Prediction of potential neoantigens
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We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.
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.
New to immunopeptidogenomics or neoantigen prediction? Follow this learning path to discover how label-free mass spectrometry and proteogenomic integration can accelerate the identification of clinically relevant neoantigens and help in personalized immunotherapy.
Module 1: Fusion-Database Generation
Learn how to generate a personalized fusion peptide database using RNA-seq data. This step sets the foundation for identifying tumor-specific fusion peptides in downstream analyses.
Time estimation: 2 hours
Learning Objectives
- Downloading databases related to 16SrRNA data
- For better neoantigen identification results.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 1: Fusion-Database-Generation |
Module 2: Non-Reference Database Generation
Construct a non-reference proteogenomic database incorporating somatic mutations, indels, and other genomic alterations from VCF data.
Time estimation: 3 hours
Learning Objectives
- Downloading databases related to 16SrRNA data
- For better neoantigen identification results.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 2: Non-Reference-Database-Generation |
Module 3: Database Merge and FragPipe Discovery
Merge the fusion and non-reference databases, then use FragPipe for mass spectrometry-based discovery of putative neopeptides.
Time estimation: 3 hours
Learning Objectives
- Understand the process of merging neoantigen databases with human protein sequences.
- Learn to use FragPipe for proteomics data analysis.
- Gain hands-on experience with bioinformatics tools such as FASTA file processing, database validation, and peptide identification.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 3: Database merge and FragPipe discovery |
Module 4: PepQuery2 Verification
Perform targeted verification of neoantigen candidates using PepQuery2 for peptide-spectrum match validation.
Time estimation: 3 hours
Learning Objectives
- Understand the workflow for neoantigen validation.
- Apply bioinformatics tools to validate peptides and proteins.
- Interpret the results from various analytical steps.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 4: PepQuery2 Verification |
Module 5: Variant Annotation
Annotate validated neopeptides with their corresponding genomic variants and protein context.
Time estimation: 3 hours
Learning Objectives
- Identify potential neoantigens from sequencing data.
- Annotate somatic mutations and predict peptide sequences.
- Predict MHC binding affinities for neoantigens.
- Interpret data using bioinformatics tools for cancer immunotherapy applications.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 5: Variant Annotation |
Module 6: Predicting HLA Binding
Predict MHC binding affinity of validated neopeptides using tools such as NetMHCpan or similar.
Time estimation: 3 hours
Learning Objectives
- Predict potential neoantigens based on HLA binding affinity.
- Understand the role of HLA genotyping in predicting personalized immune responses.
- Use specific tools for processing sequence data to predict HLA-binding peptides.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 6: Predicting HLA Binding |
Module 7: IEDB Binding of PepQuery Validated Neopeptides
Assess the immunogenic potential of neopeptides by checking their binding predictions against immune epitope databases such as IEDB.
Time estimation: 3 hours
Learning Objectives
- Understand the process of neoantigen identification and the role of peptide binding predictions.
- Learn how to use IEDB to predict the binding affinity of peptides to MHC molecules.
- Gain practical experience using PepQuery to validate novel peptides from proteomics data.
- Distinguish between strong and weak binders based on predicted binding affinity.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Neoantigen 7: IEDB binding PepQuery Validated Neopeptides |
Editorial Board
This material is reviewed by our Editorial Board:
Subina Mehta