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

Action Items

Item Status Why you should do this
Summary Done ✅ Provide a sufficiently detailed summary of the topic to let learners know what they're learning about in this topic.
Sufficient Editorial Board Members Done ✅ (4 members) Having multiple people sharing the burden of being responsible for a specific topic can reduce board member burn-out in the long term.
Enable Subtopics Done ✅ Subtopics help organize the content and make it easier to navigate.
Annotate Funders Done ✅ (3 funders) By annotating the funders of your topic's materials, you make it easier to write your grant reports later
Learning Pathway CTA Pending ❌ By providing a Learning Pathway CTA, we can help guide learners to the best resources for learning about this topic.
Review Feedback Received Ongoing 🔁 Review all feedback received for the Proteomics topic and create GitHub issues or PRs where needed.
Review open Pull Requests Ongoing 🔁 View all open PRs in the Proteomics topic and review, comment and merge!
Review open GitHub Issues Ongoing 🔁 View all open issues related to the Proteomics topic and comment, fix, or ask others for help!

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 PepQuery Validated Neopeptides ✅ ✅ ✅

Topic Workflows

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

Your topic's Workflow Testing Results

Coming Soon™

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
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
Statistics for your Learning Pathways
Stats powered by Plausible Analytics

Events using materials from this Topic

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

Statistics For Your Materials

Stats powered by Plausible Analytics