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NeoFox: annotating neoantigen candidates with neoantigen features | JoVE Visualize

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NeoFox: annotating neoantigen candidates with neoantigen features

Franziska Lang¹, Pablo Riesgo-Ferreiro¹, Martin Löwer¹

¹Biomarker Development Center, TRON-Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany.

Bioinformatics (Oxford, England)|May 10, 2021

View abstract on PubMed

Summary

NeoFox is a new Python toolbox that helps researchers identify and predict cancer neoantigens. This tool annotates neoantigen candidates with 16 key features, advancing cancer immunotherapy research.

Area of Science:

Computational biology
Bioinformatics
Cancer immunotherapy

Background:

Accurate neoantigen detection is crucial for effective cancer immunotherapy.
Existing methods may lack comprehensive feature analysis for neoantigen prediction.

Purpose of the Study:

To develop an integrated toolbox for neoantigen feature analysis.
To provide an easy-to-use resource for annotating neoantigen candidates.

Main Methods:

Literature review to identify proposed neoantigen features.
Integration of identified features into a Python package named NeoFox.
Development of annotation capabilities for neoantigen candidates.

Main Results:

Successfully created NeoFox, a Python package.
NeoFox annotates neoantigen candidates using 16 distinct features.
The toolbox is designed for ease of use in research settings.

Conclusions:

NeoFox offers a valuable resource for cancer immunotherapy research.
The package facilitates the annotation and analysis of potential neoantigens.
Open-source availability promotes wider adoption and contribution to the field.

Artificial Intelligence

Natural Language Processing

Neofox: Annotating Neoantigen Candidates With Neoantigen Features