AlloPipe and Its Web Server Allogenomics: From Genomic Data to Candidate Minor Histocompatibility Antigens

Area of Science:

• Immunology
• Bioinformatics
• Genomics

Background:

• Transplantation outcomes depend on HLA compatibility.
• Minor histocompatibility antigens (mHAgs) also drive T-cell alloreactivity, particularly in HLA-matched pairs.
• Current methods for mHAgs identification rely on limited genotyping data, hindering unbiased analysis of whole-exome or whole-genome sequencing data.

Purpose of the Study:

• To introduce AlloPipe, a novel bioinformatics tool for unbiased identification of mHAgs candidates from next-generation sequencing data.
• To enable accurate approximation of mHAgs load for clinical applications.

Main Methods:

• AlloPipe employs a two-step process: Allo-Count identifies amino acid mismatches, and Allo-Affinity predicts peptide affinity for HLA class I molecules.
• The software is designed for rapid processing of large datasets on modest computing infrastructure.

Main Results:

• AlloPipe accurately identifies directional amino acid mismatches and reconstructs peptides with their HLA class I binding affinity.
• The tool provides a clinically relevant timeframe for mHAgs approximation.

Conclusions:

• AlloPipe facilitates the integration of mHAgs approximation into clinical decisions, including immunosuppressive therapy optimization and donor selection.
• This open-source software, available locally and via a web interface, enhances accessibility and actionability of mHAgs predictions in clinical practice.