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ScanNeo2: a comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and
Richard A Schäfer1, Qingxiang Guo1, Rendong Yang1,2
1Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.
Motivation:
Neoantigens, tumor-specific protein fragments, are invaluable in cancer immunotherapy due to their ability to serve as targets for the immune system. Computational prediction of these neoantigens from sequencing data often requires multiple algorithms and sophisticated workflows, which are currently restricted to specific types of variants, such as single-nucleotide variants or insertions/deletions. Nevertheless, other sources of neoantigens are often overlooked.
Results:
We introduce ScanNeo2 an improved and fully automated bioinformatics pipeline designed for high-throughput neoantigen prediction from raw sequencing data. Unlike its predecessor, ScanNeo2 integrates multiple sources of somatic variants, including canonical- and exitron-splicing, gene fusion events, and various somatic variants. Our benchmark results demonstrate that ScanNeo2 accurately identifies neoantigens, providing a comprehensive and more efficient solution for neoantigen prediction.
Availability And Implementation:
ScanNeo2 is freely available at https://github.com/ylab-hi/ScanNeo2/ and is accompanied by instruction and application data.
Insights
ScanNeo2 is a new bioinformatics pipeline that accurately predicts cancer neoantigens from sequencing data. It integrates multiple variant sources for comprehensive and efficient neoantigen discovery in cancer immunotherapy.
Area of Science:
- Computational biology
- Genomics
- Cancer immunotherapy
Background:
- Neoantigens are crucial targets in cancer immunotherapy.
- Current neoantigen prediction methods are limited to specific variant types and overlook other sources.
- Sophisticated workflows are often required, limiting accessibility.
Purpose of the Study:
- To introduce ScanNeo2, an automated bioinformatics pipeline for high-throughput neoantigen prediction.
- To improve the comprehensive identification of neoantigens by integrating diverse variant sources.
- To provide a more efficient and accurate solution for neoantigen discovery.
Main Methods:
- Development of an automated bioinformatics pipeline, ScanNeo2.
- Integration of multiple somatic variant sources, including canonical- and exitron-splicing and gene fusion events.
- Benchmarking against existing methods for accuracy and efficiency.
Main Results:
- ScanNeo2 accurately predicts neoantigens from raw sequencing data.
- The pipeline integrates diverse variant sources, offering a more comprehensive analysis.
- ScanNeo2 provides a more efficient solution for high-throughput neoantigen prediction.
Conclusions:
- ScanNeo2 offers a comprehensive and efficient approach to neoantigen prediction.
- The pipeline enhances the identification of neoantigens for cancer immunotherapy.
- ScanNeo2 is a valuable tool for researchers in cancer genomics and immunotherapy.
