BamQuery

BamQuery quantifies RNA expression of MHC-I-associated peptides derived from coding and non-coding genomic regions, including mutations that produce neoantigens, to prioritize actionable tumor antigens for proteogenomic and cancer immunotherapy research.

Key Features:

• Exhaustive RNA Expression Attribution: Attributes RNA expression from bulk and single-cell RNA-sequencing datasets to MHC-I-associated peptides across coding and non-coding genomic sources.
• Identification of Cryptic and Mutated Antigens: Identifies cryptic and mutated tumor-specific antigens arising from discrete genomic regions, including peptides that are abundantly expressed in normal tissues.
• Immunogenicity Prediction: Predicts the immunogenicity of MHC-I-associated peptides to assess their potential to elicit immune responses.
• De Novo Identification of Actionable Antigens: Performs de novo discovery of actionable tumor-specific antigens without prior knowledge of targets.

Scientific Applications:

• Proteogenomic research: Enables proteogenomic analyses that link RNA expression to MHC-I peptide presentation for tumor antigen discovery.
• Cancer immunotherapy target prioritization: Supports prioritization of tumor antigens for immunotherapy by quantifying expression and predicting immunogenicity of neoantigens and cryptic peptides.
• Tumor heterogeneity and immune landscape analysis: Facilitates study of tumor heterogeneity and the immune landscape using bulk and single-cell RNA-sequencing to map peptide expression across tissues.
• Actionable versus non-actionable antigen discrimination: Helps distinguish actionable tumor antigens from non-actionable peptides based on expression attribution and immunogenicity prediction.

Methodology:

Integrates bulk and single-cell RNA-sequencing data with genomic information to attribute expression levels to MHC-I-associated peptides from coding and non-coding regions, including mutations, and applies predictive algorithms to assess peptide immunogenicity.