Workshops and courses
Learning Pathway Prediction of potential neoantigens
Date: No date given
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.
Keywords: cancer, immunopeptidomics, intermediate, label-free, proteogenomics
Learning objectives:
- Annotate somatic mutations and predict peptide sequences.
- Apply bioinformatics tools to validate peptides and proteins.
- Distinguish between strong and weak binders based on predicted binding affinity.
- Downloading databases related to 16SrRNA data
- For better neoantigen identification results.
- Gain hands-on experience with bioinformatics tools such as FASTA file processing, database validation, and peptide identification.
- Gain practical experience using PepQuery to validate novel peptides from proteomics data.
- Identify potential neoantigens from sequencing data.
- Interpret data using bioinformatics tools for cancer immunotherapy applications.
- Interpret the results from various analytical steps.
- Learn how to use IEDB to predict the binding affinity of peptides to MHC molecules.
- Learn to use FragPipe for proteomics data analysis.
- Predict MHC binding affinities for neoantigens.
- Predict potential neoantigens based on HLA binding affinity.
- Understand the process of merging neoantigen databases with human protein sequences.
- Understand the process of neoantigen identification and the role of peptide binding predictions.
- Understand the role of HLA genotyping in predicting personalized immune responses.
- Understand the workflow for neoantigen validation.
- Use specific tools for processing sequence data to predict HLA-binding peptides.
Event types:
- Workshops and courses
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