ImmunoPepper

ImmunoPepper extracts personalized peptides from complex splicing graphs to enable neoantigen discovery for cancer immunotherapy.

Key Features:

• Splicing graph-based peptide extraction: Extracts biologically plausible peptides from a splicing graph derived from RNA-seq datasets.
• Personalization with genetic variation: Personalizes peptide sets with germline and somatic variation.
• Novel splice variant inclusion: Takes novel RNA splice variants into account during peptide generation.
• Normal tissue background subtraction: Supports filtering by subtracting normal tissue background.
• MHC-binding affinity prediction: Predicts MHC-binding affinity for generated peptides.
• MassSpec validation: Supports MassSpec-based validation of identified peptides using target-decoy competition Subset-Neighbor-Search (SNS).

Scientific Applications:

• Cancer neoantigen discovery: Identifies splicing-derived tumor-associated peptides recognized by the immune system for personalized cancer therapy.
• TCGA cancer cohort analysis: Analyzed 32 ovarian cancer (TCGA-OV) and 31 breast invasive carcinoma (TCGA-BRCA) samples to obtain cancer-specific predicted MHC-I binding 9-mers.
• Splicing-derived neoepitope framework evaluation: Informs best practices for generation of splicing-derived neoepitopes, recommending joint data structures for homogeneous processing of cancer and normal cohorts and focusing on reproducibility across generation pipelines.

Methodology:

ImmunoPepper is implemented in Python 3. It derives splicing graphs from RNA-seq datasets, extracts peptides from these graphs, personalizes them with germline and somatic variation, and applies filtering including normal tissue background subtraction, MHC-binding affinity prediction, and MassSpec validation with target-decoy competition Subset-Neighbor-Search (SNS).