pepitope facilitates TCR-neoantigen screen analysis in the R language

Area of Science:

• Computational biology
• Immunology
• Bioinformatics

Background:

• Functional screening of T cell receptor (TCR)-neoantigen pairs is crucial for personalized immunotherapy development and mechanistic studies.
• Existing computational tools lack integrated modules for experimental screening, including sample demultiplexing, quality control, and downstream analysis.
• There is a need for a comprehensive software package to streamline the analysis of TCR-neoantigen co-culture screening data.

Purpose of the Study:

• To present pepitope, an R package designed to bridge the gap in computational toolkits for TCR-neoantigen co-culture screening.
• To provide an integrated solution for minigene library generation, sequencing-based quality control, and differential abundance analysis.
• To lower the barrier for researchers to perform end-to-end co-culture screen analyses within the R/Bioconductor ecosystem.

Main Methods:

• pepitope utilizes Bioconductor annotation resources for extracting mutant and reference peptides from tumor variant calls.
• The package includes modules for demultiplexing and barcode counting for construct quality control.
• Differential abundance analysis is performed using a negative-binomial-based approach, leveraging DESeq2 for identifying immunogenic epitopes.

Main Results:

• pepitope successfully integrates minigene library generation, construct quality control, and differential abundance analysis into a single R package.
• The software facilitates the identification of immunogenic epitopes from TCR co-culture assays.
• By operating within R, pepitope enhances accessibility for biologists familiar with R and Bioconductor.

Conclusions:

• pepitope provides a comprehensive and accessible solution for analyzing TCR-neoantigen co-culture screening data.
• The package empowers researchers to conduct sophisticated analyses without requiring extensive computational expertise.
• pepitope supports the advancement of personalized immunotherapy research through streamlined data analysis.