L2
Personalized Immunotherapy – High-Throughput HLA Typing Pipeline
Bioinformatics Bachelor Thesis Student (NCT Heidelberg) · Oct 2013 – Jun 2014
Bioinformatics workflow for identifying patient-specific cancer targets and automating HLA genotyping from Next-Generation Sequencing (NGS) data.
Technologies
PythonRHLAMinerSeq2HLANetMHCPan (Neural Networks)Shell Scripting
Contributions
- Developed an in silico genotyping pipeline to extract HLA alleles directly from Whole-Exome Sequencing (WXS) data.
- Implemented predictive modeling using ANNs (NetMHCPan) to screen missense mutations for MHC-I binding affinity.
- Engineered a consensus methodology integrating multiple HLA typing algorithms to resolve sequence ambiguities and improve diagnostic reliability.
- Validated the pipeline by analyzing binding specificities across HLA alleles to mitigate genotyping errors in vaccine design.
Outcome
Demonstrated the feasibility of a fully computational approach to neoantigen discovery, significantly reducing costs and lead times compared to traditional PCR-based clinical assays.