NeoFox: annotating neoantigen candidates with neoantigen features

Area of Science:

• Computational biology
• Bioinformatics
• Cancer immunotherapy

Background:

• Accurate neoantigen detection is crucial for effective cancer immunotherapy.
• Existing methods may not capture the full spectrum of relevant neoantigen features.

Purpose of the Study:

• To develop a comprehensive computational tool for neoantigen feature annotation.
• To integrate known neoantigen features into an accessible software package.

Main Methods:

• Literature review to identify proposed neoantigen features.
• Development of the NEOantigen Feature toolbOX (NeoFox) as a Python package.
• Implementation of annotation for 16 distinct neoantigen features.

Main Results:

• The NeoFox toolbox successfully integrates 16 neoantigen features.
• NeoFox provides an easy-to-use interface for annotating neoantigen candidates.
• The package facilitates the analysis of potential neoantigens for immunotherapy.

Conclusions:

• NeoFox offers a valuable resource for researchers in cancer immunotherapy.
• The tool enhances the ability to detect and predict true neoantigens.
• Open-source availability promotes wider adoption and contribution in the field.