Proteomics — Editorial Board Home

This is a new, experimental "Editorial Board Home" for a given topic. It is intended to provide a single place for maintainers and editorial board members to find out key information about their topic and identify action items.

Editorial Board

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

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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 1: Fusion-Database-Generation ✅ ✅ ✅ ✅
Neoantigen 2: Non-Reference-Database-Generation ✅ ✅ ✅ ✅
Neoantigen 3: Database merge and FragPipe discovery ✅ ✅ ✅ ✅
Neoantigen 4: PepQuery2 Verification ✅ ✅ ✅ ✅
Neoantigen 5: Variant Annotation ✅ ✅ ✅ ✅
Neoantigen 6: Predicting HLA Binding ✅ ✅ ✅ ✅
Neoantigen 7: IEDB binding PepQuery Validated Neopeptides ✅ ✅ ✅

Topic Workflows

Material Workflow Updated Version Tests Reports Comments
DIA Analysis using OpenSwathWorkflow DIA_Analysis_OSW May 29, 2025 1 ❌ ❌ ❌
EncyclopeDIA EncyclopeDIA-GTN May 29, 2025 2 ✅ ✅ ❌
Label-free data analysis using MaxQuant Proteomics: MaxQuant workflow May 29, 2025 6 ✅ ❌ ❌
Library Generation for DIA Analysis DIA_lib_OSW May 29, 2025 1 ❌ ❌ ❌
MaxQuant and MSstats for the analysis of TMT data MaxQuant MSstatsTMT Training May 29, 2025 1 ❌ ❌ ❌
MaxQuant and MSstats for the analysis of label-free data Proteomics: MaxQuant and MSstats for the analysis of label-free data May 29, 2025 4 ❌ ✅ ❌
Peptide and Protein ID using OpenMS tools Proteomics: Peptide and Protein ID using OpenMS May 29, 2025 6 ❌ ❌ ❌
Peptide and Protein ID using OpenMS tools Peptide And Protein ID Via OMS Using Two Search Engines May 29, 2025 4 ❌ ❌ ❌
Peptide and Protein ID using SearchGUI and PeptideShaker Peptide And Protein ID Tutorial May 29, 2025 4 ❌ ❌ ❌
Peptide and Protein ID using SearchGUI and PeptideShaker ProteinID SG PS Tutorial WF datasetCollection May 29, 2025 4 ❌ ❌ ❌
Peptide and Protein Quantification via Stable Isotope Labelling (SIL) Proteomics: Peptide and Protein Quantification via stable istobe labeling May 29, 2025 7 ✅ ❌ ❌
Protein FASTA Database Handling Proteomics: database handling May 29, 2025 5 ❌ ❌ ❌
Protein FASTA Database Handling Proteomics: database handling including mycoplasma May 29, 2025 6 ❌ ❌ ❌
Annotating a protein list identified by LC-MS/MS experiments 'Proteome Annotation' May 29, 2025 3 ❌ ❌ ❌
Annotating a protein list identified by LC-MS/MS experiments ProteoRE ProteomeAnnotation Tutorial (release 2.0) May 29, 2025 4 ❌ ❌ ❌
Biomarker candidate identification 'Biomarkers4Paris' Workflow May 29, 2025 3 ❌ ❌ ❌
Secretome Prediction Secreted Proteins Via GO Annotation And WoLF PSORT For shCTSB Paper May 29, 2025 3 ❌ ❌ ❌
Statistical analysis of DIA data DIA_analysis_MSstats May 29, 2025 2 ✅ ❌ ❌
Statistical analysis of DIA data DIA_analysis_MSstats May 29, 2025 2 ✅ ❌ ❌
Detection and quantitation of N-termini (degradomics) via N-TAILS Tails Triple Dimethyl OpenMS2.1 May 29, 2025 4 ❌ ❌ ❌
Mass spectrometry imaging: Loading and exploring MSI data MS Imaging Loading Exploring Data May 29, 2025 4 ❌ ❌ ❌
Metaproteomics tutorial Metaproteomics_GTN May 29, 2025 7 ❌ ❌ ❌
Multiomics data analysis using MultiGSEA MultiGSEA May 29, 2025 1 ✅ ✅ ❌
Proteogenomics 1: Database Creation Proteogenomics 1: Database Creation May 29, 2025 10 ❌ ✅ ❌
Proteogenomics 2: Database Search Proteogenomics 2: Database Search May 29, 2025 8 ❌ ✅ ❌
Proteogenomics 3: Novel peptide analysis GTN Proteogemics3 Novel Peptide Analysis May 29, 2025 7 ❌ ✅ ❌
metaQuantome 1: Data creation metaQuantome_datacreation_workflow May 29, 2025 3 ✅ ❌ ❌
metaQuantome 2: Function metaquantome-function-worklow May 29, 2025 2 ❌ ❌ ❌
metaQuantome 3: Taxonomy metaquantome-taxonomy-workflow May 29, 2025 2 ❌ ❌ ❌
Machine Learning Modeling of Anticancer Peptides PeptideML May 29, 2025 1 ❌ ❌ ❌
Peptide Library Data Analysis Copy of PeptideDataAnalysis (imported from uploaded file) May 29, 2025 1 ❌ ❌ ❌
Clinical Metaproteomics 1: Database-Generation WF1_Database_Generation_Workflow May 29, 2025 1 ❌ ❌ ❌
Clinical Metaproteomics 2: Discovery WF2_Discovery-Workflow May 29, 2025 1 ❌ ❌ ❌
Clinical Metaproteomics 3: Verification WF3_VERIFICATION_WORKFLOW May 29, 2025 1 ❌ ❌ ❌
Clinical Metaproteomics 4: Quantitation WF4_Quantitation_Workflow May 29, 2025 1 ❌ ❌ ❌
Clinical Metaproteomics 5: Data Interpretation WF5_Data_Interpretation_Worklow May 29, 2025 1 ❌ ❌ ❌
Neoantigen 1: Fusion-Database-Generation Gigascience_Fusions_demonstration_STS26T-Gent_Workflow May 29, 2025 1 ❌ ✅ ❌
Neoantigen 2: Non-Reference-Database-Generation Gigascience_Indels_SAV_non-reference_demonstration_STS26T-Gent_Workflow May 29, 2025 1 ❌ ✅ ❌
Neoantigen 3: Database merge and FragPipe discovery GigaScience_Database_merge_FragPipe_STS26T_demonstration May 29, 2025 2 ❌ ✅ ❌
Neoantigen 4: PepQuery2 Verification GigaScience_PepQuery2_demonstration_STS26T_neoantigen_candidates_workflow May 29, 2025 1 ❌ ✅ ❌
Neoantigen 5: Variant Annotation GigaScience_Peptide_Annotation_demonstration_STS26T_neoantigen_candidates_workflow May 29, 2025 2 ❌ ✅ ❌
Neoantigen 6: Predicting HLA Binding GigaScience-RNAseq-Optitype-seq2HLA-to-IEDB-alleles May 29, 2025 1 ❌ ✅ ❌
Neoantigen 7: IEDB binding PepQuery Validated Neopeptides GigaScience-IEDB-PepQuery-Neoantigen May 29, 2025 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 a 3D protein folding structure
Prediction of potential neoantigens

This learning path introduces a comprehensive immunopeptidogenomics 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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