorial Board

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

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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 ✅ ❌
Single Cell Proteomics data analysis with bioconductor-scp ✅ ✅
Annotating a protein list identified by LC-MS/MS experiments ✅ ✅
Biomarker candidate identification ✅ ❌
Secretome Prediction ✅ ✅
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 Nov 6, 2026 1 ❌ ❌ ❌
EncyclopeDIA EncyclopeDIA-GTN Nov 6, 2026 2 ✅ ✅ ❌
Label-free data analysis using MaxQuant Proteomics: MaxQuant workflow Nov 6, 2026 6 ✅ ❌ ❌
Library Generation for DIA Analysis DIA_lib_OSW Nov 6, 2026 1 ❌ ❌ ❌
MaxQuant and MSstats for the analysis of TMT data MaxQuant MSstatsTMT Training Nov 6, 2026 1 ❌ ❌ ❌
MaxQuant and MSstats for the analysis of label-free data Proteomics: MaxQuant and MSstats for the analysis of label-free data Nov 6, 2026 4 ❌ ✅ ❌
Peptide and Protein ID using OpenMS tools Proteomics: Peptide and Protein ID using OpenMS Nov 6, 2026 6 ❌ ❌ ❌
Peptide and Protein ID using OpenMS tools Peptide And Protein ID Via OMS Using Two Search Engines Nov 6, 2026 4 ❌ ❌ ❌
Peptide and Protein ID using SearchGUI and PeptideShaker Peptide And Protein ID Tutorial Nov 6, 2026 4 ❌ ❌ ❌
Peptide and Protein ID using SearchGUI and PeptideShaker ProteinID SG PS Tutorial WF datasetCollection Nov 6, 2026 4 ❌ ❌ ❌
Peptide and Protein Quantification via Stable Isotope Labelling (SIL) Proteomics: Peptide and Protein Quantification via stable istobe labeling Nov 6, 2026 7 ✅ ❌ ❌
Protein FASTA Database Handling Proteomics: database handling Nov 6, 2026 5 ❌ ❌ ❌
Protein FASTA Database Handling Proteomics: database handling including mycoplasma Nov 6, 2026 6 ❌ ❌ ❌
Annotating a protein list identified by LC-MS/MS experiments 'Proteome Annotation' Nov 6, 2026 3 ❌ ❌ ❌
Annotating a protein list identified by LC-MS/MS experiments ProteoRE ProteomeAnnotation Tutorial (release 2.0) Nov 6, 2026 4 ❌ ❌ ❌
Biomarker candidate identification 'Biomarkers4Paris' Workflow Nov 6, 2026 3 ❌ ❌ ❌
Secretome Prediction Secreted Proteins Via GO Annotation And WoLF PSORT For shCTSB Paper Nov 6, 2026 3 ❌ ❌ ❌
Statistical analysis of DIA data DIA_analysis_MSstats Nov 6, 2026 2 ✅ ❌ ❌
Statistical analysis of DIA data DIA_analysis_MSstats Nov 6, 2026 2 ✅ ❌ ❌
Detection and quantitation of N-termini (degradomics) via N-TAILS Tails Triple Dimethyl OpenMS2.1 Nov 6, 2026 4 ❌ ❌ ❌
Mass spectrometry imaging: Loading and exploring MSI data MS Imaging Loading Exploring Data Nov 6, 2026 4 ❌ ❌ ❌
Metaproteomics tutorial Metaproteomics_GTN Nov 6, 2026 8 ✅ ✅ ❌
Multiomics data analysis using MultiGSEA MultiGSEA Nov 6, 2026 1 ✅ ✅ ❌
Proteogenomics 1: Database Creation Proteogenomics 1: Database Creation Nov 6, 2026 10 ❌ ✅ ❌
Proteogenomics 2: Database Search Proteogenomics 2: Database Search Nov 6, 2026 9 ✅ ✅ ❌
Proteogenomics 3: Novel peptide analysis GTN Proteogemics3 Novel Peptide Analysis Nov 6, 2026 7 ❌ ✅ ❌
metaQuantome 1: Data creation metaQuantome_datacreation_workflow Nov 6, 2026 3 ✅ ❌ ❌
metaQuantome 2: Function metaquantome-function-worklow Nov 6, 2026 2 ❌ ❌ ❌
metaQuantome 3: Taxonomy metaquantome-taxonomy-workflow Nov 6, 2026 2 ❌ ❌ ❌
Machine Learning Modeling of Anticancer Peptides PeptideML Nov 6, 2026 1 ❌ ❌ ❌
Peptide Library Data Analysis Copy of PeptideDataAnalysis (imported from uploaded file) Nov 6, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 1: Database-Generation WF1_Database_Generation_Workflow Nov 6, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 2: Discovery WF2_Discovery-Workflow Nov 6, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 3: Verification WF3_VERIFICATION_WORKFLOW Nov 6, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 4: Quantitation WF4_Quantitation_Workflow Nov 6, 2026 1 ❌ ❌ ❌
Clinical Metaproteomics 5: Data Interpretation WF5_Data_Interpretation_Worklow Nov 6, 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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